From e5286b955813d2c60de657d30f31f2e85f0abed3 Mon Sep 17 00:00:00 2001 From: Ramon Winterhalder <35335120+ramonpeter@users.noreply.github.com> Date: Mon, 5 Aug 2024 22:19:51 +0300 Subject: [PATCH] Update aug (#219) * Update pre commit hook * update papers * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- .pre-commit-config.yaml | 2 +- HEPML.bib | 807 ++++++++++++++++++++++++++++++++++++++++ HEPML.tex | 60 +-- README.md | 67 +++- docs/index.md | 69 +++- docs/recent.md | 150 ++++---- 6 files changed, 1055 insertions(+), 100 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index e17b82b..701020d 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -21,7 +21,7 @@ repos: - id: trailing-whitespace - repo: https://github.com/asottile/pyupgrade - rev: v3.15.2 + rev: v3.16.0 hooks: - id: pyupgrade args: ["--py37-plus"] diff --git a/HEPML.bib b/HEPML.bib index a88e1bf..1481768 100644 --- a/HEPML.bib +++ b/HEPML.bib @@ -1,4 +1,811 @@ # HEPML Papers + +% August 05, 2024 +@article{Bieringer:2024nbc, + author = "Bieringer, Sebastian and Diefenbacher, Sascha and Kasieczka, Gregor and Trabs, Mathias", + title = "{Calibrating Bayesian Generative Machine Learning for Bayesiamplification}", + eprint = "2408.00838", + archivePrefix = "arXiv", + primaryClass = "cs.LG", + month = "8", + year = "2024" +} + +% August 05, 2024 +@article{Bose:2024pwc, + author = "Bose, Camellia and Chakraborty, Amit and Chowdhury, Shreecheta and Dutta, Saunak", + title = "{Interplay of Traditional Methods and Machine Learning Algorithms for Tagging Boosted Objects}", + eprint = "2408.01138", + archivePrefix = "arXiv", + primaryClass = "hep-ph", + doi = "10.1140/epjs/s11734-024-01256-6", + month = "8", + year = "2024" +} + +% August 02, 2024 +@article{Liuti:2024zkc, + author = "Liuti, Simonetta and others", + title = "{AI for Nuclear Physics: the EXCLAIM project}", + eprint = "2408.00163", + archivePrefix = "arXiv", + primaryClass = "hep-ph", + month = "7", + year = "2024" +} + +% August 02, 2024 +@article{Halverson:2024hax, + author = "Halverson, Jim", + title = "{TASI Lectures on Physics for Machine Learning}", + eprint = "2408.00082", + archivePrefix = "arXiv", + primaryClass = "hep-th", + month = "7", + year = "2024" +} + +% August 01, 2024 +@article{Englert:2024ufr, + author = "Englert, Philipp", + title = "{Improved Precision in $Vh(\rightarrow b\bar b)$ via Boosted Decision Trees}", + eprint = "2407.21239", + archivePrefix = "arXiv", + primaryClass = "hep-ph", + month = "7", + year = "2024" +} + +% July 31, 2024 +@article{Hallin:2024gmt, + author = "Hallin, Anna and Kasieczka, Gregor and Kraml, Sabine and Lessa, Andr\'e and Moureaux, Louis and von Schwartz, Tore and Shih, David", + title = "{Universal New Physics Latent Space}", + eprint = "2407.20315", + archivePrefix = "arXiv", + primaryClass = "hep-ph", + month = "7", + year = "2024" +} + +% July 31, 2024 +@article{Cerdeno:2024uqt, + author = "Cerdeno, David and de los Rios, Martin and Perez, Andres D.", + title = "{Bayesian technique to combine independently-trained Machine-Learning models applied to direct dark matter detection}", + eprint = "2407.21008", + archivePrefix = "arXiv", + primaryClass = "hep-ph", + reportNumber = "IFT-UAM/CSIC-24-116", + month = "7", + year = "2024" +} + +% July 31, 2024 +@inproceedings{Harilal:2024tqq, + author = "{CMS Collaboration}", + title = "{Anomaly Detection Based on Machine Learning for the CMS Electromagnetic Calorimeter Online Data Quality Monitoring}", + booktitle = "{20th International Conference on Calorimetry in Particle Physics}", + eprint = "2407.20278", + archivePrefix = "arXiv", + primaryClass = "physics.ins-det", + reportNumber = "CMS CR-2024/135", + month = "7", + year = "2024" +} + +% July 30, 2024 +@article{Leigh:2024chm, + author = "Leigh, Matthew and Sengupta, Debajyoti and Nachman, Benjamin and Golling, Tobias", + title = "{Accelerating template generation in resonant anomaly detection searches with optimal transport}", + eprint = "2407.19818", + archivePrefix = "arXiv", + primaryClass = "hep-ph", + month = "7", + year = "2024" +} + +% July 30, 2024 +@article{Dong:2024gts, + author = "Dong, Nuoyu and Hou, Hongsheng and Qian, Zhuoni and Wang, Bowen and Xu, Qingjun", + title = "{Probing Charm Yukawa through $ch$ Associated Production at the Hadron Collider}", + eprint = "2407.19797", + archivePrefix = "arXiv", + primaryClass = "hep-ph", + month = "7", + year = "2024" +} + +% July 30, 2024 +@article{ATLAS:2024rua, + author = "{ATLAS Collaboration}", + title = "{Accuracy versus precision in boosted top tagging with the ATLAS detector}", + eprint = "2407.20127", + archivePrefix = "arXiv", + primaryClass = "hep-ex", + reportNumber = "CERN-EP-2024-159", + month = "7", + year = "2024" +} + +% July 30, 2024 +@article{Zhu:2024ubz, + author = {Zhu, Tong and Jin, Miaochen and Arg\"uelles, Carlos A.}, + title = "{Comparison of Geometrical Layouts for Next-Generation Large-volume Cherenkov Neutrino Telescopes}", + eprint = "2407.19010", + archivePrefix = "arXiv", + primaryClass = "physics.ins-det", + month = "7", + year = "2024" +} + +% July 25, 2024 +@article{Mosala:2024mcy, + author = "Mosala, Karabo and Mulaudzi, Anza-Tshilidzi and Mathaha, Thuso and Sharma, Pramod and Kumar, Mukesh and Mellado, Bruce and Ruan, Manqi", + title = "{The Observation of a 95 GeV Scalar at Future Electron-Positron Colliders}", + eprint = "2407.16806", + archivePrefix = "arXiv", + primaryClass = "hep-ph", + month = "7", + year = "2024" +} + +% July 25, 2024 +@article{Wojnar:2024cbn, + author = "Wojnar, Maksymilian", + title = "{Applying generative neural networks for fast simulations of the ALICE (CERN) experiment}", + eprint = "2407.16704", + archivePrefix = "arXiv", + primaryClass = "physics.ins-det", + month = "7", + year = "2024" +} + +% July 22, 2024 +@article{Calafiura:2024qhv, + author = "Calafiura, Paolo and Chan, Jay and Delabrouille, Loic and Wang, Brandon", + title = "{EggNet: An Evolving Graph-based Graph Attention Network for Particle Track Reconstruction}", + eprint = "2407.13925", + archivePrefix = "arXiv", + primaryClass = "physics.data-an", + month = "7", + year = "2024" +} + +% July 18, 2024 +@article{Alves:2024hrj, + author = "Alves, Alexandre and Almeida, Eduardo da Silva and Dias, Alex G. and Gon\c{c}alves, Diego S. V.", + title = "{Exploring Top-Quark Signatures of Heavy Flavor-Violating Scalars at the LHC with Parametrized Neural Networks}", + eprint = "2407.12118", + archivePrefix = "arXiv", + primaryClass = "hep-ph", + month = "7", + year = "2024" +} + +% July 18, 2024 +@article{Gao:2024nzg, + author = "Gao, Lin", + title = "{Study of the mass of pseudoscalar glueball with a deep neural network}", + eprint = "2407.12010", + archivePrefix = "arXiv", + primaryClass = "hep-lat", + month = "6", + year = "2024" +} + +% July 18, 2024 +@article{Correia:2024ogc, + author = "Correia, Anthony and Giasemis, Fotis I. and Garroum, Nabil and Gligorov, Vladimir Vava and Granado, Bertrand", + title = "{Graph Neural Network-Based Track Finding in the LHCb Vertex Detector}", + eprint = "2407.12119", + archivePrefix = "arXiv", + primaryClass = "physics.ins-det", + month = "7", + year = "2024" +} + +% July 17, 2024 +@article{Desai:2024kpd, + author = "Desai, Krish and Nachman, Benjamin and Thaler, Jesse", + title = "{Moment Unfolding}", + eprint = "2407.11284", + archivePrefix = "arXiv", + primaryClass = "hep-ph", + reportNumber = "MIT-CTP 5727", + month = "7", + year = "2024" +} + +% July 12, 2024 +@article{Cazzaniga:2024mmv, + author = "Cazzaniga, Cesare and Russo, Alessandro and Sitti, Emre and de Cosa, Annapaola", + title = "{Phenomenology of photons-enriched semi-visible jets}", + eprint = "2407.08276", + archivePrefix = "arXiv", + primaryClass = "hep-ph", + month = "7", + year = "2024" +} + +% July 12, 2024 +@article{Wu:2024thh, + author = "Wu, Yifan and Wang, Kun and Zhu, Jingya", + title = "{Jet Tagging with More-Interaction Particle Transformer}", + eprint = "2407.08682", + archivePrefix = "arXiv", + primaryClass = "hep-ph", + month = "7", + year = "2024" +} + +% July 12, 2024 +@article{CMS:2024ddc, + author = "{CMS Collaboration}", + title = "{Measurement of boosted Higgs bosons produced via vector boson fusion or gluon fusion in the H $\to$$\mathrm{b\bar{b}}$ decay mode using LHC proton-proton collision data at $\sqrt{s}$ = 13 TeV}", + eprint = "2407.08012", + archivePrefix = "arXiv", + primaryClass = "hep-ex", + reportNumber = "CMS-HIG-21-020, CERN-EP-2024-162", + month = "7", + year = "2024" +} + +% July 12, 2024 +@article{Boukela:2024rst, + author = "Boukela, Lynda and Eichler, Annika and Branlard, Julien and Jomhari, Nur Zulaiha", + title = "{A Two-Stage Machine Learning-Aided Approach for Quench Identification at the European XFEL}", + eprint = "2407.08408", + archivePrefix = "arXiv", + primaryClass = "physics.ins-det", + month = "7", + year = "2024" +} + +% July 11, 2024 +@article{Wojcik:2024lfy, + author = "Wojcik, George N. and Eu, Shu Tian and Everett, Lisa L.", + title = "{Graph Reinforcement Learning for Exploring BSM Model Spaces}", + eprint = "2407.07203", + archivePrefix = "arXiv", + primaryClass = "hep-ph", + month = "7", + year = "2024" +} + +% July 11, 2024 +@article{Caron:2024cyo, + author = "Caron, Sascha and Dobreva, Nadezhda and S\'anchez, Antonio Ferrer and Mart\'\i{}n-Guerrero, Jos\'e D. and Odyurt, Uraz and Ruiz de Austri Bazan, Roberto Ruiz and Wolffs, Zef and Zhao, Yue", + title = "{TrackFormers: In Search of Transformer-Based Particle Tracking for the High-Luminosity LHC Era}", + eprint = "2407.07179", + archivePrefix = "arXiv", + primaryClass = "hep-ex", + month = "7", + year = "2024" +} + +% July 11, 2024 +@article{Fanelli:2024wrj, + author = "Fanelli, Cristiano and Giroux, James and Stevens, Justin", + title = "{Deep(er) Reconstruction of Imaging Cherenkov Detectors with Swin Transformers and Normalizing Flow Models}", + eprint = "2407.07376", + archivePrefix = "arXiv", + primaryClass = "physics.ins-det", + month = "7", + year = "2024" +} + +% July 10, 2024 +@article{Tani:2024qzm, + author = "Tani, Laurits and Seeba, Nalong-Norman and Vanaveski, Hardi and Pata, Joosep and Lange, Torben", + title = "{A unified machine learning approach for reconstructing hadronically decaying tau leptons}", + eprint = "2407.06788", + archivePrefix = "arXiv", + primaryClass = "hep-ex", + month = "7", + year = "2024" +} + +% July 10, 2024 +@article{Bachtis:2024dss, + author = "Bachtis, Dimitrios", + title = "{Disordered Lattice Glass $\phi^{4}$ Quantum Field Theory}", + eprint = "2407.06569", + archivePrefix = "arXiv", + primaryClass = "hep-lat", + month = "7", + year = "2024" +} + +% July 10, 2024 +@article{Hendi:2024yin, + author = "Hendi, Yacoub and Larfors, Magdalena and Walden, Moritz", + title = "{Learning Group Invariant Calabi-Yau Metrics by Fundamental Domain Projections}", + eprint = "2407.06914", + archivePrefix = "arXiv", + primaryClass = "hep-th", + reportNumber = "UUITP-21/24", + month = "7", + year = "2024" +} + +% July 09, 2024 +@article{Larkoski:2024uoc, + author = "Larkoski, Andrew J.", + title = "{QCD Masterclass Lectures on Jet Physics and Machine Learning}", + eprint = "2407.04897", + archivePrefix = "arXiv", + primaryClass = "hep-ph", + month = "7", + year = "2024" +} + +% July 09, 2024 +@article{Chen:2024vdq, + author = "Chen, Jian and Wang, Jun and Wang, Wei and Wei, Yuehuan", + title = "{Extraction of fissile isotope antineutrino spectra using feedforward neural network}", + eprint = "2407.05834", + archivePrefix = "arXiv", + primaryClass = "hep-ph", + month = "7", + year = "2024" +} + +% July 09, 2024 +@article{Farrell:2024aah, + author = "Farrell, Sophia and Bergevin, Marc and Bernstein, Adam", + title = "{Physics-informed machine learning approaches to reactor antineutrino detection}", + eprint = "2407.06139", + archivePrefix = "arXiv", + primaryClass = "physics.ins-det", + reportNumber = "LLNL-JRNL-865846", + month = "7", + year = "2024" +} + +% July 08, 2024 +@article{Kriesten:2024are, + author = "Kriesten, Brandon and Gomprecht, Jonathan and Hobbs, T. J.", + title = "{Explainable AI classification for parton density theory}", + eprint = "2407.03411", + archivePrefix = "arXiv", + primaryClass = "hep-ph", + reportNumber = "ANL-189807", + month = "7", + year = "2024" +} + +% July 08, 2024 +@article{Sandoval:2024ldp, + author = "Sandoval, Jairo Orozco and Manian, Vidya and Malik, Sudhir", + title = "{A multicategory jet image classification framework using deep neural network}", + eprint = "2407.03524", + archivePrefix = "arXiv", + primaryClass = "hep-ph", + month = "7", + year = "2024" +} + +% July 08, 2024 +@article{Ramirez-Morales:2024krk, + author = "Ramirez-Morales, A. and Guti\'errez-Rodr\'\i{}guez, A. and Cisneros-P\'erez, T. and Garcia-Tecocoatzi, H. and D\'avila-Rivera, A.", + title = "{Exotic and physics-informed support vector machines for high energy physics}", + eprint = "2407.03538", + archivePrefix = "arXiv", + primaryClass = "hep-ex", + month = "7", + year = "2024" +} + +% July 03, 2024 +@article{Dong:2024xsg, + author = "Dong, Zhongtian and Gon\c{c}alves, Dorival and Kong, Kyoungchul and Larkoski, Andrew J. and Navarro, Alberto", + title = "{Hadronic Top Quark Polarimetry with ParticleNet}", + eprint = "2407.01663", + archivePrefix = "arXiv", + primaryClass = "hep-ph", + month = "7", + year = "2024" +} + +% July 02, 2024 +@article{Barman:2024wfx, + author = "Barman, Rahool Kumar and Biswas, Sumit", + title = "{Top-philic Machine Learning}", + eprint = "2407.00183", + archivePrefix = "arXiv", + primaryClass = "hep-ph", + doi = "10.1140/epjs/s11734-024-01237-9", + month = "6", + year = "2024" +} + +% July 02, 2024 +@article{Hirvonen:2024zne, + author = "Hirvonen, Henry and Kuha, Mikko and Auvinen, Jussi and Eskola, Kari J. and Kanakubo, Yuuka and Niemi, Harri", + title = "{Effects of saturation and fluctuating hotspots for flow observables in ultrarelativistic heavy-ion collisions}", + eprint = "2407.01338", + archivePrefix = "arXiv", + primaryClass = "hep-ph", + month = "7", + year = "2024" +} + +% July 02, 2024 +@article{Grosso:2024nho, + author = "Grosso, Gaia", + title = "{Anomaly-aware summary statistic from data batches}", + eprint = "2407.01249", + archivePrefix = "arXiv", + primaryClass = "hep-ex", + month = "7", + year = "2024" +} + +% July 02, 2024 +@article{Soum-Sidikov:2024khx, + author = {Soum-Sidikov, Gabrielle and Crocombette, Jean-Paul and Marinica, Mihai-Cosmin and Doutre, Corentin and Lhuillier, David and Thulliez, Lo\"\i{}c}, + title = "{Calculation of crystal defects induced in CaWO$_{4}$ by 100 eV displacement cascades using a linear Machine Learning interatomic potential}", + eprint = "2407.00133", + archivePrefix = "arXiv", + primaryClass = "physics.ins-det", + month = "6", + year = "2024" +} + +% July 02, 2024 +@article{CALICE:2024jke, + author = "{ALICE Collaboration}", + title = "{Shower Separation in Five Dimensions for Highly Granular Calorimeters using Machine Learning}", + eprint = "2407.00178", + archivePrefix = "arXiv", + primaryClass = "physics.ins-det", + month = "6", + year = "2024" +} + +% July 01, 2024 +@article{Los:2024xzl, + author = "Los, E. E. and Arran, C. and Gerstmayr, E. and Streeter, M. J. V. and Najmudin, Z. and Ridgers, C. P. and Sarri, G. and Mangles, S. P. D.", + title = "{A Bayesian Framework to Investigate Radiation Reaction in Strong Fields}", + eprint = "2406.19420", + archivePrefix = "arXiv", + primaryClass = "physics.data-an", + month = "6", + year = "2024" +} + +% June 28, 2024 +@article{Heisig:2024jkk, + author = {Heisig, Jan and Korsmeier, Michael and Kr\"amer, Michael and Nippel, Kathrin and Rathmann, Lena}, + title = "{$\overline{\text{D}}$arkRayNet: Emulation of cosmic-ray antideuteron fluxes from dark matter}", + eprint = "2406.18642", + archivePrefix = "arXiv", + primaryClass = "hep-ph", + reportNumber = "TTK-24-25", + month = "6", + year = "2024" +} + +% June 28, 2024 +@article{Schofbeck:2024zjo, + author = {Sch\"ofbeck, Robert}, + title = "{Refinable modeling for unbinned SMEFT analyses}", + eprint = "2406.19076", + archivePrefix = "arXiv", + primaryClass = "hep-ph", + month = "6", + year = "2024" +} + +% June 25, 2024 +@article{Soybelman:2024mbv, + author = "Soybelman, Nathalie and Schiavi, Carlo and Di Bello, Francesco A. and Gross, Eilam", + title = "{Accelerating Graph-based Tracking Tasks with Symbolic Regression}", + eprint = "2406.16752", + archivePrefix = "arXiv", + primaryClass = "hep-ex", + month = "6", + year = "2024" +} + +% June 24, 2024 +@article{Jiang:2024vsr, + author = "Jiang, Fu-Jiun", + title = "{Berezinskii--Kosterlitz--Thouless transition of the two-dimensional $XY$ model on the honeycomb lattice}", + eprint = "2406.14812", + archivePrefix = "arXiv", + primaryClass = "hep-lat", + month = "6", + year = "2024" +} + +% June 24, 2024 +@article{Parpillon:2024maz, + author = "Parpillon, Benjamin and others", + title = "{Smart Pixels: In-pixel AI for on-sensor data filtering}", + eprint = "2406.14860", + archivePrefix = "arXiv", + primaryClass = "physics.ins-det", + reportNumber = "FERMILAB-CONF-24-0233-ETD", + month = "6", + year = "2024" +} + +% June 21, 2024 +@article{Quetant:2024ftg, + author = "Qu\'etant, Guillaume and Raine, John Andrew and Leigh, Matthew and Sengupta, Debajyoti and Golling, Tobias", + title = "{PIPPIN: Generating variable length full events from partons}", + eprint = "2406.13074", + archivePrefix = "arXiv", + primaryClass = "hep-ph", + month = "6", + year = "2024" +} + +% June 21, 2024 +@article{Ahmad:2024dql, + author = "Ahmad, Farzana Yasmin and Venkataswamy, Vanamala and Fox, Geoffrey", + title = "{A Comprehensive Evaluation of Generative Models in Calorimeter Shower Simulation}", + eprint = "2406.12898", + archivePrefix = "arXiv", + primaryClass = "physics.ins-det", + month = "6", + year = "2024" +} + +% June 21, 2024 +@article{Gavrikov:2024rso, + author = "Gavrikov, A. and others", + title = "{Interpretable machine learning approach for electron antineutrino selection in a large liquid scintillator detector}", + eprint = "2406.12901", + archivePrefix = "arXiv", + primaryClass = "physics.ins-det", + month = "6", + year = "2024" +} + +% June 19, 2024 +@article{Cai:2024eqa, + author = "Cai, Rong-Gen and He, Song and Li, Li and Zeng, Hong-An", + title = "{QCD Phase Diagram at finite Magnetic Field and Chemical Potential: A Holographic Approach Using Machine Learning}", + eprint = "2406.12772", + archivePrefix = "arXiv", + primaryClass = "hep-th", + month = "6", + year = "2024" +} + +% June 19, 2024 +@article{Aamir:2024lpz, + author = "Aamir, M. and others", + title = "{Using graph neural networks to reconstruct charged pion showers in the CMS High Granularity Calorimeter}", + eprint = "2406.11937", + archivePrefix = "arXiv", + primaryClass = "physics.ins-det", + month = "6", + year = "2024" +} + +% June 18, 2024 +@article{Pratiush:2024ltm, + author = "Pratiush, Utkarsh and Houston, Austin and Kalinin, Sergei V. and Duscher, Gerd", + title = "{Implementing dynamic high-performance computing supported workflows on Scanning Transmission Electron Microscope}", + eprint = "2406.11018", + archivePrefix = "arXiv", + primaryClass = "physics.ins-det", + month = "6", + year = "2024" +} + +% June 17, 2024 +@article{Kara:2024xkk, + author = "Kara, S. O. and Akkoyun, S.", + title = "{A Search for Leptonic Photon, $Z_{l}$, at All Three CLIC Energy Stages by Using Artificial Neural Networks (ANN)}", + eprint = "2406.10097", + archivePrefix = "arXiv", + primaryClass = "hep-ph", + doi = "10.5506/APhysPolB.55.6-A4", + journal = "Acta Phys. Polon. B", + volume = "55", + number = "6", + pages = "6--A4", + year = "2024" +} + +% June 17, 2024 +@article{Goodsell:2024aig, + author = "Goodsell, Mark D.", + title = "{HackAnalysis 2: A powerful and hackable recasting tool}", + eprint = "2406.10042", + archivePrefix = "arXiv", + primaryClass = "hep-ph", + month = "6", + year = "2024" +} + +% June 17, 2024 +@article{MicroBooNE:2024zhz, + author = "Abratenko, P. and others", + collaboration = "MicroBooNE", + title = "{Improving neutrino energy estimation of charged-current interaction events with recurrent neural networks in MicroBooNE}", + eprint = "2406.10123", + archivePrefix = "arXiv", + primaryClass = "hep-ex", + reportNumber = "FERMILAB-PUB-24-0287", + month = "6", + year = "2024" +} + +% June 14, 2024 +@article{Liuti:2024umy, + author = "Liuti, Simonetta", + title = "{Extraction of Information from Polarized Deep Exclusive Scattering with Machine Learning}", + eprint = "2406.09258", + archivePrefix = "arXiv", + primaryClass = "hep-ph", + month = "6", + year = "2024" +} + +% June 14, 2024 +@article{Blekman:2024wyf, + author = "Blekman, Freya and Canelli, Florencia and De Moor, Alexandre and Gautam, Kunal and Ilg, Armin and Macchiolo, Anna and Ploerer, Eduardo", + title = "{Jet Flavour Tagging at FCC-ee with a Transformer-based Neural Network: DeepJetTransformer}", + eprint = "2406.08590", + archivePrefix = "arXiv", + primaryClass = "hep-ex", + reportNumber = "PUBDB-2024-01826", + month = "6", + year = "2024" +} + +% June 14, 2024 +@article{Tiras:2024yzr, + author = "Tiras, Emrah and Tas, Merve and Kizilkaya, Dilara and Yagiz, Muhammet Anil and Kandemir, Mustafa", + title = "{Comprehensive Machine Learning Model Comparison for Cherenkov and Scintillation Light Separation due to Particle Interactions}", + eprint = "2406.09191", + archivePrefix = "arXiv", + primaryClass = "hep-ex", + month = "6", + year = "2024" +} + +% June 12, 2024 +@article{Ahn:2024lkh, + author = "Ahn, Byoungjoon and Jeong, Hyun-Sik and Kim, Keun-Young and Yun, Kwan", + title = "{Holographic reconstruction of black hole spacetime: machine learning and entanglement entropy}", + eprint = "2406.07395", + archivePrefix = "arXiv", + primaryClass = "hep-th", + reportNumber = "IFT-UAM/CSIC-24-88", + month = "6", + year = "2024" +} + +% June 11, 2024 +@article{Mansouri:2024uwc, + author = "Mansouri, Mahdi and Bitaghsir Fadafan, Kazem and Chen, Xun", + title = "{Holographic complex potential of a quarkonium from deep learning}", + eprint = "2406.06285", + archivePrefix = "arXiv", + primaryClass = "hep-ph", + month = "6", + year = "2024" +} + +% June 07, 2024 +@article{Hatefi:2024asc, + author = "Hatefi, Armin and Hatefi, Ehsan and Lopez-Sastre, Roberto J.", + title = "{Neural Networks Assisted Metropolis-Hastings for Bayesian Estimation of Critical Exponent on Elliptic Black Hole Solution in 4D Using Quantum Perturbation Theory}", + eprint = "2406.04310", + archivePrefix = "arXiv", + primaryClass = "hep-th", + month = "6", + year = "2024" +} + +% June 06, 2024 +@article{Bickendorf:2024ovi, + author = "Bickendorf, Gerrit and Drees, Manuel", + title = "{Learning to see R-parity violating scalar top decays}", + eprint = "2406.03096", + archivePrefix = "arXiv", + primaryClass = "hep-ph", + month = "6", + year = "2024" +} + +% June 06, 2024 +@article{Kita:2024nnw, + author = "Kita, Miko\l{}aj and Dubi\'nski, Jan and Rokita, Przemys\l{}aw and Deja, Kamil", + title = "{Generative Diffusion Models for Fast Simulations of Particle Collisions at CERN}", + eprint = "2406.03233", + archivePrefix = "arXiv", + primaryClass = "physics.data-an", + month = "6", + year = "2024" +} + +% June 05, 2024 +@article{Yan:2024yir, + author = "Yan, Mengshi and Hou, Tie-Jiun and Li, Zhao and Mohan, Kirtimaan and Yuan, C. -P.", + title = "{A generalized statistical model for fits to parton distributions}", + eprint = "2406.01664", + archivePrefix = "arXiv", + primaryClass = "hep-ph", + reportNumber = "MSUHEP-24-002", + month = "6", + year = "2024" +} + +% June 05, 2024 +@inproceedings{Barontini:2024dyb, + author = "Barontini, Andrea and Laurenti, Niccolo and Rojo, Juan", + title = "{NNPDF4.0 aN$^3$LO PDFs with QED corrections}", + booktitle = "{31st International Workshop on Deep-Inelastic Scattering and Related Subjects}", + eprint = "2406.01779", + archivePrefix = "arXiv", + primaryClass = "hep-ph", + month = "6", + year = "2024" +} + +% June 05, 2024 +@article{Dreyer:2024bhs, + author = "Dreyer, Etienne and Gross, Eilam and Kobylianskii, Dmitrii and Mikuni, Vinicius and Nachman, Benjamin and Soybelman, Nathalie", + title = "{Parnassus: An Automated Approach to Accurate, Precise, and Fast Detector Simulation and Reconstruction}", + eprint = "2406.01620", + archivePrefix = "arXiv", + primaryClass = "physics.data-an", + month = "5", + year = "2024" +} + +% June 03, 2024 +@article{Buss:2024orz, + author = "Buss, Thorsten and Gaede, Frank and Kasieczka, Gregor and Krause, Claudius and Shih, David", + title = "{Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows}", + eprint = "2405.20407", + archivePrefix = "arXiv", + primaryClass = "physics.ins-det", + reportNumber = "HEPHY-ML-24-02", + month = "5", + year = "2024" +} + +% May 31, 2024 +@article{ATLAS:2024xxl, + author = "{ATLAS Collaboration}", + title = "{A simultaneous unbinned differential cross section measurement of twenty-four $Z$+jets kinematic observables with the ATLAS detector}", + eprint = "2405.20041", + archivePrefix = "arXiv", + primaryClass = "hep-ex", + reportNumber = "CERN-EP-2024-132", + month = "5", + year = "2024" +} + +% May 31, 2024 +@article{Zhang:2024mcu, + author = "Zhang, Zhengkang", + title = "{Neural Scaling Laws From Large-N Field Theory: Solvable Model Beyond the Ridgeless Limit}", + eprint = "2405.19398", + archivePrefix = "arXiv", + primaryClass = "hep-th", + month = "5", + year = "2024" +} + +% May 30, 2024 +@article{Hammad:2024hhm, + author = "Hammad, A. and Ko, P. and Lu, Chih-Ting and Park, Myeonghun", + title = "{Exploring Exotic Decays of the Higgs Boson to Multi-Photons at the LHC via Multimodal Learning Approaches}", + eprint = "2405.18834", + archivePrefix = "arXiv", + primaryClass = "hep-ph", + month = "5", + year = "2024" +} + % May 28, 2024 @article{Kopp:2024lch, author = "Kopp, Joachim and Machado, Pedro and MacMahon, Margot and Martinez-Soler, Ivan", diff --git a/HEPML.tex b/HEPML.tex index bd0f8cf..edd83f6 100644 --- a/HEPML.tex +++ b/HEPML.tex @@ -43,7 +43,7 @@ \\\textit{Below are links to many (static) general and specialized reviews. The third bullet contains links to classic papers that applied shallow learning methods many decades before the deep learning revolution.} \begin{itemize} \item Modern reviews~\cite{Larkoski:2017jix,Guest:2018yhq,Albertsson:2018maf,Radovic:2018dip,Carleo:2019ptp,Bourilkov:2019yoi,Schwartz:2021ftp,Karagiorgi:2021ngt,Boehnlein:2021eym,Shanahan:2022ifi} - \item Specialized reviews~\cite{Kasieczka:2019dbj,1807719,1808887,Psihas:2020pby,Butter:2020tvl,Forte:2020yip,Brehmer:2020cvb,Nachman:2020ccu,Duarte:2020ngm,Vlimant:2020enz,Cranmer:2019eaq,Rousseau:2020rnz,Kagan:2020yrm,Guan:2020bdl,deLima:2021fwm,Alanazi:2021grv,Baldi:2022okj,Viren:2022qon,Bogatskiy:2022hub,Butter:2022rso,Dvorkin:2022pwo,Adelmann:2022ozp,Thais:2022iok,Harris:2022qtm,Coadou:2022nsh,Benelli:2022sqn,Chen:2022pzc,Plehn:2022ftl,Cheng:2022idp,Huerta:2022kgj,Huber:2022lpm,Zhou:2023pti,DeZoort:2023vrm,Du:2023qst,Allaire:2023fgp,Hashemi:2023rgo,Belis:2023mqs,Araz:2023mda,Gooding:2024wpi,Kheddar:2024osf,Bardhan:2024zla,Mondal:2024nsa,Huetsch:2024quz} + \item Specialized reviews~\cite{Kasieczka:2019dbj,1807719,1808887,Psihas:2020pby,Butter:2020tvl,Forte:2020yip,Brehmer:2020cvb,Nachman:2020ccu,Duarte:2020ngm,Vlimant:2020enz,Cranmer:2019eaq,Rousseau:2020rnz,Kagan:2020yrm,Guan:2020bdl,deLima:2021fwm,Alanazi:2021grv,Baldi:2022okj,Viren:2022qon,Bogatskiy:2022hub,Butter:2022rso,Dvorkin:2022pwo,Adelmann:2022ozp,Thais:2022iok,Harris:2022qtm,Coadou:2022nsh,Benelli:2022sqn,Chen:2022pzc,Plehn:2022ftl,Cheng:2022idp,Huerta:2022kgj,Huber:2022lpm,Zhou:2023pti,DeZoort:2023vrm,Du:2023qst,Allaire:2023fgp,Hashemi:2023rgo,Belis:2023mqs,Araz:2023mda,Gooding:2024wpi,Kheddar:2024osf,Bardhan:2024zla,Mondal:2024nsa,Huetsch:2024quz,Ahmad:2024dql,Barman:2024wfx,Larkoski:2024uoc,Halverson:2024hax} \item Classical papers~\cite{Denby:1987rk,Lonnblad:1990bi} \item Datasets~\cite{Kasieczka:2021xcg,Aarrestad:2021oeb,Benato:2021olt,Govorkova:2021hqu,Chen:2021euv,Qu:2022mxj,Eller:2023myr,Rusack:2023pob} \end{itemize} @@ -63,42 +63,42 @@ \\\textit{Data that have a variable with a particular order may be represented as a sequence. Recurrent neural networks are natural tools for processing sequence data. } \item \textbf{Trees}~\cite{Louppe:2017ipp,Cheng:2017rdo,Jercic:2021bfc,Dutta:2023jbz,Belfkir:2023vpo,Finke:2023ltw,Matousek:2024vpa,Choudhury:2024crp} \\\textit{Recursive neural networks are natural tools for processing data in a tree structure.} - \item \textbf{Graphs}~\cite{Henrion:DLPS2017,Ju:2020xty,Abdughani:2018wrw,Martinez:2018fwc,Ren:2019xhp,Moreno:2019bmu,Qasim:2019otl,Chakraborty:2019imr,DiBello:2020bas,Chakraborty:2020yfc,1797439,1801423,1808887,Iiyama:2020wap,1811770,Choma:2020cry,alonsomonsalve2020graph,guo2020boosted,Heintz:2020soy,Verma:2020gnq,Dreyer:2020brq,Qian:2021vnh,Pata:2021oez,Biscarat:2021dlj,Rossi:2021tjf,Hewes:2021heg,Thais:2021qcb,Dezoort:2021kfk,Verma:2021ceh,Hariri:2021clz,Belavin:2021bxb,Atkinson:2021nlt,Konar:2021zdg,Atkinson:2021jnj,Tsan:2021brw,Elabd:2021lgo,Pata:2022wam,Gong:2022lye,Qasim:2022rww,Ma:2022bvt,Bogatskiy:2022czk,Builtjes:2022usj,DiBello:2022iwf,Mokhtar:2022pwm,Huang:2023ssr,Forestano:2023fpj,Anisha:2023xmh,Ehrke:2023cpn,Murnane:2023kfm,Yu:2023juh,Neu:2023sfh,Wang:2023cac,McEneaney:2023vwp,Liu:2023siw,GarciaPardinas:2023pmx,Duperrin:2023elp,BelleII:2023egc,Holmberg:2023rfr,Bhattacherjee:2023evs,Murnane:2023ksa,Konar:2023ptv,Chatterjee:2024pbp,Heinrich:2024tdf,Mo:2024dru,Lu:2024qrc,Birch-Sykes:2024gij,Belle-II:2024lwr,Pfeffer:2024tjl,Aurisano:2024uvd,Kobylianskii:2024sup} + \item \textbf{Graphs}~\cite{Henrion:DLPS2017,Ju:2020xty,Abdughani:2018wrw,Martinez:2018fwc,Ren:2019xhp,Moreno:2019bmu,Qasim:2019otl,Chakraborty:2019imr,DiBello:2020bas,Chakraborty:2020yfc,1797439,1801423,1808887,Iiyama:2020wap,1811770,Choma:2020cry,alonsomonsalve2020graph,guo2020boosted,Heintz:2020soy,Verma:2020gnq,Dreyer:2020brq,Qian:2021vnh,Pata:2021oez,Biscarat:2021dlj,Rossi:2021tjf,Hewes:2021heg,Thais:2021qcb,Dezoort:2021kfk,Verma:2021ceh,Hariri:2021clz,Belavin:2021bxb,Atkinson:2021nlt,Konar:2021zdg,Atkinson:2021jnj,Tsan:2021brw,Elabd:2021lgo,Pata:2022wam,Gong:2022lye,Qasim:2022rww,Ma:2022bvt,Bogatskiy:2022czk,Builtjes:2022usj,DiBello:2022iwf,Mokhtar:2022pwm,Huang:2023ssr,Forestano:2023fpj,Anisha:2023xmh,Ehrke:2023cpn,Murnane:2023kfm,Yu:2023juh,Neu:2023sfh,Wang:2023cac,McEneaney:2023vwp,Liu:2023siw,GarciaPardinas:2023pmx,Duperrin:2023elp,BelleII:2023egc,Holmberg:2023rfr,Bhattacherjee:2023evs,Murnane:2023ksa,Konar:2023ptv,Chatterjee:2024pbp,Heinrich:2024tdf,Mo:2024dru,Lu:2024qrc,Birch-Sykes:2024gij,Belle-II:2024lwr,Pfeffer:2024tjl,Aurisano:2024uvd,Kobylianskii:2024sup,Aamir:2024lpz,Soybelman:2024mbv,Correia:2024ogc,Calafiura:2024qhv} \\\textit{A graph is a collection of nodes and edges. Graph neural networks are natural tools for processing data in a tree structure.} \item \textbf{Sets (point clouds)}~\cite{Komiske:2018cqr,Qu:2019gqs,Mikuni:2020wpr,Shlomi:2020ufi,Dolan:2020qkr,Fenton:2020woz,Lee:2020qil,collado2021learning,Mikuni:2021pou,Shmakov:2021qdz,Shimmin:2021pkm,ATL-PHYS-PUB-2020-014,Qu:2022mxj,Kach:2022uzq,Onyisi:2022hdh,Athanasakos:2023fhq,Kach:2023rqw,Badea:2023jdb,Buhmann:2023zgc,Acosta:2023nuw,Mondal:2023law,Hammad:2023sbd,Odagiu:2024bkp,Gambhir:2024dtf} \\\textit{A point cloud is a (potentially variable-size) set of points in space. Sets are distinguished from sequences in that there is no particular order (i.e. permutation invariance). Sets can also be viewed as graphs without edges and so graph methods that can parse variable-length inputs may also be appropriate for set learning, although there are other methods as well.} - \item \textbf{Physics-inspired basis}~\cite{Datta:2019,Datta:2017rhs,Datta:2017lxt,Komiske:2017aww,Butter:2017cot,Grojean:2020ech,Kishimoto:2022eum,Larkoski:2023nye,Munoz:2023csn,Witkowski:2023xmx,Romero:2023hrk,Diaz:2023otq,Matchev:2024ash} + \item \textbf{Physics-inspired basis}~\cite{Datta:2019,Datta:2017rhs,Datta:2017lxt,Komiske:2017aww,Butter:2017cot,Grojean:2020ech,Kishimoto:2022eum,Larkoski:2023nye,Munoz:2023csn,Witkowski:2023xmx,Romero:2023hrk,Diaz:2023otq,Matchev:2024ash,Ramirez-Morales:2024krk,Farrell:2024aah,Hallin:2024gmt} \\\textit{This is a catch-all category for learning using other representations that use some sort of manual or automated physics-preprocessing.} \end{itemize} \item \textbf{Targets} \begin{itemize} - \item \textbf{$W/Z$ tagging}~\cite{deOliveira:2015xxd,Barnard:2016qma,Louppe:2017ipp,Sirunyan:2020lcu,Chen:2019uar,1811770,Dreyer:2020brq,Kim:2021gtv,Subba:2022czw,Aguilar-Saavedra:2023pde,Athanasakos:2023fhq,Grossi:2023fqq,Baron:2023yhw,Bogatskiy:2023nnw} + \item \textbf{$W/Z$ tagging}~\cite{deOliveira:2015xxd,Barnard:2016qma,Louppe:2017ipp,Sirunyan:2020lcu,Chen:2019uar,1811770,Dreyer:2020brq,Kim:2021gtv,Subba:2022czw,Aguilar-Saavedra:2023pde,Athanasakos:2023fhq,Grossi:2023fqq,Baron:2023yhw,Bogatskiy:2023nnw,Bose:2024pwc} \\\textit{Boosted, hadronically decaying $W$ and $Z$ bosons form jets that are distinguished from generic quark and gluon jets by their mass near the boson mass and their two-prong substructure.} \item \textbf{$H\rightarrow b\bar{b}$}~\cite{Datta:2019ndh,Lin:2018cin,Moreno:2019neq,Chakraborty:2019imr,Sirunyan:2020lcu,Chung:2020ysf,Tannenwald:2020mhq,guo2020boosted,Abbas:2020khd,Jang:2021eph,Khosa:2021cyk} \\\textit{Due to the fidelity of $b$-tagging, boosted, hadronically decaying Higgs bosons (predominantly decaying to $b\bar{b}$) has unique challenged and opportunities compared with $W/Z$ tagging.} - \item \textbf{quarks and gluons}~\cite{ATL-PHYS-PUB-2017-017,Komiske:2016rsd,Cheng:2017rdo,Stoye:DLPS2017,Chien:2018dfn,Moreno:2019bmu,Kasieczka:2018lwf,1806025,Lee:2019ssx,Lee:2019cad,Dreyer:2020brq,Romero:2021qlf,Filipek:2021qbe,Dreyer:2021hhr,Bright-Thonney:2022xkx,CrispimRomao:2023ssj,Athanasakos:2023fhq,He:2023cfc,Shen:2023ofd,Dolan:2023abg} + \item \textbf{quarks and gluons}~\cite{ATL-PHYS-PUB-2017-017,Komiske:2016rsd,Cheng:2017rdo,Stoye:DLPS2017,Chien:2018dfn,Moreno:2019bmu,Kasieczka:2018lwf,1806025,Lee:2019ssx,Lee:2019cad,Dreyer:2020brq,Romero:2021qlf,Filipek:2021qbe,Dreyer:2021hhr,Bright-Thonney:2022xkx,CrispimRomao:2023ssj,Athanasakos:2023fhq,He:2023cfc,Shen:2023ofd,Dolan:2023abg,Blekman:2024wyf,Sandoval:2024ldp,Wu:2024thh} \\\textit{Quark jets tend to be narrower and have fewer particles than gluon jets. This classification task has been a benchmark for many new machine learning models.} - \item \textbf{top quark} tagging~\cite{Almeida:2015jua,Stoye:DLPS2017,Kasieczka:2019dbj,Chakraborty:2020yfc,Diefenbacher:2019ezd,Butter:2017cot,Kasieczka:2017nvn,Macaluso:2018tck,Bhattacharya:2020vzu,Lim:2020igi,Dreyer:2020brq,Aguilar-Saavedra:2021rjk,Andrews:2021ejw,Dreyer:2022yom,Ahmed:2022hct,Munoz:2022gjq,Bhattacherjee:2022gjq,Choi:2023slq,Keicher:2023mer,He:2023cfc,Bogatskiy:2023nnw,Shen:2023ofd,Isildak:2023dnf,Sahu:2023uwb,Baron:2023yhw,Bogatskiy:2023fug,Liu:2023dio,Batson:2023ohn,Furuichi:2023vdx,Ngairangbam:2023cps,Cai:2024xnt} + \item \textbf{top quark} tagging~\cite{Almeida:2015jua,Stoye:DLPS2017,Kasieczka:2019dbj,Chakraborty:2020yfc,Diefenbacher:2019ezd,Butter:2017cot,Kasieczka:2017nvn,Macaluso:2018tck,Bhattacharya:2020vzu,Lim:2020igi,Dreyer:2020brq,Aguilar-Saavedra:2021rjk,Andrews:2021ejw,Dreyer:2022yom,Ahmed:2022hct,Munoz:2022gjq,Bhattacherjee:2022gjq,Choi:2023slq,Keicher:2023mer,He:2023cfc,Bogatskiy:2023nnw,Shen:2023ofd,Isildak:2023dnf,Sahu:2023uwb,Baron:2023yhw,Bogatskiy:2023fug,Liu:2023dio,Batson:2023ohn,Furuichi:2023vdx,Ngairangbam:2023cps,Cai:2024xnt,Dong:2024xsg} \\\textit{Boosted top quarks form jets that have a three-prong substructure ($t\rightarrow Wb,W\rightarrow q\bar{q}$).} \item \textbf{strange jets}~\cite{Nakai:2020kuu,Erdmann:2019blf,Erdmann:2020ovh,Subba:2023rpm} \\\textit{Strange quarks have a very similar fragmentation to generic quark and gluon jets, so this is a particularly challenging task.} \item \textbf{$b$-tagging}~\cite{Sirunyan:2017ezt,Guest:2016iqz,Keck:2018lcd,bielkov2020identifying,Bols:2020bkb,ATL-PHYS-PUB-2017-003,ATL-PHYS-PUB-2020-014,Liao:2022ufk,Stein:2023cnt,ATLAS:2023gog,Tamir:2023aiz,VanStroud:2023ggs,Song:2024aka} \\\textit{Due to their long (but not too long) lifetime, the $B$-hadron lifetime is macroscopic and $b$-jet tagging has been one of the earliest adapters of modern machine learning tools.} - \item \textbf{Flavor physics}~\cite{1811097,Bahtiyar:2022une,Zhang:2023czx,Nishimura:2023wdu,Smith:2023ssh,Tian:2024yfz,Chang:2024ksq,Co:2024bfl,Malekhosseini:2024eot,Chen:2024epd} + \item \textbf{Flavor physics}~\cite{1811097,Bahtiyar:2022une,Zhang:2023czx,Nishimura:2023wdu,Smith:2023ssh,Tian:2024yfz,Chang:2024ksq,Co:2024bfl,Malekhosseini:2024eot,Chen:2024epd,Mansouri:2024uwc} \\\textit{This category is for studies related to exclusive particle decays, especially with bottom and charm hadrons.} - \item \textbf{BSM particles and models}~\cite{Datta:2019ndh,Baldi:2014kfa,Chakraborty:2019imr,10.1088/2632-2153/ab9023,1792136,1801423,Chang:2020rtc,Cogollo:2020afo,Grossi:2020orx,Ngairangbam:2020ksz,Englert:2020ntw,Freitas:2020ttd,Khosa:2019kxd,Freitas:2019hbk,Stakia:2021pvp,Arganda:2021azw,Jorge:2021vpo,Ren:2021prq,Barron:2021btf,Yang:2021gge,Alvestad:2021sje,Morais:2021ead,Jung:2021tym,Drees:2021oew,Cornell:2021gut,Vidal:2021oed,Beauchesne:2021qrw,Feng:2021eke,Konar:2022bgc,Badea:2022dzb,Freitas:2022cno,Goodsell:2022beo,Lv:2022pme,Ai:2022qvs,Yang:2022fhw,Alasfar:2022vqw,Barbosa:2022mmw,Chiang:2022lsn,Hall:2022bme,Faucett:2022zie,Bhattacharya:2022kje,Bardhan:2022sif,Bhattacharyya:2022umc,ATLAS:2022ihe,CMS:2022idi,Ballabene:2022fms,ATLAS:2023mcc,Palit:2023dvs,Liu:2023gpt,Pedro:2023sdp,MB:2023edk,Dong:2023nir,Guo:2023jkz,Lu:2023gjk,Flacke:2023eil,Bardhan:2023mia,Aguilar-Saavedra:2023pde,Cremer:2023gne,Esmail:2023axd,Choudhury:2023eje,Bhattacherjee:2023evs,Grefsrud:2023dad,Wang:2023pqx,Zhang:2023ykh,Hammad:2023wme,Hammad:2023sbd,Zhang:2024bld,Ma:2024deu,Jurciukonis:2024hlg,Chiang:2024pho,Birch-Sykes:2024gij,Ahmed:2024iqx,Esmail:2024gdc} + \item \textbf{BSM particles and models}~\cite{Datta:2019ndh,Baldi:2014kfa,Chakraborty:2019imr,10.1088/2632-2153/ab9023,1792136,1801423,Chang:2020rtc,Cogollo:2020afo,Grossi:2020orx,Ngairangbam:2020ksz,Englert:2020ntw,Freitas:2020ttd,Khosa:2019kxd,Freitas:2019hbk,Stakia:2021pvp,Arganda:2021azw,Jorge:2021vpo,Ren:2021prq,Barron:2021btf,Yang:2021gge,Alvestad:2021sje,Morais:2021ead,Jung:2021tym,Drees:2021oew,Cornell:2021gut,Vidal:2021oed,Beauchesne:2021qrw,Feng:2021eke,Konar:2022bgc,Badea:2022dzb,Freitas:2022cno,Goodsell:2022beo,Lv:2022pme,Ai:2022qvs,Yang:2022fhw,Alasfar:2022vqw,Barbosa:2022mmw,Chiang:2022lsn,Hall:2022bme,Faucett:2022zie,Bhattacharya:2022kje,Bardhan:2022sif,Bhattacharyya:2022umc,ATLAS:2022ihe,CMS:2022idi,Ballabene:2022fms,ATLAS:2023mcc,Palit:2023dvs,Liu:2023gpt,Pedro:2023sdp,MB:2023edk,Dong:2023nir,Guo:2023jkz,Lu:2023gjk,Flacke:2023eil,Bardhan:2023mia,Aguilar-Saavedra:2023pde,Cremer:2023gne,Esmail:2023axd,Choudhury:2023eje,Bhattacherjee:2023evs,Grefsrud:2023dad,Wang:2023pqx,Zhang:2023ykh,Hammad:2023wme,Hammad:2023sbd,Zhang:2024bld,Ma:2024deu,Jurciukonis:2024hlg,Chiang:2024pho,Birch-Sykes:2024gij,Ahmed:2024iqx,Esmail:2024gdc,Bickendorf:2024ovi,Wojcik:2024lfy} \\\textit{There are many proposals to train classifiers to enhance the presence of particular new physics models.} \item \textbf{Particle identification}~\cite{deOliveira:2018lqd,Paganini:DLPS2017,Hooberman:DLPS2017,Keck:2018lcd,Belayneh:2019vyx,Qasim:2019otl,Collado:2020fwm,Verma:2021ixg,Graziani:2021vai,Graczykowski:2022zae,Fanelli:2022ifa,Dimitrova:2022uum,Ryzhikov:2022lbu,Kushawaha:2023dms,Wu:2023pzn,Prasad:2023zdd,Lange:2023gbe,Novosel:2023cki,Charan:2023ldg,NA62:2023wzm,Karwowska:2023dhl,Song:2023ceh,Kasak:2023hhr,Ai:2024mkl} \\\textit{This is a generic category for direct particle identification and categorization using various detector technologies. Direct means that the particle directly interacts with the detector (in contrast with $b$-tagging).} \item \textbf{Neutrino Detectors}~\cite{Aurisano:2016jvx,Acciarri:2016ryt,Hertel:DLPS2017,Adams:2018bvi,Domine:2019zhm,Aiello:2020orq,Adams:2020vlj,Domine:2020tlx,DUNE:2020gpm,DeepLearnPhysics:2020hut,Koh:2020snv,Yu:2020wxu,Psihas:2020pby,alonsomonsalve2020graph,Abratenko:2020pbp,Clerbaux:2020ttg,Liu:2020pzv,Abratenko:2020ocq,Chen:2020zkj,Qian:2021vnh,abbasi2021convolutional,Drielsma:2021jdv,Rossi:2021tjf,Hewes:2021heg,Acciarri:2021oav,Belavin:2021bxb,Maksimovic:2021dmz,Gavrikov:2021ktt,Garcia-Mendez:2021vts,Carloni:2021zbc,MicroBooNE:2021nss,MicroBooNE:2021ojx,Elkarghli:2020owr,DUNE:2022fiy,Lutkus:2022eou,Chappell:2022yxd,Bachlechner:2022cvf,Sogaard:2022qgg,IceCube:2022njh,Bai:2022lbv,Biassoni:2023lih,Yu:2023ehc,Mo:2024dru,Bat:2024gln,Aurisano:2024uvd,IceCube:2024xjj,Cai:2024bpv,Kopp:2024lch} \\\textit{Neutrino detectors are very large in order to have a sizable rate of neutrino detection. The entire neutrino interaction can be characterized to distinguish different neutrino flavors.} - \item \textbf{Direct Dark Matter Detectors}~\cite{Ilyasov_2020,Akerib:2020aws,Khosa:2019qgp,Golovatiuk:2021lqn,McDonald:2021hus,Coarasa:2021fpv,Herrero-Garcia:2021goa,Liang:2021nsz,Li:2022tvg,Biassoni:2023lih,XENONCollaboration:2023dar,Ghrear:2024rku} + \item \textbf{Direct Dark Matter Detectors}~\cite{Ilyasov_2020,Akerib:2020aws,Khosa:2019qgp,Golovatiuk:2021lqn,McDonald:2021hus,Coarasa:2021fpv,Herrero-Garcia:2021goa,Liang:2021nsz,Li:2022tvg,Biassoni:2023lih,XENONCollaboration:2023dar,Ghrear:2024rku,Cerdeno:2024uqt} \\\textit{Dark matter detectors are similar to neutrino detectors, but aim to achieve `zero' background.} - \item \textbf{Cosmology, Astro Particle, and Cosmic Ray physics}~\cite{Ostdiek:2020cqz,Brehmer:2019jyt,Tsai:2020vcx,Verma:2020gnq,Aab:2021rcn,Balazs:2021uhg,gonzalez2021tackling,Conceicao:2021xgn,huang2021convolutionalneuralnetwork,Droz:2021wnh,Han:2021kjx,Arjona:2021hmg,1853992,Shih:2021kbt,Ikeda:2021sxm,Aizpuru:2021vhd,Vago:2021grx,List:2021aer,Kahlhoefer:2021sha,Sabiu:2021aea,Mishra-Sharma:2021nhh,Mishra-Sharma:2021oxe,Bister:2021arb,Chen:2019avc,De:2022sde,Montel:2022fhv,Glauch:2022xth,Sun:2022djj,Abel:2022nje,Zhang:2022djp,Nguyen:2022ldb,Goriely:2022upe,Kim:2023wuk,Zhou:2023cfs,Carvalho:2023ele,Cai:2023gol,Krastev:2023fnh,Hatefi:2023gpj,Guo:2023mhf,Thakur:2024mxs,Kalaczynski:2024wxa,Riehn:2024prp,Yoon:2024pbz} + \item \textbf{Cosmology, Astro Particle, and Cosmic Ray physics}~\cite{Ostdiek:2020cqz,Brehmer:2019jyt,Tsai:2020vcx,Verma:2020gnq,Aab:2021rcn,Balazs:2021uhg,gonzalez2021tackling,Conceicao:2021xgn,huang2021convolutionalneuralnetwork,Droz:2021wnh,Han:2021kjx,Arjona:2021hmg,1853992,Shih:2021kbt,Ikeda:2021sxm,Aizpuru:2021vhd,Vago:2021grx,List:2021aer,Kahlhoefer:2021sha,Sabiu:2021aea,Mishra-Sharma:2021nhh,Mishra-Sharma:2021oxe,Bister:2021arb,Chen:2019avc,De:2022sde,Montel:2022fhv,Glauch:2022xth,Sun:2022djj,Abel:2022nje,Zhang:2022djp,Nguyen:2022ldb,Goriely:2022upe,Kim:2023wuk,Zhou:2023cfs,Carvalho:2023ele,Cai:2023gol,Krastev:2023fnh,Hatefi:2023gpj,Guo:2023mhf,Thakur:2024mxs,Kalaczynski:2024wxa,Riehn:2024prp,Yoon:2024pbz,Hatefi:2024asc,Ahn:2024lkh,Heisig:2024jkk} \\\textit{Machine learning is often used in astrophysics and cosmology in different ways than terrestrial particle physics experiments due to a general divide between Bayesian and Frequentist statistics. However, there are many similar tasks and a growing number of proposals designed for one domain that apply to the other. See also https://github.com/georgestein/ml-in-cosmology.} - \item \textbf{Tracking}~\cite{Farrell:DLPS2017,Farrell:2018cjr,Amrouche:2019wmx,Ju:2020xty,Akar:2020jti,Shlomi:2020ufi,Choma:2020cry,Siviero:2020tim,Fox:2020hfm,Amrouche:2021tlm,goto2021development,Biscarat:2021dlj,Akar:2021gns,Thais:2021qcb,Ju:2021ayy,Dezoort:2021kfk,Edmonds:2021lzd,Lavrik:2021zgt,Huth:2021zcm,Goncharov:2021wvd,Wang:2022oer,Alonso-Monsalve:2022zlm,Bakina:2022mhs,Akram:2022zmj,Sun:2022bxx,Abidi:2022ogh,Bae:2023eec,Knipfer:2023zrv,Akar:2023zhd,Mieskolainen:2023hkz,Allaire:2023dfg,Allaire:2023llb,Huang:2024voo,Gavalian:2024icb,Guiang:2024qzk} + \item \textbf{Tracking}~\cite{Farrell:DLPS2017,Farrell:2018cjr,Amrouche:2019wmx,Ju:2020xty,Akar:2020jti,Shlomi:2020ufi,Choma:2020cry,Siviero:2020tim,Fox:2020hfm,Amrouche:2021tlm,goto2021development,Biscarat:2021dlj,Akar:2021gns,Thais:2021qcb,Ju:2021ayy,Dezoort:2021kfk,Edmonds:2021lzd,Lavrik:2021zgt,Huth:2021zcm,Goncharov:2021wvd,Wang:2022oer,Alonso-Monsalve:2022zlm,Bakina:2022mhs,Akram:2022zmj,Sun:2022bxx,Abidi:2022ogh,Bae:2023eec,Knipfer:2023zrv,Akar:2023zhd,Mieskolainen:2023hkz,Allaire:2023dfg,Allaire:2023llb,Huang:2024voo,Gavalian:2024icb,Guiang:2024qzk,Caron:2024cyo} \\\textit{Charged particle tracking is a challenging pattern recognition task. This category is for various classification tasks associated with tracking, such as seed selection.} - \item \textbf{Heavy Ions / Nuclear Physics}~\cite{Pang:2016vdc,Chien:2018dfn,Du:2020pmp,Du:2019civ,Mallick:2021wop,Nagu:2021zho,Zhao:2021yjo,Sombillo:2021ifs,Zhou:2021bvw,Apolinario:2021olp,Brown:2021upr,Du:2021pqa,Kuttan:2021npg,Huang:2021iux,Shokr:2021ouh,He:2021uko,Habashy:2021orz,Zepeda:2021tzp,Mishra:2021eqb,Ng:2021ibr,Habashy:2021qku,Biro:2021zgm,Lai:2021ckt,Du:2021qwv,Du:2021brx,Xiang:2021ssj,Soma:2022qnv,Rahman:2022tfq,Boglione:2022gpv,Liyanage:2022byj,Liu:2022hzd,Fanelli:2022kro,Chen:2022shj,Saha:2022skj,Lee:2022kdn,Biro:2022zhl,Zhang:2022hjh,Yang:2022eag,Rigo:2022ces,Yang:2022rlw,Munoz:2022slm,Goriely:2022upe,Mallick:2022alr,Fore:2022ljl,Steffanic:2023cyx,Mallick:2023vgi,He:2023urp,Xu:2023fbs,Kanwar:2023otc,Mumpower:2023lch,Escher:2023oyy,Hirvonen:2023lqy,Biro:2023kyx,He:2023zin,Zhou:2023pti,CrispimRomao:2023ssj,Basak:2023wzq,Shi:2023xfz,Soleymaninia:2023dds,Lin:2023bmy,Dellen:2023avd,AlHammal:2023svo,Wang:2023muv,Wang:2023kcg,Ai:2023azx,Yiu:2023ido,Karmakar:2023mhy,Lasseri:2023dhi,Yoshida:2023wrb,Liu:2023xgl,Hizawa:2023plv,Wen:2023oju,Allaire:2023fgp,Bedaque:2023udu,Lay:2023boz,Wang:2024gjz,Mengel:2024fcl,Hirvonen:2024ycx,Goswami:2024xrx,Santos:2024bqr} + \item \textbf{Heavy Ions / Nuclear Physics}~\cite{Pang:2016vdc,Chien:2018dfn,Du:2020pmp,Du:2019civ,Mallick:2021wop,Nagu:2021zho,Zhao:2021yjo,Sombillo:2021ifs,Zhou:2021bvw,Apolinario:2021olp,Brown:2021upr,Du:2021pqa,Kuttan:2021npg,Huang:2021iux,Shokr:2021ouh,He:2021uko,Habashy:2021orz,Zepeda:2021tzp,Mishra:2021eqb,Ng:2021ibr,Habashy:2021qku,Biro:2021zgm,Lai:2021ckt,Du:2021qwv,Du:2021brx,Xiang:2021ssj,Soma:2022qnv,Rahman:2022tfq,Boglione:2022gpv,Liyanage:2022byj,Liu:2022hzd,Fanelli:2022kro,Chen:2022shj,Saha:2022skj,Lee:2022kdn,Biro:2022zhl,Zhang:2022hjh,Yang:2022eag,Rigo:2022ces,Yang:2022rlw,Munoz:2022slm,Goriely:2022upe,Mallick:2022alr,Fore:2022ljl,Steffanic:2023cyx,Mallick:2023vgi,He:2023urp,Xu:2023fbs,Kanwar:2023otc,Mumpower:2023lch,Escher:2023oyy,Hirvonen:2023lqy,Biro:2023kyx,He:2023zin,Zhou:2023pti,CrispimRomao:2023ssj,Basak:2023wzq,Shi:2023xfz,Soleymaninia:2023dds,Lin:2023bmy,Dellen:2023avd,AlHammal:2023svo,Wang:2023muv,Wang:2023kcg,Ai:2023azx,Yiu:2023ido,Karmakar:2023mhy,Lasseri:2023dhi,Yoshida:2023wrb,Liu:2023xgl,Hizawa:2023plv,Wen:2023oju,Allaire:2023fgp,Bedaque:2023udu,Lay:2023boz,Wang:2024gjz,Mengel:2024fcl,Hirvonen:2024ycx,Goswami:2024xrx,Santos:2024bqr,Hirvonen:2024zne,Liuti:2024zkc} \\\textit{Many tools in high energy nuclear physics are similar to high energy particle physics. The physics target of these studies are to understand collective properties of the strong force.} \end{itemize} \item \textbf{Learning strategies} @@ -126,9 +126,9 @@ \item \textbf{Fast inference / deployment} \\\textit{There are many practical issues that can be critical for the actual application of machine learning models.} \begin{itemize} - \item \textbf{Software}~\cite{Strong:2020mge,Gligorov:2012qt,Weitekamp:DLPS2017,Nguyen:2018ugw,Bourgeois:2018nvk,1792136,Balazs:2021uhg,Rehm:2021zow,Mahesh:2021iph,Amrouche:2021tio,Pol:2021iqw,Goncharov:2021wvd,Saito:2021vpp,Jiang:2022zho,Garg:2022tal,Duarte:2022job,Guo:2023nfu,Tyson:2023zkx,DPHEP:2023blx,DiBello:2023kzc,Bal:2023bvt,Kauffman:2024bov,Held:2024gwj,CALICE:2024imr,Ivanov:2024whr,Bierlich:2024vqo} + \item \textbf{Software}~\cite{Strong:2020mge,Gligorov:2012qt,Weitekamp:DLPS2017,Nguyen:2018ugw,Bourgeois:2018nvk,1792136,Balazs:2021uhg,Rehm:2021zow,Mahesh:2021iph,Amrouche:2021tio,Pol:2021iqw,Goncharov:2021wvd,Saito:2021vpp,Jiang:2022zho,Garg:2022tal,Duarte:2022job,Guo:2023nfu,Tyson:2023zkx,DPHEP:2023blx,DiBello:2023kzc,Bal:2023bvt,Kauffman:2024bov,Held:2024gwj,CALICE:2024imr,Ivanov:2024whr,Bierlich:2024vqo,Pratiush:2024ltm} \\\textit{Strategies for efficient inference for a given hardware architecture.} - \item \textbf{Hardware/firmware}~\cite{Duarte:2018ite,DiGuglielmo:2020eqx,Summers:2020xiy,1808088,Iiyama:2020wap,Mohan:2020vvi,Carrazza:2020qwu,Rankin:2020usv,Heintz:2020soy,Rossi:2020sbh,Aarrestad:2021zos,Hawks:2021ruw,Teixeira:2021yhl,Hong:2021snb,DiGuglielmo:2021ide,Migliorini:2021fuj,Govorkova:2021utb,Elabd:2021lgo,Jwa:2019zlh,Butter:2022lkf,Sun:2022bxx,Khoda:2022dwz,Carlson:2022vac,Abidi:2022ogh,MeyerzuTheenhausen:2022ffb,Cai:2023ldc,Herbst:2023lug,Coccaro:2023nol,Neu:2023sfh,Okabe:2023efz,Yaary:2023dvw,Schulte:2023gtt,Yoo:2023lxy,Grosso:2023owo,Jin:2023xts,Lin:2023xrw,Zipper:2023ybp,Delaney:2023swp,Dickinson:2023yes,CMS:2024twn,Bahr:2024dzg} + \item \textbf{Hardware/firmware}~\cite{Duarte:2018ite,DiGuglielmo:2020eqx,Summers:2020xiy,1808088,Iiyama:2020wap,Mohan:2020vvi,Carrazza:2020qwu,Rankin:2020usv,Heintz:2020soy,Rossi:2020sbh,Aarrestad:2021zos,Hawks:2021ruw,Teixeira:2021yhl,Hong:2021snb,DiGuglielmo:2021ide,Migliorini:2021fuj,Govorkova:2021utb,Elabd:2021lgo,Jwa:2019zlh,Butter:2022lkf,Sun:2022bxx,Khoda:2022dwz,Carlson:2022vac,Abidi:2022ogh,MeyerzuTheenhausen:2022ffb,Cai:2023ldc,Herbst:2023lug,Coccaro:2023nol,Neu:2023sfh,Okabe:2023efz,Yaary:2023dvw,Schulte:2023gtt,Yoo:2023lxy,Grosso:2023owo,Jin:2023xts,Lin:2023xrw,Zipper:2023ybp,Delaney:2023swp,Dickinson:2023yes,CMS:2024twn,Bahr:2024dzg,Tiras:2024yzr,Parpillon:2024maz,Los:2024xzl,Zhu:2024ubz} \\\textit{Various accelerators have been studied for fast inference that is very important for latency-limited applications like the trigger at collider experiments.} \item \textbf{Deployment}~\cite{Kuznetsov:2020mcj,SunnebornGudnadottir:2021nhk,Holmberg:2023rfr,Savard:2023wwi,Bieringer:2024pzt,Li:2024uju} \\\textit{This category is for the deployment of machine learning interfaces, such as in the cloud.} @@ -141,15 +141,15 @@ \\\textit{A given bunch crossing at the LHC will have many nearly simultaneous proton-proton collisions. Only one of those is usually interesting and the rest introduce a source of noise (pileup) that must be mitigating for precise final state reconstruction.} \item \textbf{Calibration}~\cite{Cheong:2019upg,ATL-PHYS-PUB-2020-001,ATL-PHYS-PUB-2018-013,Hooberman:DLPS2017,Kasieczka:2020vlh,Sirunyan:2019wwa,Baldi:2020hjm,Du:2020pmp,Kieseler:2021jxc,Pollard:2021fqv,Akchurin:2021afn,Kieseler:2020wcq,Akchurin:2021ahx,Diefenthaler:2021rdj,Polson:2021kvr,Micallef:2021src,Arratia:2021tsq,Kronheim:2021hdb,Renteria-Estrada:2021zrd,Pata:2022wam,Chadeeva:2022kay,Dorigo:2022tfi,Alves:2022gnw,Qiu:2022xvr,Akchurin:2022apq,Gambhir:2022gua,Gambhir:2022dut,Valsecchi:2022rla,Leigh:2022lpn,Darulis:2022brn,Ge:2022xrv,Guglielmi:2022ftj,Aad:2023ula,Lee:2023jew,Schwenker:2023bih,Basak:2023wzq,Grosso:2023jxp,Grosso:2023ltd,Soleymaninia:2023dds,Raine:2023fko,Khozani:2023bql,ATLAS:2023tyv,ALICETPC:2023ojd,Meyer:2023ffd,Holmberg:2023rfr,Bein:2023ylt,Acosta:2023nuw,Kocot:2023izs,Zdybal:2024yzu,Hashmani:2024ykk,Britton:2024pdy} \\\textit{The goal of calibration is to remove the bias (and reduce variance if possible) from detector (or related) effects.} - \item \textbf{Recasting}~\cite{Caron:2017hku,Bertone:2016mdy,1806026,Hammad:2022wpq} + \item \textbf{Recasting}~\cite{Caron:2017hku,Bertone:2016mdy,1806026,Hammad:2022wpq,Goodsell:2024aig} \\\textit{Even though an experimental analysis may provide a single model-dependent interpretation of the result, the results are likely to have important implications for a variety of other models. Recasting is the task of taking a result and interpreting it in the context of a model that was not used for the original analysis.} \item \textbf{Matrix elements}~\cite{Badger:2020uow,Bishara:2019iwh,1804325,Bury:2020ewi,Sombillo:2021yxe,Sombillo:2021rxv,Aylett-Bullock:2021hmo,Maitre:2021uaa,Danziger:2021eeg,Winterhalder:2021ngy,Karl:2022jda,Alnuqaydan:2022ncd,Dersy:2022bym,Badger:2022hwf,Janssen:2023ahv,Maitre:2023dqz,Kaidisch:2023lwp,Heimel:2023ngj} \\\textit{Regression methods can be used as surrogate models for functions that are too slow to evaluate. One important class of functions are matrix elements, which form the core component of cross section calculations in quantum field theory.} \item \textbf{Parameter estimation}~\cite{Lei:2020ucb,1808105,Lazzarin:2020uvv,Kim:2021pcz,Alda:2021rgt,Craven:2021ems,Castro:2022zpq,Meng:2022lmd,Garg:2022tal,Qiu:2023ihi,AlHammal:2023svo,Shi:2023xfz,Goos:2023opq,Schroder:2023akt,Yang:2023rbg,Dubey:2023pro,Simkina:2023ztj} \\\textit{The target features could be parameters of a model, which can be learned directly through a regression setup. Other forms of inference are described in later sections (which could also be viewed as regression).} - \item \textbf{Parton Distribution Functions (and related)}~\cite{DelDebbio:2020rgv,Grigsby:2020auv,Rossi:2020sbh,Carrazza:2021hny,Ball:2021leu,Ball:2021xlu,Khalek:2021gon,Iranipour:2022iak,Gao:2022uhg,Gao:2022srd,Candido:2023utz,Wang:2023nab,Kassabov:2023hbm,Wang:2023poi,Fernando:2023obn,Rabemananjara:2023xfq,Kriesten:2023uoi,NNPDF:2024djq,NNPDF:2024dpb,DallOlio:2024vjv,Gombas:2024rvw,Costantini:2024xae,Bertone:2024taw,Soleymaninia:2024jam,Ochoa-Oregon:2024zgm} + \item \textbf{Parton Distribution Functions (and related)}~\cite{DelDebbio:2020rgv,Grigsby:2020auv,Rossi:2020sbh,Carrazza:2021hny,Ball:2021leu,Ball:2021xlu,Khalek:2021gon,Iranipour:2022iak,Gao:2022uhg,Gao:2022srd,Candido:2023utz,Wang:2023nab,Kassabov:2023hbm,Wang:2023poi,Fernando:2023obn,Rabemananjara:2023xfq,Kriesten:2023uoi,NNPDF:2024djq,NNPDF:2024dpb,DallOlio:2024vjv,Gombas:2024rvw,Costantini:2024xae,Bertone:2024taw,Soleymaninia:2024jam,Ochoa-Oregon:2024zgm,Barontini:2024dyb,Yan:2024yir,Liuti:2024umy,Kriesten:2024are} \\\textit{Various machine learning models can provide flexible function approximators, which can be useful for modeling functions that cannot be determined easily from first principles such as parton distribution functions.} - \item \textbf{Lattice Gauge Theory}~\cite{Kanwar:2003.06413,Favoni:2020reg,Bulusu:2021rqz,Shi:2021qri,Hackett:2021idh,Yoon:2018krb,Zhang:2019qiq,Nguyen:2019gpo,Favoni:2021epq,Chen:2021jey,Bulusu:2021njs,Shi:2022yqw,Luo:2022jzl,Chen:2022ytr,Li:2022ozl,Kang:2022jbg,Albandea:2022fky,Khan:2022vot,Sale:2022snt,Kim:2022rna,Karsch:2022yka,Favoni:2022mcg,Chen:2022asj,Bacchio:2022vje,Bacchio:2022vje,Gao:2022uhg,Aguilar:2022thg,Lawrence:2022dba,Peng:2022wdl,Lehner:2023bba,Albandea:2023wgd,Nicoli:2023qsl,Aronsson:2023rli,Zhou:2023pti,Hudspith:2023loy,R:2023dcr,Bender:2023gwr,NarcisoFerreira:2023kak,Lehner:2023prf,Singha:2023xxq,Riberdy:2023awf,Buzzicotti:2023qdv,Caselle:2023mvh,Detmold:2023kjm,Kashiwa:2023dfx,Ermann:2023unw,Albandea:2023ais,Alvestad:2023jgl,Tomiya:2023jdy,Wang:2023sry,Gao:2023uel,Soloveva:2023tvj,Holland:2023lfx,Gao:2023quv,Foreman:2023ymy,Lawrence:2023cft,Kanwar:2024ujc,Goswami:2024jlc,Holland:2024muu,Catumba:2024wxc,Chen:2024ckb,Boyle:2024nlh,Chu:2024swv,Bonanno:2024udh,Lin:2024eiz,Kim:2024rpd,Finkenrath:2024tdp,Abbott:2024knk,Bai:2024pii,Chen:2024mmd,Xu:2024tjp,Apte:2024vwn,Bachtis:2024vks} + \item \textbf{Lattice Gauge Theory}~\cite{Kanwar:2003.06413,Favoni:2020reg,Bulusu:2021rqz,Shi:2021qri,Hackett:2021idh,Yoon:2018krb,Zhang:2019qiq,Nguyen:2019gpo,Favoni:2021epq,Chen:2021jey,Bulusu:2021njs,Shi:2022yqw,Luo:2022jzl,Chen:2022ytr,Li:2022ozl,Kang:2022jbg,Albandea:2022fky,Khan:2022vot,Sale:2022snt,Kim:2022rna,Karsch:2022yka,Favoni:2022mcg,Chen:2022asj,Bacchio:2022vje,Bacchio:2022vje,Gao:2022uhg,Aguilar:2022thg,Lawrence:2022dba,Peng:2022wdl,Lehner:2023bba,Albandea:2023wgd,Nicoli:2023qsl,Aronsson:2023rli,Zhou:2023pti,Hudspith:2023loy,R:2023dcr,Bender:2023gwr,NarcisoFerreira:2023kak,Lehner:2023prf,Singha:2023xxq,Riberdy:2023awf,Buzzicotti:2023qdv,Caselle:2023mvh,Detmold:2023kjm,Kashiwa:2023dfx,Ermann:2023unw,Albandea:2023ais,Alvestad:2023jgl,Tomiya:2023jdy,Wang:2023sry,Gao:2023uel,Soloveva:2023tvj,Holland:2023lfx,Gao:2023quv,Foreman:2023ymy,Lawrence:2023cft,Kanwar:2024ujc,Goswami:2024jlc,Holland:2024muu,Catumba:2024wxc,Chen:2024ckb,Boyle:2024nlh,Chu:2024swv,Bonanno:2024udh,Lin:2024eiz,Kim:2024rpd,Finkenrath:2024tdp,Abbott:2024knk,Bai:2024pii,Chen:2024mmd,Xu:2024tjp,Apte:2024vwn,Bachtis:2024vks,Cai:2024eqa,Jiang:2024vsr,Bachtis:2024dss,Gao:2024nzg} \\\textit{Lattice methods offer a complementary approach to perturbation theory. A key challenge is to create approaches that respect the local gauge symmetry (equivariant networks).} \item \textbf{Function Approximation}~\cite{1853982,Coccaro:2019lgs,Haddadin:2021mmo,Chahrour:2021eiv,Wang:2021jou,Kitouni:2021fkh,Lei:2022dvn,Wang:2023nab,Fernando:2023obn,Reyes-Gonzalez:2023oei,Hirst:2024abn} \\\textit{Approximating functions that obey certain (physical) constraints.} @@ -158,22 +158,22 @@ \item \textbf{Monitoring}~\cite{Mukund:2023oyy,Matha:2023tmf,CMSMuon:2023czf,Joshi:2023btt,Chen:2023cim,Harilal:2023smf,Das:2023ktd,CMSECAL:2023fvz,Shutt:2024che,Cushman:2024jgi} \\\textit{Regression models can be used to monitor experimental setups and sensors.} \end{itemize} -\item \textbf{Equivariant networks}~\cite{Kanwar:2003.06413,Dolan:2020qkr,Favoni:2020reg,Bulusu:2021njs,Gong:2022lye,Shi:2022yqw,Bogatskiy:2022hub,Favoni:2022mcg,Bogatskiy:2022czk,Hao:2022zns,Lehner:2023bba,Forestano:2023fpj,Aronsson:2023rli,Buhmann:2023pmh,Forestano:2023qcy,Lehner:2023prf,Murnane:2023kfm,Bogatskiy:2023nnw,Bright-Thonney:2023gdl,Gu:2024lrz,Bressler:2024wzc,Chatterjee:2024pbp,Bhardwaj:2024djv,Sahu:2024sts,Bhardwaj:2024wrf,Spinner:2024hjm,Cruz:2024grk} +\item \textbf{Equivariant networks}~\cite{Kanwar:2003.06413,Dolan:2020qkr,Favoni:2020reg,Bulusu:2021njs,Gong:2022lye,Shi:2022yqw,Bogatskiy:2022hub,Favoni:2022mcg,Bogatskiy:2022czk,Hao:2022zns,Lehner:2023bba,Forestano:2023fpj,Aronsson:2023rli,Buhmann:2023pmh,Forestano:2023qcy,Lehner:2023prf,Murnane:2023kfm,Bogatskiy:2023nnw,Bright-Thonney:2023gdl,Gu:2024lrz,Bressler:2024wzc,Chatterjee:2024pbp,Bhardwaj:2024djv,Sahu:2024sts,Bhardwaj:2024wrf,Spinner:2024hjm,Cruz:2024grk,Hendi:2024yin} \\\textit{It is often the case that implementing equivariance or learning symmetries with a model better describes the physics and improves performance} \item \textbf{Decorrelation methods}~\cite{Louppe:2016ylz,Dolen:2016kst,Moult:2017okx,Stevens:2013dya,Shimmin:2017mfk,Bradshaw:2019ipy,ATL-PHYS-PUB-2018-014,DiscoFever,Xia:2018kgd,Englert:2018cfo,Wunsch:2019qbo,Rogozhnikov:2014zea,10.1088/2632-2153/ab9023,clavijo2020adversarial,Kasieczka:2020pil,Kitouni:2020xgb,Ghosh:2021hrh,Dolan:2021pml,Mikuni:2021nwn,Klein:2022hdv,Das:2022cjl,Rabusov:2022woa,Algren:2023spv} \\\textit{It it sometimes the case that a classification or regression model needs to be independent of a set of features (usually a mass-like variable) in order to estimate the background or otherwise reduce the uncertainty. These techniques are related to what the machine learning literature calls model `fairness'.} \item \textbf{Generative models / density estimation} \\\textit{The goal of generative modeling is to learn (explicitly or implicitly) a probability density $p(x)$ for the features $x\in\mathbb{R}^n$. This task is usually unsupervised (no labels).} \begin{itemize} - \item \textbf{GANs}~\cite{deOliveira:2017pjk,Paganini:2017hrr,Paganini:2017dwg,Alonso-Monsalve:2018aqs,Butter:2019eyo,Martinez:2019jlu,Bellagente:2019uyp,Vallecorsa:2019ked,SHiP:2019gcl,Carrazza:2019cnt,Butter:2019cae,Lin:2019htn,DiSipio:2019imz,Hashemi:2019fkn,Chekalina:2018hxi,ATL-SOFT-PUB-2018-001,Zhou:2018ill,Carminati:2018khv,Vallecorsa:2018zco,Datta:2018mwd,Musella:2018rdi,Erdmann:2018kuh,Deja:2019vcv,Derkach:2019qfk,Erbin:2018csv,Erdmann:2018jxd,Urban:2018tqv,Oliveira:DLPS2017,deOliveira:2017rwa,Farrell:2019fsm,Hooberman:DLPS2017,Belayneh:2019vyx,Wang:2020tap,buhmann2020getting,Alanazi:2020jod,2009.03796,2008.06545,Kansal:2020svm,Maevskiy:2020ank,Lai:2020byl,Choi:2021sku,Rehm:2021zow,Rehm:2021zoz,Carrazza:2021hny,Rehm:2021qwm,Lebese:2021foi,Winterhalder:2021ave,Kansal:2021cqp,NEURIPS2020_a878dbeb,Khattak:2021ndw,Mu:2021nno,Li:2021cbp,Bravo-Prieto:2021ehz,Anderlini:2021qpm,Chisholm:2021pdn,Desai:2021wbb,Buhmann:2021caf,Bieringer:2022cbs,Ghosh:2022zdz,Anderlini:2022ckd,Ratnikov:2022hge,Rogachev:2022hjg,ATLAS:2022jhk,Anderlini:2022hgm,Buhmann:2023pmh,Yue:2023uva,Hashemi:2023ruu,EXO:2023pkl,Diefenbacher:2023prl,Chan:2023ume,Dubinski:2023fsy,Alghamdi:2023emm,Barbetti:2023bvi,Erdmann:2023ngr,FaucciGiannelli:2023fow,Scham:2023cwn,Scham:2023usu,Chan:2023icm,Dooney:2024pvt} + \item \textbf{GANs}~\cite{deOliveira:2017pjk,Paganini:2017hrr,Paganini:2017dwg,Alonso-Monsalve:2018aqs,Butter:2019eyo,Martinez:2019jlu,Bellagente:2019uyp,Vallecorsa:2019ked,SHiP:2019gcl,Carrazza:2019cnt,Butter:2019cae,Lin:2019htn,DiSipio:2019imz,Hashemi:2019fkn,Chekalina:2018hxi,ATL-SOFT-PUB-2018-001,Zhou:2018ill,Carminati:2018khv,Vallecorsa:2018zco,Datta:2018mwd,Musella:2018rdi,Erdmann:2018kuh,Deja:2019vcv,Derkach:2019qfk,Erbin:2018csv,Erdmann:2018jxd,Urban:2018tqv,Oliveira:DLPS2017,deOliveira:2017rwa,Farrell:2019fsm,Hooberman:DLPS2017,Belayneh:2019vyx,Wang:2020tap,buhmann2020getting,Alanazi:2020jod,2009.03796,2008.06545,Kansal:2020svm,Maevskiy:2020ank,Lai:2020byl,Choi:2021sku,Rehm:2021zow,Rehm:2021zoz,Carrazza:2021hny,Rehm:2021qwm,Lebese:2021foi,Winterhalder:2021ave,Kansal:2021cqp,NEURIPS2020_a878dbeb,Khattak:2021ndw,Mu:2021nno,Li:2021cbp,Bravo-Prieto:2021ehz,Anderlini:2021qpm,Chisholm:2021pdn,Desai:2021wbb,Buhmann:2021caf,Bieringer:2022cbs,Ghosh:2022zdz,Anderlini:2022ckd,Ratnikov:2022hge,Rogachev:2022hjg,ATLAS:2022jhk,Anderlini:2022hgm,Buhmann:2023pmh,Yue:2023uva,Hashemi:2023ruu,EXO:2023pkl,Diefenbacher:2023prl,Chan:2023ume,Dubinski:2023fsy,Alghamdi:2023emm,Barbetti:2023bvi,Erdmann:2023ngr,FaucciGiannelli:2023fow,Scham:2023cwn,Scham:2023usu,Chan:2023icm,Dooney:2024pvt,Wojnar:2024cbn} \\\textit{Generative Adversarial Networks~\cite{Goodfellow:2014upx} learn $p(x)$ implicitly through the minimax optimization of two networks: one that maps noise to structure $G(z)$ and one a classifier (called the discriminator) that learns to distinguish examples generated from $G(z)$ and those generated from the target process. When the discriminator is maximally `confused', then the generator is effectively mimicking $p(x)$.} \item \textbf{(Variational) Autoencoders}~\cite{Monk:2018zsb,ATL-SOFT-PUB-2018-001,Cheng:2020dal,1816035,Howard:2021pos,Buhmann:2021lxj,Bortolato:2021zic,deja2020endtoend,Hariri:2021clz,Fanelli:2019qaq,Collins:2021pld,Orzari:2021suh,Jawahar:2021vyu,Tsan:2021brw,Buhmann:2021caf,Touranakou:2022qrp,Ilten:2022jfm,Collins:2022qpr,AbhishekAbhishek:2022wby,Cresswell:2022tof,Roche:2023int,Anzalone:2023ugq,Lasseri:2023dhi,Chekanov:2023uot,Zhang:2023khv,Hoque:2023zjt,Kuh:2024lgx,Liu:2024kvv} \\\textit{An autoencoder consists of two functions: one that maps $x$ into a latent space $z$ (encoder) and a second one that maps the latent space back into the original space (decoder). The encoder and decoder are simultaneously trained so that their composition is nearly the identity. When the latent space has a well-defined probability density (as in variational autoencoders), then one can sample from the autoencoder by applying the detector to a randomly chosen element of the latent space.} - \item \textbf{Normalizing flows}~\cite{Albergo:2019eim,1800956,Kanwar:2003.06413,Brehmer:2020vwc,Bothmann:2020ywa,Gao:2020zvv,Gao:2020vdv,Nachman:2020lpy,Choi:2020bnf,Lu:2020npg,Bieringer:2020tnw,Hollingsworth:2021sii,Winterhalder:2021ave,Krause:2021ilc,Hackett:2021idh,Menary:2021tjg,Hallin:2021wme,NEURIPS2020_a878dbeb,Vandegar:2020yvw,Jawahar:2021vyu,Bister:2021arb,Krause:2021wez,Butter:2021csz,Winterhalder:2021ngy,Butter:2022lkf,Verheyen:2022tov,Leigh:2022lpn,Chen:2022ytr,Albandea:2022fky,Krause:2022jna,Cresswell:2022tof,Kach:2022qnf,Kach:2022uzq,Dolan:2022ikg,Backes:2022vmn,Heimel:2022wyj,Albandea:2023wgd,Rousselot:2023pcj,Diefenbacher:2023vsw,Nicoli:2023qsl,R:2023dcr,Nachman:2023clf,Raine:2023fko,Golling:2023yjq,Wen:2023oju,Xu:2023xdc,Singha:2023xxq,Buckley:2023rez,Pang:2023wfx,Golling:2023mqx,Reyes-Gonzalez:2023oei,Bickendorf:2023nej,Finke:2023ltw,Bright-Thonney:2023sqf,Albandea:2023ais,Pham:2023bnl,Gavranovic:2023oam,Heimel:2023ngj,Bierlich:2023zzd,ElBaz:2023ijr,Ernst:2023qvn,Krause:2023uww,Kanwar:2024ujc,Deutschmann:2024lml,Kelleher:2024rmb,Kelleher:2024jsh,Schnake:2024mip,Daumann:2024kfd,Abbott:2024knk,Bai:2024pii,Du:2024gbp,Favaro:2024rle} + \item \textbf{(Continuous) Normalizing flows}~\cite{Albergo:2019eim,1800956,Kanwar:2003.06413,Brehmer:2020vwc,Bothmann:2020ywa,Gao:2020zvv,Gao:2020vdv,Nachman:2020lpy,Choi:2020bnf,Lu:2020npg,Bieringer:2020tnw,Hollingsworth:2021sii,Winterhalder:2021ave,Krause:2021ilc,Hackett:2021idh,Menary:2021tjg,Hallin:2021wme,NEURIPS2020_a878dbeb,Vandegar:2020yvw,Jawahar:2021vyu,Bister:2021arb,Krause:2021wez,Butter:2021csz,Winterhalder:2021ngy,Butter:2022lkf,Verheyen:2022tov,Leigh:2022lpn,Chen:2022ytr,Albandea:2022fky,Krause:2022jna,Cresswell:2022tof,Kach:2022qnf,Kach:2022uzq,Dolan:2022ikg,Backes:2022vmn,Heimel:2022wyj,Albandea:2023wgd,Rousselot:2023pcj,Diefenbacher:2023vsw,Nicoli:2023qsl,R:2023dcr,Nachman:2023clf,Raine:2023fko,Golling:2023yjq,Wen:2023oju,Xu:2023xdc,Singha:2023xxq,Buckley:2023rez,Pang:2023wfx,Golling:2023mqx,Reyes-Gonzalez:2023oei,Bickendorf:2023nej,Finke:2023ltw,Bright-Thonney:2023sqf,Albandea:2023ais,Pham:2023bnl,Gavranovic:2023oam,Heimel:2023ngj,Bierlich:2023zzd,ElBaz:2023ijr,Ernst:2023qvn,Krause:2023uww,Kanwar:2024ujc,Deutschmann:2024lml,Kelleher:2024rmb,Kelleher:2024jsh,Schnake:2024mip,Daumann:2024kfd,Abbott:2024knk,Bai:2024pii,Du:2024gbp,Favaro:2024rle,Buss:2024orz,Dreyer:2024bhs,Quetant:2024ftg} \\\textit{Normalizing flows~\cite{pmlr-v37-rezende15} learn $p(x)$ explicitly by starting with a simple probability density and then applying a series of bijective transformations with tractable Jacobians.} - \item \textbf{Diffusion Models}~\cite{Mikuni:2022xry,Leigh:2023toe,Mikuni:2023dvk,Shmakov:2023kjj,Buhmann:2023bwk,Butter:2023fov,Mikuni:2023tok,Acosta:2023zik,Leigh:2023zle,Imani:2023blb,Amram:2023onf,Diefenbacher:2023flw,Cotler:2023lem,Diefenbacher:2023wec,Mikuni:2023tqg,Hunt-Smith:2023ccp,Buhmann:2023kdg,Buhmann:2023zgc,Buhmann:2023acn,Devlin:2023jzp,Heimel:2023ngj,Wang:2023sry,Butter:2023ira,Sengupta:2023vtm,Jiang:2024ohg,Kobylianskii:2024ijw,Vaselli:2024vrx,Jiang:2024bwr,Kobylianskii:2024sup,Favaro:2024rle} + \item \textbf{Diffusion Models}~\cite{Mikuni:2022xry,Leigh:2023toe,Mikuni:2023dvk,Shmakov:2023kjj,Buhmann:2023bwk,Butter:2023fov,Mikuni:2023tok,Acosta:2023zik,Leigh:2023zle,Imani:2023blb,Amram:2023onf,Diefenbacher:2023flw,Cotler:2023lem,Diefenbacher:2023wec,Mikuni:2023tqg,Hunt-Smith:2023ccp,Buhmann:2023kdg,Buhmann:2023zgc,Buhmann:2023acn,Devlin:2023jzp,Heimel:2023ngj,Wang:2023sry,Butter:2023ira,Sengupta:2023vtm,Jiang:2024ohg,Kobylianskii:2024ijw,Vaselli:2024vrx,Jiang:2024bwr,Kobylianskii:2024sup,Favaro:2024rle,Kita:2024nnw,Quetant:2024ftg,Wojnar:2024cbn} \\\textit{These approaches learn the gradient of the density instead of the density directly.} - \item \textbf{Transformer Models}~\cite{Finke:2023veq,Butter:2023fov,Raine:2023fko,Tomiya:2023jdy,Li:2023xhj,Paeng:2024ary,Spinner:2024hjm} + \item \textbf{Transformer Models}~\cite{Finke:2023veq,Butter:2023fov,Raine:2023fko,Tomiya:2023jdy,Li:2023xhj,Paeng:2024ary,Spinner:2024hjm,Quetant:2024ftg} \\\textit{These approaches learn the density or perform generative modeling using transformer-based networks.} \item \textbf{Physics-inspired}~\cite{Andreassen:2018apy,Andreassen:2019txo,1808876,Lai:2020byl,Barenboim:2021vzh,Larkoski:2023xam} \\\textit{A variety of methods have been proposed to use machine learning tools (e.g. neural networks) combined with physical components.} @@ -186,7 +186,7 @@ \item \textbf{Other/hybrid}~\cite{Cresswell:2022tof,DiBello:2022rss,Li:2022jon,Kansal:2022spb,Butter:2023fov,Kronheim:2023jrl,Santos:2023mib,Sahu:2023uwb} \\\textit{Architectures that combine different network elements or otherwise do not fit into the other categories.} \end{itemize} -\item \textbf{Anomaly detection}~\cite{DAgnolo:2018cun,Collins:2018epr,Collins:2019jip,DAgnolo:2019vbw,Farina:2018fyg,Heimel:2018mkt,Roy:2019jae,Cerri:2018anq,Blance:2019ibf,Hajer:2018kqm,DeSimone:2018efk,Mullin:2019mmh,1809.02977,Dillon:2019cqt,Andreassen:2020nkr,Nachman:2020lpy,Aguilar-Saavedra:2017rzt,Romao:2019dvs,Romao:2020ojy,knapp2020adversarially,collaboration2020dijet,1797846,1800445,Amram:2020ykb,Cheng:2020dal,Khosa:2020qrz,Thaprasop:2020mzp,Alexander:2020mbx,aguilarsaavedra2020mass,1815227,pol2020anomaly,Mikuni:2020qds,vanBeekveld:2020txa,Park:2020pak,Faroughy:2020gas,Stein:2020rou,Kasieczka:2021xcg,Chakravarti:2021svb,Batson:2021agz,Blance:2021gcs,Bortolato:2021zic,Collins:2021nxn,Dillon:2021nxw,Finke:2021sdf,Shih:2021kbt,Atkinson:2021nlt,Kahn:2021drv,Aarrestad:2021oeb,Dorigo:2021iyy,Caron:2021wmq,Govorkova:2021hqu,Kasieczka:2021tew,Volkovich:2021txe,Govorkova:2021utb,Hallin:2021wme,Ostdiek:2021bem,Fraser:2021lxm,Jawahar:2021vyu,Herrero-Garcia:2021goa,Aguilar-Saavedra:2021utu,Tombs:2021wae,Lester:2021aks,Mikuni:2021nwn,Chekanov:2021pus,dAgnolo:2021aun,Canelli:2021aps,Ngairangbam:2021yma,Bradshaw:2022qev,Aguilar-Saavedra:2022ejy,Buss:2022lxw,Alvi:2022fkk,Jiang:2022sfw,Dillon:2022tmm,Birman:2022xzu,Raine:2022hht,Letizia:2022xbe,Fanelli:2022xwl,Finke:2022lsu,Verheyen:2022tov,Dillon:2022mkq,Caron:2022wrw,Park:2022zov,Kamenik:2022qxs,Hallin:2022eoq,Kasieczka:2022naq,Araz:2022zxk,Mastandrea:2022vas,Schuhmacher:2023pro,Roche:2023int,Golling:2023juz,Sengupta:2023xqy,Mikuni:2023tok,Golling:2023yjq,Vaslin:2023lig,ATLAS:2023azi,Chekanov:2023uot,CMSECAL:2023fvz,Bickendorf:2023nej,Finke:2023ltw,Buhmann:2023acn,Freytsis:2023cjr,Grosso:2023owo,Bai:2023yyy,Zhang:2023khv,Liu:2023djx,Metodiev:2023izu,Zipper:2023ybp,Sengupta:2023vtm,Krause:2023uww,Cheng:2024yig,Li:2024htp} +\item \textbf{Anomaly detection}~\cite{DAgnolo:2018cun,Collins:2018epr,Collins:2019jip,DAgnolo:2019vbw,Farina:2018fyg,Heimel:2018mkt,Roy:2019jae,Cerri:2018anq,Blance:2019ibf,Hajer:2018kqm,DeSimone:2018efk,Mullin:2019mmh,1809.02977,Dillon:2019cqt,Andreassen:2020nkr,Nachman:2020lpy,Aguilar-Saavedra:2017rzt,Romao:2019dvs,Romao:2020ojy,knapp2020adversarially,collaboration2020dijet,1797846,1800445,Amram:2020ykb,Cheng:2020dal,Khosa:2020qrz,Thaprasop:2020mzp,Alexander:2020mbx,aguilarsaavedra2020mass,1815227,pol2020anomaly,Mikuni:2020qds,vanBeekveld:2020txa,Park:2020pak,Faroughy:2020gas,Stein:2020rou,Kasieczka:2021xcg,Chakravarti:2021svb,Batson:2021agz,Blance:2021gcs,Bortolato:2021zic,Collins:2021nxn,Dillon:2021nxw,Finke:2021sdf,Shih:2021kbt,Atkinson:2021nlt,Kahn:2021drv,Aarrestad:2021oeb,Dorigo:2021iyy,Caron:2021wmq,Govorkova:2021hqu,Kasieczka:2021tew,Volkovich:2021txe,Govorkova:2021utb,Hallin:2021wme,Ostdiek:2021bem,Fraser:2021lxm,Jawahar:2021vyu,Herrero-Garcia:2021goa,Aguilar-Saavedra:2021utu,Tombs:2021wae,Lester:2021aks,Mikuni:2021nwn,Chekanov:2021pus,dAgnolo:2021aun,Canelli:2021aps,Ngairangbam:2021yma,Bradshaw:2022qev,Aguilar-Saavedra:2022ejy,Buss:2022lxw,Alvi:2022fkk,Jiang:2022sfw,Dillon:2022tmm,Birman:2022xzu,Raine:2022hht,Letizia:2022xbe,Fanelli:2022xwl,Finke:2022lsu,Verheyen:2022tov,Dillon:2022mkq,Caron:2022wrw,Park:2022zov,Kamenik:2022qxs,Hallin:2022eoq,Kasieczka:2022naq,Araz:2022zxk,Mastandrea:2022vas,Schuhmacher:2023pro,Roche:2023int,Golling:2023juz,Sengupta:2023xqy,Mikuni:2023tok,Golling:2023yjq,Vaslin:2023lig,ATLAS:2023azi,Chekanov:2023uot,CMSECAL:2023fvz,Bickendorf:2023nej,Finke:2023ltw,Buhmann:2023acn,Freytsis:2023cjr,Grosso:2023owo,Bai:2023yyy,Zhang:2023khv,Liu:2023djx,Metodiev:2023izu,Zipper:2023ybp,Sengupta:2023vtm,Krause:2023uww,Cheng:2024yig,Li:2024htp,Grosso:2024nho,Leigh:2024chm,Harilal:2024tqq} \\\textit{The goal of anomaly detection is to identify abnormal events. The abnormal events could be from physics beyond the Standard Model or from faults in a detector. While nearly all searches for new physics are technically anomaly detection, this category is for methods that are mode-independent (broadly defined). Anomalies in high energy physics tend to manifest as over-densities in phase space (often called `population anomalies') in contrast to off-manifold anomalies where you can flag individual examples as anomalous. } \item \textbf{Foundation Models, LLMs}~\cite{Vigl:2024lat,Birk:2024knn,Harris:2024sra,Fanelli:2024ktq,Zhang:2024kws,Mikuni:2024qsr} \\\textit{A foundation model is a machine learning or deep learning model that is trained on broad data such that it can be applied across a wide range of use cases.} @@ -195,11 +195,11 @@ \begin{itemize} \item \textbf{Parameter estimation}~\cite{Andreassen:2019nnm,Stoye:2018ovl,Hollingsworth:2020kjg,Brehmer:2018kdj,Brehmer:2018eca,Brehmer:2019xox,Brehmer:2018hga,Cranmer:2015bka,Andreassen:2020gtw,Coogan:2020yux,Flesher:2020kuy,Bieringer:2020tnw,Nachman:2021yvi,Chatterjee:2021nms,NEURIPS2020_a878dbeb,Mishra-Sharma:2021oxe,Barman:2021yfh,Bahl:2021dnc,Arganda:2022qzy,Kong:2022rnd,Arganda:2022zbs,Butter:2022vkj,Neubauer:2022gbu,Rizvi:2023mws,Heinrich:2023bmt,Breitenmoser:2023tmi,Erdogan:2023uws,Morandini:2023pwj,Barrue:2023ysk,Espejo:2023wzf,Heimel:2023mvw,Chai:2024zyl,Chatterjee:2024pbp,Alvarez:2024owq,Diaz:2024yfu,Mastandrea:2024irf} \\\textit{This can also be viewed as a regression problem, but there the goal is typically to do maximum likelihood estimation in contrast to directly minimizing the mean squared error between a function and the target.} - \item \textbf{Unfolding}~\cite{Mieskolainen:2018fhf,Andreassen:2019cjw,Datta:2018mwd,Bellagente:2019uyp,Gagunashvili:2010zw,Glazov:2017vni,Martschei:2012pr,Lindemann:1995ut,Zech2003BinningFreeUB,1800956,Vandegar:2020yvw,Howard:2021pos,Baron:2021vvl,Andreassen:2021zzk,Komiske:2021vym,H1:2021wkz,Arratia:2021otl,Wong:2021zvv,Arratia:2022wny,Backes:2022vmn,Chan:2023tbf,Shmakov:2023kjj,Shmakov:2024gkd,Huetsch:2024quz} + \item \textbf{Unfolding}~\cite{Mieskolainen:2018fhf,Andreassen:2019cjw,Datta:2018mwd,Bellagente:2019uyp,Gagunashvili:2010zw,Glazov:2017vni,Martschei:2012pr,Lindemann:1995ut,Zech2003BinningFreeUB,1800956,Vandegar:2020yvw,Howard:2021pos,Baron:2021vvl,Andreassen:2021zzk,Komiske:2021vym,H1:2021wkz,Arratia:2021otl,Wong:2021zvv,Arratia:2022wny,Backes:2022vmn,Chan:2023tbf,Shmakov:2023kjj,Shmakov:2024gkd,Huetsch:2024quz,Desai:2024kpd} \\\textit{This is the task of removing detector distortions. In contrast to parameter estimation, the goal is not to infer model parameters, but instead, the undistorted phase space probability density. This is often also called deconvolution.} \item \textbf{Domain adaptation}~\cite{Rogozhnikov:2016bdp,Andreassen:2019nnm,Cranmer:2015bka,2009.03796,Nachman:2021opi,Camaiani:2022kul,Schreck:2023pzs,Algren:2023qnb,Zhao:2024ely,Kelleher:2024rmb,Kelleher:2024jsh} \\\textit{Morphing simulations to look like data is a form of domain adaptation.} - \item \textbf{BSM}~\cite{Andreassen:2020nkr,Hollingsworth:2020kjg,Brehmer:2018kdj,Brehmer:2018eca,Brehmer:2018hga,Brehmer:2019xox,Romao:2020ojy,deSouza:2022uhk,GomezAmbrosio:2022mpm,Castro:2022zpq,Anisha:2023xmh,Dennis:2023kfe,vanBeekveld:2023ney,Chhibra:2023tyf,Mandal:2023mck,Franz:2023gic,Arganda:2023qni,Barman:2024xlc,vanBeekveld:2024cby,Bhattacharya:2024sxl,Catena:2024fjn,Ahmed:2024oxg,Baruah:2024gwy,Choudhury:2024mox,Ahmed:2024uaz} + \item \textbf{BSM}~\cite{Andreassen:2020nkr,Hollingsworth:2020kjg,Brehmer:2018kdj,Brehmer:2018eca,Brehmer:2018hga,Brehmer:2019xox,Romao:2020ojy,deSouza:2022uhk,GomezAmbrosio:2022mpm,Castro:2022zpq,Anisha:2023xmh,Dennis:2023kfe,vanBeekveld:2023ney,Chhibra:2023tyf,Mandal:2023mck,Franz:2023gic,Arganda:2023qni,Barman:2024xlc,vanBeekveld:2024cby,Bhattacharya:2024sxl,Catena:2024fjn,Ahmed:2024oxg,Baruah:2024gwy,Choudhury:2024mox,Ahmed:2024uaz,Hammad:2024hhm,Schofbeck:2024zjo} \\\textit{This category is for parameter estimation when the parameter is the signal strength of new physics.} \item \textbf{Differentiable Simulation}~\cite{Heinrich:2022xfa,MODE:2022znx,Nachman:2022jbj,Lei:2022dvn,Napolitano:2023jhg,Shenoy:2023ros,Kagan:2023gxz,Aehle:2023wwi,Smith:2023ssh,BarhamAlzas:2024ggt} \\\textit{Coding up a simulation using a differentiable programming language like TensorFlow, PyTorch, or JAX.} @@ -207,9 +207,9 @@ \item \textbf{Uncertainty Quantification} \\\textit{Estimating and mitigating uncertainty is essential for the successful deployment of machine learning methods in high energy physics. } \begin{itemize} - \item \textbf{Interpretability}~\cite{deOliveira:2015xxd,Chang:2017kvc,Diefenbacher:2019ezd,Agarwal:2020fpt,Grojean:2020ech,Romero:2021qlf,Collins:2021pld,Mokhtar:2021bkf,Bradshaw:2022qev,Anzalone:2022hrt,Grojean:2022mef,Khot:2022aky,Roy:2022gge,Mengel:2023mnw,Ngairangbam:2023cps,Wilkinson:2024xva} + \item \textbf{Interpretability}~\cite{deOliveira:2015xxd,Chang:2017kvc,Diefenbacher:2019ezd,Agarwal:2020fpt,Grojean:2020ech,Romero:2021qlf,Collins:2021pld,Mokhtar:2021bkf,Bradshaw:2022qev,Anzalone:2022hrt,Grojean:2022mef,Khot:2022aky,Roy:2022gge,Mengel:2023mnw,Ngairangbam:2023cps,Wilkinson:2024xva,Gavrikov:2024rso,Kriesten:2024are} \\\textit{Machine learning methods that are interpretable maybe more robust and thus less susceptible to various sources of uncertainty.} - \item \textbf{Estimation}~\cite{Nachman:2019dol,Nachman:2019yfl,Barnard:2016qma,Bellagente:2021yyh,Cheung:2022dil,Koh:2023wst,Golutvin:2023fle,Dickinson:2023yes} + \item \textbf{Estimation}~\cite{Nachman:2019dol,Nachman:2019yfl,Barnard:2016qma,Bellagente:2021yyh,Cheung:2022dil,Koh:2023wst,Golutvin:2023fle,Dickinson:2023yes,Bieringer:2024nbc} \\\textit{A first step in reducing uncertainties is estimating their size.} \item \textbf{Mitigation}~\cite{Estrade:DLPS2017,Englert:2018cfo,Louppe:2016ylz,Araz:2021wqm,Stein:2022nvf} \\\textit{This category is for proposals to reduce uncertainty.} @@ -219,14 +219,14 @@ \item \textbf{Formal Theory and ML} \\\textit{ML can also be utilized in formal theory.} \begin{itemize} - \item Theory and physics for ML~\cite{Erbin:2022lls,Zuniga-Galindo:2023hty,Banta:2023kqe,Zuniga-Galindo:2023uwp,Kumar:2023hlu,Demirtas:2023fir,Halverson:2023ndu} + \item Theory and physics for ML~\cite{Erbin:2022lls,Zuniga-Galindo:2023hty,Banta:2023kqe,Zuniga-Galindo:2023uwp,Kumar:2023hlu,Demirtas:2023fir,Halverson:2023ndu,Zhang:2024mcu} \item ML for theory~\cite{Berglund:2022gvm,Erbin:2022rgx,Gerdes:2022nzr,Escalante-Notario:2022fik,Chen:2022jwd,Cheung:2022itk,He:2023csq,Lal:2023dkj,Dorrill:2023vox,Forestano:2023ijh,Dersy:2023job,Cotler:2023lem,Mizera:2023bsw,Gnech:2023prs,Seong:2023njx,Wojcik:2023usm,Alawadhi:2023gxa,Choi:2023rqg,Halverson:2023ndu,Matchev:2023mii,Lanza:2023vee,Erbin:2023ncy,Hirst:2023kdl,Ishiguro:2023hcv,Constantin:2024yxh,Berman:2024pax,Gukov:2024buj,Lanza:2024mqp,Hashimoto:2024aga,Orman:2024mpw,Bea:2024xgv,Balduf:2024gvv,Hou:2024vtx,Keita:2024skh,LopesCardoso:2024tol,Gukov:2024opc,Dao:2024zab} \end{itemize} \item \textbf{Experimental results} \\\textit{This section is incomplete as there are many results that directly and indirectly (e.g. via flavor tagging) use modern machine learning techniques. We will try to highlight experimental results that use deep learning in a critical way for the final analysis sensitivity.} \begin{itemize} - \item Performance studies~\cite{CMS:2022prd,Yang:2022dwu,NEOS-II:2022mov,Jiang:2022zho,Gronroos:2023qff,ATLAS:2023zca,Palo:2023xnr,Karwowska:2024xqy} - \item Searches and measurements where ML reconstruction is a core component~\cite{Keck:2018lcd,CMS:2019dqq,MicroBooNE:2021nxr,MicroBooNE:2021jwr,ATLAS:2022ihe,CMS:2022idi,CMS:2022fxs,Li:2022gpb,Tran:2022ago,Manganelli:2022whv,CMS:2022wjc,ATLAS:2023mcc,ATLAS:2023hbp,ATLAS:2023vxg,ATLAS:2023qdu,ATLAS:2023bzb,ATLAS:2023sbu,ATLAS:2023dnm,NOvA:2023uxq,Gravili:2023hbp,Dutta:2023jbz,Belfkir:2023vpo,Tung:2023lkv,Akar:2023puf,BOREXINO:2023pcv,Vourliotis:2024bem,ATLAS:2024rcx,CMS:2024trg,ATLAS:2024ett,ATLAS:2024fdw,CMS:2024fkb,ATLAS:2024auw,CMS:2024vjn,ATLAS:2024itc,Belle-II:2024vvr,CMS:2024zqs} + \item Performance studies~\cite{CMS:2022prd,Yang:2022dwu,NEOS-II:2022mov,Jiang:2022zho,Gronroos:2023qff,ATLAS:2023zca,Palo:2023xnr,Karwowska:2024xqy,Kara:2024xkk} + \item Searches and measurements where ML reconstruction is a core component~\cite{Keck:2018lcd,CMS:2019dqq,MicroBooNE:2021nxr,MicroBooNE:2021jwr,ATLAS:2022ihe,CMS:2022idi,CMS:2022fxs,Li:2022gpb,Tran:2022ago,Manganelli:2022whv,CMS:2022wjc,ATLAS:2023mcc,ATLAS:2023hbp,ATLAS:2023vxg,ATLAS:2023qdu,ATLAS:2023bzb,ATLAS:2023sbu,ATLAS:2023dnm,NOvA:2023uxq,Gravili:2023hbp,Dutta:2023jbz,Belfkir:2023vpo,Tung:2023lkv,Akar:2023puf,BOREXINO:2023pcv,Vourliotis:2024bem,ATLAS:2024rcx,CMS:2024trg,ATLAS:2024ett,ATLAS:2024fdw,CMS:2024fkb,ATLAS:2024auw,CMS:2024vjn,ATLAS:2024itc,Belle-II:2024vvr,CMS:2024zqs,ATLAS:2024xxl,MicroBooNE:2024zhz,CALICE:2024jke,CMS:2024ddc,ATLAS:2024rua} \item Final analysis discriminate for searches~\cite{Aad:2019yxi,Aad:2020hzm,collaboration2020dijet,Sirunyan:2020hwz,Manganelli:2022whv}. \item Measurements using deep learning directly (not through object reconstruction)~\cite{H1:2021wkz,H1:2023fzk} \end{itemize} diff --git a/README.md b/README.md index 1659c5c..8389a9b 100644 --- a/README.md +++ b/README.md @@ -68,6 +68,10 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [Unsupervised and lightly supervised learning in particle physics](https://arxiv.org/abs/2403.13676) * [Machine Learning in High Energy Physics: A review of heavy-flavor jet tagging at the LHC](https://arxiv.org/abs/2404.01071) * [The Landscape of Unfolding with Machine Learning](https://arxiv.org/abs/2404.18807) +* [A Comprehensive Evaluation of Generative Models in Calorimeter Shower Simulation](https://arxiv.org/abs/2406.12898) +* [Top-philic Machine Learning](https://arxiv.org/abs/2407.00183) [[DOI](https://doi.org/10.1140/epjs/s11734-024-01237-9)] +* [QCD Masterclass Lectures on Jet Physics and Machine Learning](https://arxiv.org/abs/2407.04897) +* [TASI Lectures on Physics for Machine Learning](https://arxiv.org/abs/2408.00082) ### Classical papers @@ -224,6 +228,10 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [A case study of sending graph neural networks back to the test bench for applications in high-energy particle physics](https://arxiv.org/abs/2402.17386) * [NuGraph2: A Graph Neural Network for Neutrino Physics Event Reconstruction](https://arxiv.org/abs/2403.11872) * [Advancing Set-Conditional Set Generation: Graph Diffusion for Fast Simulation of Reconstructed Particles](https://arxiv.org/abs/2405.10106) +* [Using graph neural networks to reconstruct charged pion showers in the CMS High Granularity Calorimeter](https://arxiv.org/abs/2406.11937) +* [Accelerating Graph-based Tracking Tasks with Symbolic Regression](https://arxiv.org/abs/2406.16752) +* [Graph Neural Network-Based Track Finding in the LHCb Vertex Detector](https://arxiv.org/abs/2407.12119) +* [EggNet: An Evolving Graph-based Graph Attention Network for Particle Track Reconstruction](https://arxiv.org/abs/2407.13925) #### Sets (point clouds) @@ -267,6 +275,9 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [Jet Rotational Metrics](https://arxiv.org/abs/2311.06686) * [JetLOV: Enhancing Jet Tree Tagging through Neural Network Learning of Optimal LundNet Variables](https://arxiv.org/abs/2311.14654) * [Exploring the Truth and Beauty of Theory Landscapes with Machine Learning](https://arxiv.org/abs/2401.11513) +* [Exotic and physics-informed support vector machines for high energy physics](https://arxiv.org/abs/2407.03538) +* [Physics-informed machine learning approaches to reactor antineutrino detection](https://arxiv.org/abs/2407.06139) +* [Universal New Physics Latent Space](https://arxiv.org/abs/2407.20315) ### Targets @@ -286,6 +297,7 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [Amplitude-assisted tagging of longitudinally polarised bosons using wide neural networks](https://arxiv.org/abs/2306.07726) [[DOI](https://doi.org/10.1140/epjc/s10052-023-11931-y)] * [Application of Machine Learning Based Top Quark and W Jet Tagging to Hadronic Four-Top Final States Induced by SM as well as BSM Processes](https://arxiv.org/abs/2310.13009) * [Explainable Equivariant Neural Networks for Particle Physics: PELICAN](https://arxiv.org/abs/2307.16506) [[DOI](https://doi.org/10.1007/JHEP03(2024)113)] +* [Interplay of Traditional Methods and Machine Learning Algorithms for Tagging Boosted Objects](https://arxiv.org/abs/2408.01138) [[DOI](https://doi.org/10.1140/epjs/s11734-024-01256-6)] #### $H\rightarrow b\bar{b}$ @@ -323,6 +335,9 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [Quark/Gluon Discrimination and Top Tagging with Dual Attention Transformer](https://arxiv.org/abs/2307.04723) [[DOI](https://doi.org/10.1140/epjc/s10052-023-12293-1)] * [Hierarchical High-Point Energy Flow Network for Jet Tagging](https://arxiv.org/abs/2308.08300) [[DOI](https://doi.org/10.1007/JHEP09(2023)135)] * [Quark-versus-gluon tagging in CMS Open Data with CWoLa and TopicFlow](https://arxiv.org/abs/2312.03434) +* [Jet Flavour Tagging at FCC-ee with a Transformer-based Neural Network: DeepJetTransformer](https://arxiv.org/abs/2406.08590) +* [A multicategory jet image classification framework using deep neural network](https://arxiv.org/abs/2407.03524) +* [Jet Tagging with More-Interaction Particle Transformer](https://arxiv.org/abs/2407.08682) #### top quark tagging @@ -357,6 +372,7 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [Jet Classification Using High-Level Features from Anatomy of Top Jets](https://arxiv.org/abs/2312.11760) * [Interpretable deep learning models for the inference and classification of LHC data](https://arxiv.org/abs/2312.12330) [[DOI](https://doi.org/10.1007/JHEP05(2024)004)] * [The Phase Space Distance Between Collider Events](https://arxiv.org/abs/2405.16698) +* [Hadronic Top Quark Polarimetry with ParticleNet](https://arxiv.org/abs/2407.01663) #### strange jets @@ -393,6 +409,7 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [A Deep Learning Framework for Disentangling Triangle Singularity and Pole-Based Enhancements](https://arxiv.org/abs/2403.18265) * [Meson mass and width: Deep learning approach](https://arxiv.org/abs/2404.00448) * [Exploring Transport Properties of Quark-Gluon Plasma with a Machine-Learning assisted Holographic Approach](https://arxiv.org/abs/2404.18217) +* [Holographic complex potential of a quarkonium from deep learning](https://arxiv.org/abs/2406.06285) #### BSM particles and models @@ -469,6 +486,8 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [Reconstruction of Short-Lived Particles using Graph-Hypergraph Representation Learning](https://arxiv.org/abs/2402.10149) * [Leptoquark Searches at TeV Scale Using Neural Networks at Hadron Collider](https://arxiv.org/abs/2405.08090) * [Boosting probes of CP violation in the top Yukawa coupling with Deep Learning](https://arxiv.org/abs/2405.16499) +* [Learning to see R-parity violating scalar top decays](https://arxiv.org/abs/2406.03096) +* [Graph Reinforcement Learning for Exploring BSM Model Spaces](https://arxiv.org/abs/2407.07203) #### Particle identification @@ -562,6 +581,7 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [Assessment of few-hits machine learning classification algorithms for low energy physics in liquid argon detectors](https://arxiv.org/abs/2305.09744) * [Detector signal characterization with a Bayesian network in XENONnT](https://arxiv.org/abs/2304.05428) [[DOI](https://doi.org/10.1103/PhysRevD.108.012016)] * [Deep Probabilistic Direction Prediction in 3D with Applications to Directional Dark Matter Detectors](https://arxiv.org/abs/2403.15949) +* [Bayesian technique to combine independently-trained Machine-Learning models applied to direct dark matter detection](https://arxiv.org/abs/2407.21008) #### Cosmology, Astro Particle, and Cosmic Ray physics @@ -608,6 +628,9 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [The Measurement and Modelling of Cosmic Ray Muons at KM3NeT Detectors](https://arxiv.org/abs/2402.02620) * [Sibyll★](https://arxiv.org/abs/2404.02636) [[DOI](https://doi.org/10.1016/j.astropartphys.2024.102964)] * [Preheating with deep learning](https://arxiv.org/abs/2405.08901) +* [Neural Networks Assisted Metropolis-Hastings for Bayesian Estimation of Critical Exponent on Elliptic Black Hole Solution in 4D Using Quantum Perturbation Theory](https://arxiv.org/abs/2406.04310) +* [Holographic reconstruction of black hole spacetime: machine learning and entanglement entropy](https://arxiv.org/abs/2406.07395) +* [$\overline{\text{D}}$arkRayNet: Emulation of cosmic-ray antideuteron fluxes from dark matter](https://arxiv.org/abs/2406.18642) #### Tracking @@ -646,6 +669,7 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [A Language Model for Particle Tracking](https://arxiv.org/abs/2402.10239) * [Real-Time Charged Track Reconstruction for CLAS12](https://arxiv.org/abs/2403.04020) * [Improving tracking algorithms with machine learning: a case for line-segment tracking at the High Luminosity LHC](https://arxiv.org/abs/2403.13166) +* [TrackFormers: In Search of Transformer-Based Particle Tracking for the High-Luminosity LHC Era](https://arxiv.org/abs/2407.07179) #### Heavy Ions / Nuclear Physics @@ -729,6 +753,8 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [Deep learning for flow observables in high energy heavy-ion collisions](https://arxiv.org/abs/2404.02602) * [A machine learning-based study of open-charm hadrons in proton-proton collisions at the Large Hadron Collider](https://arxiv.org/abs/2404.09839) * [Pole structure of $P_\psi^N(4312)^+$ via machine learning and uniformized S-matrix](https://arxiv.org/abs/2405.11906) +* [Effects of saturation and fluctuating hotspots for flow observables in ultrarelativistic heavy-ion collisions](https://arxiv.org/abs/2407.01338) +* [AI for Nuclear Physics: the EXCLAIM project](https://arxiv.org/abs/2408.00163) ### Learning strategies @@ -893,6 +919,7 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [Software Compensation for Highly Granular Calorimeters using Machine Learning](https://arxiv.org/abs/2403.04632) [[DOI](https://doi.org/10.1088/1748-0221/19/04/P04037)] * [RootInteractive tool for multidimensional statistical analysis, machine learning and analytical model validation](https://arxiv.org/abs/2403.19330) [[DOI](https://doi.org/10.1051/epjconf/202429506019)] * [Robust Independent Validation of Experiment and Theory: Rivet version 4 release note](https://arxiv.org/abs/2404.15984) +* [Implementing dynamic high-performance computing supported workflows on Scanning Transmission Electron Microscope](https://arxiv.org/abs/2406.11018) #### Hardware/firmware @@ -937,6 +964,10 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [Smartpixels: Towards on-sensor inference of charged particle track parameters and uncertainties](https://arxiv.org/abs/2312.11676) * [Portable acceleration of CMS computing workflows with coprocessors as a service](https://arxiv.org/abs/2402.15366) * [The Neural Network First-Level Hardware Track Trigger of the Belle II Experiment](https://arxiv.org/abs/2402.14962) +* [Comprehensive Machine Learning Model Comparison for Cherenkov and Scintillation Light Separation due to Particle Interactions](https://arxiv.org/abs/2406.09191) +* [Smart Pixels: In-pixel AI for on-sensor data filtering](https://arxiv.org/abs/2406.14860) +* [A Bayesian Framework to Investigate Radiation Reaction in Strong Fields](https://arxiv.org/abs/2406.19420) +* [Comparison of Geometrical Layouts for Next-Generation Large-volume Cherenkov Neutrino Telescopes](https://arxiv.org/abs/2407.19010) #### Deployment @@ -1020,6 +1051,7 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [Accelerating the BSM interpretation of LHC data with machine learning](https://arxiv.org/abs/1611.02704) [[DOI](https://doi.org/10.1016/j.dark.2019.100293)] * [Bayesian Neural Networks for Fast SUSY Predictions](https://arxiv.org/abs/2007.04506) [[DOI](https://doi.org/10.1016/j.physletb.2020.136041)] * [Exploration of Parameter Spaces Assisted by Machine Learning](https://arxiv.org/abs/2207.09959) [[DOI](https://doi.org/10.1016/j.cpc.2023.108902)] +* [HackAnalysis 2: A powerful and hackable recasting tool](https://arxiv.org/abs/2406.10042) ### Matrix elements @@ -1089,6 +1121,10 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [Helicity-dependent parton distribution functions at next-to-next-to-leading order accuracy from inclusive and semi-inclusive deep-inelastic scattering data](https://arxiv.org/abs/2404.04712) * [Determination of $K^0_S$ Fragmentation Functions including BESIII Measurements and using Neural Networks](https://arxiv.org/abs/2404.07334) * [Using analytic models to describe effective PDFs](https://arxiv.org/abs/2404.15175) +* [NNPDF4.0 aN$^3$LO PDFs with QED corrections](https://arxiv.org/abs/2406.01779) +* [A generalized statistical model for fits to parton distributions](https://arxiv.org/abs/2406.01664) +* [Extraction of Information from Polarized Deep Exclusive Scattering with Machine Learning](https://arxiv.org/abs/2406.09258) +* [Explainable AI classification for parton density theory](https://arxiv.org/abs/2407.03411) ### Lattice Gauge Theory @@ -1165,6 +1201,10 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [Building imaginary-time thermal filed theory with artificial neural networks](https://arxiv.org/abs/2405.10493) * [Deep learning lattice gauge theories](https://arxiv.org/abs/2405.14830) * [Generating configurations of increasing lattice size with machine learning and the inverse renormalization group](https://arxiv.org/abs/2405.16288) +* [QCD Phase Diagram at finite Magnetic Field and Chemical Potential: A Holographic Approach Using Machine Learning](https://arxiv.org/abs/2406.12772) +* [Berezinskii--Kosterlitz--Thouless transition of the two-dimensional $XY$ model on the honeycomb lattice](https://arxiv.org/abs/2406.14812) +* [Disordered Lattice Glass $\phi^{4}$ Quantum Field Theory](https://arxiv.org/abs/2407.06569) +* [Study of the mass of pseudoscalar glueball with a deep neural network](https://arxiv.org/abs/2407.12010) ### Function Approximation @@ -1230,6 +1270,7 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [Foundations of automatic feature extraction at LHC--point clouds and graphs](https://arxiv.org/abs/2404.16207) * [Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics](https://arxiv.org/abs/2405.14806) * [Equivariant neural networks for robust $\textit{CP}$ observables](https://arxiv.org/abs/2405.13524) +* [Learning Group Invariant Calabi-Yau Metrics by Fundamental Domain Projections](https://arxiv.org/abs/2407.06914) ## Decorrelation methods. @@ -1339,6 +1380,7 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [DeepTreeGANv2: Iterative Pooling of Point Clouds](https://arxiv.org/abs/2312.00042) * [Integrating Particle Flavor into Deep Learning Models for Hadronization](https://arxiv.org/abs/2312.08453) * [cDVGAN: One Flexible Model for Multi-class Gravitational Wave Signal and Glitch Generation](https://arxiv.org/abs/2401.16356) +* [Applying generative neural networks for fast simulations of the ALICE (CERN) experiment](https://arxiv.org/abs/2407.16704) ### (Variational) Autoencoders @@ -1371,7 +1413,7 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [Deep Generative Models for Ultra-High Granularity Particle Physics Detector Simulation: A Voyage From Emulation to Extrapolation](https://arxiv.org/abs/2403.13825) * [Calo-VQ: Vector-Quantized Two-Stage Generative Model in Calorimeter Simulation](https://arxiv.org/abs/2405.06605) -### Normalizing flows +### (Continuous) Normalizing flows * [Flow-based generative models for Markov chain Monte Carlo in lattice field theory](https://arxiv.org/abs/1904.12072) [[DOI](https://doi.org/10.1103/PhysRevD.100.034515)] * [Invertible Networks or Partons to Detector and Back Again](https://arxiv.org/abs/2006.06685) [[DOI](https://doi.org/10.21468/SciPostPhys.9.5.074)] @@ -1445,6 +1487,9 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [Flow-based Nonperturbative Simulation of First-order Phase Transitions](https://arxiv.org/abs/2404.18323) * [Unifying Simulation and Inference with Normalizing Flows](https://arxiv.org/abs/2404.18992) * [CaloDREAM -- Detector Response Emulation via Attentive flow Matching](https://arxiv.org/abs/2405.09629) +* [Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows](https://arxiv.org/abs/2405.20407) +* [Parnassus: An Automated Approach to Accurate, Precise, and Fast Detector Simulation and Reconstruction](https://arxiv.org/abs/2406.01620) +* [PIPPIN: Generating variable length full events from partons](https://arxiv.org/abs/2406.13074) ### Diffusion Models @@ -1478,6 +1523,9 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [BUFF: Boosted Decision Tree based Ultra-Fast Flow matching](https://arxiv.org/abs/2404.18219) * [Advancing Set-Conditional Set Generation: Graph Diffusion for Fast Simulation of Reconstructed Particles](https://arxiv.org/abs/2405.10106) * [CaloDREAM -- Detector Response Emulation via Attentive flow Matching](https://arxiv.org/abs/2405.09629) +* [Generative Diffusion Models for Fast Simulations of Particle Collisions at CERN](https://arxiv.org/abs/2406.03233) +* [PIPPIN: Generating variable length full events from partons](https://arxiv.org/abs/2406.13074) +* [Applying generative neural networks for fast simulations of the ALICE (CERN) experiment](https://arxiv.org/abs/2407.16704) ### Transformer Models @@ -1488,6 +1536,7 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [Induced Generative Adversarial Particle Transformers](https://arxiv.org/abs/2312.04757) * [Folded context condensation in Path Integral formalism for infinite context transformers](https://arxiv.org/abs/2405.04620) * [Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics](https://arxiv.org/abs/2405.14806) +* [PIPPIN: Generating variable length full events from partons](https://arxiv.org/abs/2406.13074) ### Physics-inspired @@ -1659,6 +1708,9 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [Anomaly detection with flow-based fast calorimeter simulators](https://arxiv.org/abs/2312.11618) * [Incorporating Physical Priors into Weakly-Supervised Anomaly Detection](https://arxiv.org/abs/2405.08889) * [Accelerating Resonance Searches via Signature-Oriented Pre-training](https://arxiv.org/abs/2405.12972) +* [Anomaly-aware summary statistic from data batches](https://arxiv.org/abs/2407.01249) +* [Accelerating template generation in resonant anomaly detection searches with optimal transport](https://arxiv.org/abs/2407.19818) +* [Anomaly Detection Based on Machine Learning for the CMS Electromagnetic Calorimeter Online Data Quality Monitoring](https://arxiv.org/abs/2407.20278) ## Foundation Models, LLMs. @@ -1735,6 +1787,7 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics](https://arxiv.org/abs/2305.10399) * [Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusion](https://arxiv.org/abs/2404.14332) * [The Landscape of Unfolding with Machine Learning](https://arxiv.org/abs/2404.18807) +* [Moment Unfolding](https://arxiv.org/abs/2407.11284) ### Domain adaptation @@ -1777,6 +1830,8 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [Probing intractable beyond-standard-model parameter spaces armed with Machine Learning](https://arxiv.org/abs/2404.02698) * [Boosted four-top production at the LHC : a window to Randall-Sundrum or extended color symmetry](https://arxiv.org/abs/2404.04409) * [Magnetic Monopole Phenomenology at Future Hadron Colliders](https://arxiv.org/abs/2404.10871) +* [Exploring Exotic Decays of the Higgs Boson to Multi-Photons at the LHC via Multimodal Learning Approaches](https://arxiv.org/abs/2405.18834) +* [Refinable modeling for unbinned SMEFT analyses](https://arxiv.org/abs/2406.19076) ### Differentiable Simulation @@ -1810,6 +1865,8 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [Interpretable Machine Learning Methods Applied to Jet Background Subtraction in Heavy Ion Collisions](https://arxiv.org/abs/2303.08275) [[DOI](https://doi.org/10.1103/PhysRevC.108.L021901)] * [Interpretable deep learning models for the inference and classification of LHC data](https://arxiv.org/abs/2312.12330) [[DOI](https://doi.org/10.1007/JHEP05(2024)004)] * [Statistical divergences in high-dimensional hypothesis testing and a modern technique for estimating them](https://arxiv.org/abs/2405.06397) +* [Interpretable machine learning approach for electron antineutrino selection in a large liquid scintillator detector](https://arxiv.org/abs/2406.12901) +* [Explainable AI classification for parton density theory](https://arxiv.org/abs/2407.03411) ### Estimation @@ -1821,6 +1878,7 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [Deep Neural Network Uncertainty Quantification for LArTPC Reconstruction](https://arxiv.org/abs/2302.03787) [[DOI](https://doi.org/10.1088/1748-0221/18/12/P12013)] * [The DL Advocate: Playing the devil's advocate with hidden systematic uncertainties](https://arxiv.org/abs/2303.15956) [[DOI](https://doi.org/10.1140/epjc/s10052-023-11925-w)] * [Smartpixels: Towards on-sensor inference of charged particle track parameters and uncertainties](https://arxiv.org/abs/2312.11676) +* [Calibrating Bayesian Generative Machine Learning for Bayesiamplification](https://arxiv.org/abs/2408.00838) ### Mitigation @@ -1851,6 +1909,7 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [Black holes and the loss landscape in machine learning](https://arxiv.org/abs/2306.14817) [[DOI](https://doi.org/10.1007/JHEP10(2023)107)] * [Neural Network Field Theories: Non-Gaussianity, Actions, and Locality](https://arxiv.org/abs/2307.03223) [[DOI](https://doi.org/10.1088/2632-2153/ad17d3)] * [Metric Flows with Neural Networks](https://arxiv.org/abs/2310.19870) +* [Neural Scaling Laws From Large-N Field Theory: Solvable Model Beyond the Ridgeless Limit](https://arxiv.org/abs/2405.19398) ### ML for theory @@ -1905,6 +1964,7 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [Simultaneous energy and mass calibration of large-radius jets with the ATLAS detector using a deep neural network](https://arxiv.org/abs/2311.08885) * [Neural Network Applications to Improve Drift Chamber Track Position Measurements](https://arxiv.org/abs/2311.15541) * [Particle identification with machine learning from incomplete data in the ALICE experiment](https://arxiv.org/abs/2403.17436) +* [A Search for Leptonic Photon, $Z_{l}$, at All Three CLIC Energy Stages by Using Artificial Neural Networks (ANN)](https://arxiv.org/abs/2406.10097) [[DOI](https://doi.org/10.5506/APhysPolB.55.6-A4)] ### Searches and measurements where ML reconstruction is a core component @@ -1944,6 +2004,11 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [ATLAS searches for additional scalars and exotic Higgs boson decays with the LHC Run 2 dataset](https://arxiv.org/abs/2405.04914) * [Test of light-lepton universality in $\tau$ decays with the Belle II experiment](https://arxiv.org/abs/2405.14625) * [Dark sector searches with the CMS experiment](https://arxiv.org/abs/2405.13778) +* [A simultaneous unbinned differential cross section measurement of twenty-four $Z$+jets kinematic observables with the ATLAS detector](https://arxiv.org/abs/2405.20041) +* [Improving neutrino energy estimation of charged-current interaction events with recurrent neural networks in MicroBooNE](https://arxiv.org/abs/2406.10123) +* [Shower Separation in Five Dimensions for Highly Granular Calorimeters using Machine Learning](https://arxiv.org/abs/2407.00178) +* [Measurement of boosted Higgs bosons produced via vector boson fusion or gluon fusion in the H $\to$$\mathrm{b\bar{b}}$ decay mode using LHC proton-proton collision data at $\sqrt{s}$](https://arxiv.org/abs/2407.08012) +* [Accuracy versus precision in boosted top tagging with the ATLAS detector](https://arxiv.org/abs/2407.20127) ### Final analysis discriminate for searches diff --git a/docs/index.md b/docs/index.md index 79ac57b..cea84da 100644 --- a/docs/index.md +++ b/docs/index.md @@ -99,6 +99,10 @@ const expandElements = shouldExpand => { * [Unsupervised and lightly supervised learning in particle physics](https://arxiv.org/abs/2403.13676) * [Machine Learning in High Energy Physics: A review of heavy-flavor jet tagging at the LHC](https://arxiv.org/abs/2404.01071) * [The Landscape of Unfolding with Machine Learning](https://arxiv.org/abs/2404.18807) + * [A Comprehensive Evaluation of Generative Models in Calorimeter Shower Simulation](https://arxiv.org/abs/2406.12898) + * [Top-philic Machine Learning](https://arxiv.org/abs/2407.00183) [[DOI](https://doi.org/10.1140/epjs/s11734-024-01237-9)] + * [QCD Masterclass Lectures on Jet Physics and Machine Learning](https://arxiv.org/abs/2407.04897) + * [TASI Lectures on Physics for Machine Learning](https://arxiv.org/abs/2408.00082) ??? example "Classical papers" @@ -275,6 +279,10 @@ const expandElements = shouldExpand => { * [A case study of sending graph neural networks back to the test bench for applications in high-energy particle physics](https://arxiv.org/abs/2402.17386) * [NuGraph2: A Graph Neural Network for Neutrino Physics Event Reconstruction](https://arxiv.org/abs/2403.11872) * [Advancing Set-Conditional Set Generation: Graph Diffusion for Fast Simulation of Reconstructed Particles](https://arxiv.org/abs/2405.10106) + * [Using graph neural networks to reconstruct charged pion showers in the CMS High Granularity Calorimeter](https://arxiv.org/abs/2406.11937) + * [Accelerating Graph-based Tracking Tasks with Symbolic Regression](https://arxiv.org/abs/2406.16752) + * [Graph Neural Network-Based Track Finding in the LHCb Vertex Detector](https://arxiv.org/abs/2407.12119) + * [EggNet: An Evolving Graph-based Graph Attention Network for Particle Track Reconstruction](https://arxiv.org/abs/2407.13925) #### Sets (point clouds) @@ -318,6 +326,9 @@ const expandElements = shouldExpand => { * [Jet Rotational Metrics](https://arxiv.org/abs/2311.06686) * [JetLOV: Enhancing Jet Tree Tagging through Neural Network Learning of Optimal LundNet Variables](https://arxiv.org/abs/2311.14654) * [Exploring the Truth and Beauty of Theory Landscapes with Machine Learning](https://arxiv.org/abs/2401.11513) + * [Exotic and physics-informed support vector machines for high energy physics](https://arxiv.org/abs/2407.03538) + * [Physics-informed machine learning approaches to reactor antineutrino detection](https://arxiv.org/abs/2407.06139) + * [Universal New Physics Latent Space](https://arxiv.org/abs/2407.20315) ??? example "Targets" @@ -342,6 +353,7 @@ const expandElements = shouldExpand => { * [Amplitude-assisted tagging of longitudinally polarised bosons using wide neural networks](https://arxiv.org/abs/2306.07726) [[DOI](https://doi.org/10.1140/epjc/s10052-023-11931-y)] * [Application of Machine Learning Based Top Quark and W Jet Tagging to Hadronic Four-Top Final States Induced by SM as well as BSM Processes](https://arxiv.org/abs/2310.13009) * [Explainable Equivariant Neural Networks for Particle Physics: PELICAN](https://arxiv.org/abs/2307.16506) [[DOI](https://doi.org/10.1007/JHEP03(2024)113)] + * [Interplay of Traditional Methods and Machine Learning Algorithms for Tagging Boosted Objects](https://arxiv.org/abs/2408.01138) [[DOI](https://doi.org/10.1140/epjs/s11734-024-01256-6)] #### $H\rightarrow b\bar{b}$ @@ -379,6 +391,9 @@ const expandElements = shouldExpand => { * [Quark/Gluon Discrimination and Top Tagging with Dual Attention Transformer](https://arxiv.org/abs/2307.04723) [[DOI](https://doi.org/10.1140/epjc/s10052-023-12293-1)] * [Hierarchical High-Point Energy Flow Network for Jet Tagging](https://arxiv.org/abs/2308.08300) [[DOI](https://doi.org/10.1007/JHEP09(2023)135)] * [Quark-versus-gluon tagging in CMS Open Data with CWoLa and TopicFlow](https://arxiv.org/abs/2312.03434) + * [Jet Flavour Tagging at FCC-ee with a Transformer-based Neural Network: DeepJetTransformer](https://arxiv.org/abs/2406.08590) + * [A multicategory jet image classification framework using deep neural network](https://arxiv.org/abs/2407.03524) + * [Jet Tagging with More-Interaction Particle Transformer](https://arxiv.org/abs/2407.08682) #### top quark tagging @@ -413,6 +428,7 @@ const expandElements = shouldExpand => { * [Jet Classification Using High-Level Features from Anatomy of Top Jets](https://arxiv.org/abs/2312.11760) * [Interpretable deep learning models for the inference and classification of LHC data](https://arxiv.org/abs/2312.12330) [[DOI](https://doi.org/10.1007/JHEP05(2024)004)] * [The Phase Space Distance Between Collider Events](https://arxiv.org/abs/2405.16698) + * [Hadronic Top Quark Polarimetry with ParticleNet](https://arxiv.org/abs/2407.01663) #### strange jets @@ -449,6 +465,7 @@ const expandElements = shouldExpand => { * [A Deep Learning Framework for Disentangling Triangle Singularity and Pole-Based Enhancements](https://arxiv.org/abs/2403.18265) * [Meson mass and width: Deep learning approach](https://arxiv.org/abs/2404.00448) * [Exploring Transport Properties of Quark-Gluon Plasma with a Machine-Learning assisted Holographic Approach](https://arxiv.org/abs/2404.18217) + * [Holographic complex potential of a quarkonium from deep learning](https://arxiv.org/abs/2406.06285) #### BSM particles and models @@ -525,6 +542,8 @@ const expandElements = shouldExpand => { * [Reconstruction of Short-Lived Particles using Graph-Hypergraph Representation Learning](https://arxiv.org/abs/2402.10149) * [Leptoquark Searches at TeV Scale Using Neural Networks at Hadron Collider](https://arxiv.org/abs/2405.08090) * [Boosting probes of CP violation in the top Yukawa coupling with Deep Learning](https://arxiv.org/abs/2405.16499) + * [Learning to see R-parity violating scalar top decays](https://arxiv.org/abs/2406.03096) + * [Graph Reinforcement Learning for Exploring BSM Model Spaces](https://arxiv.org/abs/2407.07203) #### Particle identification @@ -618,6 +637,7 @@ const expandElements = shouldExpand => { * [Assessment of few-hits machine learning classification algorithms for low energy physics in liquid argon detectors](https://arxiv.org/abs/2305.09744) * [Detector signal characterization with a Bayesian network in XENONnT](https://arxiv.org/abs/2304.05428) [[DOI](https://doi.org/10.1103/PhysRevD.108.012016)] * [Deep Probabilistic Direction Prediction in 3D with Applications to Directional Dark Matter Detectors](https://arxiv.org/abs/2403.15949) + * [Bayesian technique to combine independently-trained Machine-Learning models applied to direct dark matter detection](https://arxiv.org/abs/2407.21008) #### Cosmology, Astro Particle, and Cosmic Ray physics @@ -664,6 +684,9 @@ const expandElements = shouldExpand => { * [The Measurement and Modelling of Cosmic Ray Muons at KM3NeT Detectors](https://arxiv.org/abs/2402.02620) * [Sibyll★](https://arxiv.org/abs/2404.02636) [[DOI](https://doi.org/10.1016/j.astropartphys.2024.102964)] * [Preheating with deep learning](https://arxiv.org/abs/2405.08901) + * [Neural Networks Assisted Metropolis-Hastings for Bayesian Estimation of Critical Exponent on Elliptic Black Hole Solution in 4D Using Quantum Perturbation Theory](https://arxiv.org/abs/2406.04310) + * [Holographic reconstruction of black hole spacetime: machine learning and entanglement entropy](https://arxiv.org/abs/2406.07395) + * [$\overline{\text{D}}$arkRayNet: Emulation of cosmic-ray antideuteron fluxes from dark matter](https://arxiv.org/abs/2406.18642) #### Tracking @@ -702,6 +725,7 @@ const expandElements = shouldExpand => { * [A Language Model for Particle Tracking](https://arxiv.org/abs/2402.10239) * [Real-Time Charged Track Reconstruction for CLAS12](https://arxiv.org/abs/2403.04020) * [Improving tracking algorithms with machine learning: a case for line-segment tracking at the High Luminosity LHC](https://arxiv.org/abs/2403.13166) + * [TrackFormers: In Search of Transformer-Based Particle Tracking for the High-Luminosity LHC Era](https://arxiv.org/abs/2407.07179) #### Heavy Ions / Nuclear Physics @@ -785,6 +809,8 @@ const expandElements = shouldExpand => { * [Deep learning for flow observables in high energy heavy-ion collisions](https://arxiv.org/abs/2404.02602) * [A machine learning-based study of open-charm hadrons in proton-proton collisions at the Large Hadron Collider](https://arxiv.org/abs/2404.09839) * [Pole structure of $P_\psi^N(4312)^+$ via machine learning and uniformized S-matrix](https://arxiv.org/abs/2405.11906) + * [Effects of saturation and fluctuating hotspots for flow observables in ultrarelativistic heavy-ion collisions](https://arxiv.org/abs/2407.01338) + * [AI for Nuclear Physics: the EXCLAIM project](https://arxiv.org/abs/2408.00163) ??? example "Learning strategies" @@ -959,6 +985,7 @@ const expandElements = shouldExpand => { * [Software Compensation for Highly Granular Calorimeters using Machine Learning](https://arxiv.org/abs/2403.04632) [[DOI](https://doi.org/10.1088/1748-0221/19/04/P04037)] * [RootInteractive tool for multidimensional statistical analysis, machine learning and analytical model validation](https://arxiv.org/abs/2403.19330) [[DOI](https://doi.org/10.1051/epjconf/202429506019)] * [Robust Independent Validation of Experiment and Theory: Rivet version 4 release note](https://arxiv.org/abs/2404.15984) + * [Implementing dynamic high-performance computing supported workflows on Scanning Transmission Electron Microscope](https://arxiv.org/abs/2406.11018) #### Hardware/firmware @@ -1003,6 +1030,10 @@ const expandElements = shouldExpand => { * [Smartpixels: Towards on-sensor inference of charged particle track parameters and uncertainties](https://arxiv.org/abs/2312.11676) * [Portable acceleration of CMS computing workflows with coprocessors as a service](https://arxiv.org/abs/2402.15366) * [The Neural Network First-Level Hardware Track Trigger of the Belle II Experiment](https://arxiv.org/abs/2402.14962) + * [Comprehensive Machine Learning Model Comparison for Cherenkov and Scintillation Light Separation due to Particle Interactions](https://arxiv.org/abs/2406.09191) + * [Smart Pixels: In-pixel AI for on-sensor data filtering](https://arxiv.org/abs/2406.14860) + * [A Bayesian Framework to Investigate Radiation Reaction in Strong Fields](https://arxiv.org/abs/2406.19420) + * [Comparison of Geometrical Layouts for Next-Generation Large-volume Cherenkov Neutrino Telescopes](https://arxiv.org/abs/2407.19010) #### Deployment @@ -1101,6 +1132,7 @@ const expandElements = shouldExpand => { * [Accelerating the BSM interpretation of LHC data with machine learning](https://arxiv.org/abs/1611.02704) [[DOI](https://doi.org/10.1016/j.dark.2019.100293)] * [Bayesian Neural Networks for Fast SUSY Predictions](https://arxiv.org/abs/2007.04506) [[DOI](https://doi.org/10.1016/j.physletb.2020.136041)] * [Exploration of Parameter Spaces Assisted by Machine Learning](https://arxiv.org/abs/2207.09959) [[DOI](https://doi.org/10.1016/j.cpc.2023.108902)] + * [HackAnalysis 2: A powerful and hackable recasting tool](https://arxiv.org/abs/2406.10042) ??? example "Matrix elements" @@ -1185,6 +1217,10 @@ const expandElements = shouldExpand => { * [Helicity-dependent parton distribution functions at next-to-next-to-leading order accuracy from inclusive and semi-inclusive deep-inelastic scattering data](https://arxiv.org/abs/2404.04712) * [Determination of $K^0_S$ Fragmentation Functions including BESIII Measurements and using Neural Networks](https://arxiv.org/abs/2404.07334) * [Using analytic models to describe effective PDFs](https://arxiv.org/abs/2404.15175) + * [NNPDF4.0 aN$^3$LO PDFs with QED corrections](https://arxiv.org/abs/2406.01779) + * [A generalized statistical model for fits to parton distributions](https://arxiv.org/abs/2406.01664) + * [Extraction of Information from Polarized Deep Exclusive Scattering with Machine Learning](https://arxiv.org/abs/2406.09258) + * [Explainable AI classification for parton density theory](https://arxiv.org/abs/2407.03411) ??? example "Lattice Gauge Theory" @@ -1266,6 +1302,10 @@ const expandElements = shouldExpand => { * [Building imaginary-time thermal filed theory with artificial neural networks](https://arxiv.org/abs/2405.10493) * [Deep learning lattice gauge theories](https://arxiv.org/abs/2405.14830) * [Generating configurations of increasing lattice size with machine learning and the inverse renormalization group](https://arxiv.org/abs/2405.16288) + * [QCD Phase Diagram at finite Magnetic Field and Chemical Potential: A Holographic Approach Using Machine Learning](https://arxiv.org/abs/2406.12772) + * [Berezinskii--Kosterlitz--Thouless transition of the two-dimensional $XY$ model on the honeycomb lattice](https://arxiv.org/abs/2406.14812) + * [Disordered Lattice Glass $\phi^{4}$ Quantum Field Theory](https://arxiv.org/abs/2407.06569) + * [Study of the mass of pseudoscalar glueball with a deep neural network](https://arxiv.org/abs/2407.12010) ??? example "Function Approximation" @@ -1349,6 +1389,7 @@ const expandElements = shouldExpand => { * [Foundations of automatic feature extraction at LHC--point clouds and graphs](https://arxiv.org/abs/2404.16207) * [Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics](https://arxiv.org/abs/2405.14806) * [Equivariant neural networks for robust $\textit{CP}$ observables](https://arxiv.org/abs/2405.13524) + * [Learning Group Invariant Calabi-Yau Metrics by Fundamental Domain Projections](https://arxiv.org/abs/2407.06914) ## Decorrelation methods. @@ -1466,6 +1507,7 @@ const expandElements = shouldExpand => { * [DeepTreeGANv2: Iterative Pooling of Point Clouds](https://arxiv.org/abs/2312.00042) * [Integrating Particle Flavor into Deep Learning Models for Hadronization](https://arxiv.org/abs/2312.08453) * [cDVGAN: One Flexible Model for Multi-class Gravitational Wave Signal and Glitch Generation](https://arxiv.org/abs/2401.16356) + * [Applying generative neural networks for fast simulations of the ALICE (CERN) experiment](https://arxiv.org/abs/2407.16704) ??? example "(Variational) Autoencoders" @@ -1504,10 +1546,10 @@ const expandElements = shouldExpand => { * [Calo-VQ: Vector-Quantized Two-Stage Generative Model in Calorimeter Simulation](https://arxiv.org/abs/2405.06605) -??? example "Normalizing flows" +??? example "(Continuous) Normalizing flows" * [Flow-based generative models for Markov chain Monte Carlo in lattice field theory](https://arxiv.org/abs/1904.12072) [[DOI](https://doi.org/10.1103/PhysRevD.100.034515)] @@ -1582,6 +1624,9 @@ const expandElements = shouldExpand => { * [Flow-based Nonperturbative Simulation of First-order Phase Transitions](https://arxiv.org/abs/2404.18323) * [Unifying Simulation and Inference with Normalizing Flows](https://arxiv.org/abs/2404.18992) * [CaloDREAM -- Detector Response Emulation via Attentive flow Matching](https://arxiv.org/abs/2405.09629) + * [Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows](https://arxiv.org/abs/2405.20407) + * [Parnassus: An Automated Approach to Accurate, Precise, and Fast Detector Simulation and Reconstruction](https://arxiv.org/abs/2406.01620) + * [PIPPIN: Generating variable length full events from partons](https://arxiv.org/abs/2406.13074) ??? example "Diffusion Models" @@ -1620,6 +1665,9 @@ const expandElements = shouldExpand => { * [BUFF: Boosted Decision Tree based Ultra-Fast Flow matching](https://arxiv.org/abs/2404.18219) * [Advancing Set-Conditional Set Generation: Graph Diffusion for Fast Simulation of Reconstructed Particles](https://arxiv.org/abs/2405.10106) * [CaloDREAM -- Detector Response Emulation via Attentive flow Matching](https://arxiv.org/abs/2405.09629) + * [Generative Diffusion Models for Fast Simulations of Particle Collisions at CERN](https://arxiv.org/abs/2406.03233) + * [PIPPIN: Generating variable length full events from partons](https://arxiv.org/abs/2406.13074) + * [Applying generative neural networks for fast simulations of the ALICE (CERN) experiment](https://arxiv.org/abs/2407.16704) ??? example "Transformer Models" @@ -1635,6 +1683,7 @@ const expandElements = shouldExpand => { * [Induced Generative Adversarial Particle Transformers](https://arxiv.org/abs/2312.04757) * [Folded context condensation in Path Integral formalism for infinite context transformers](https://arxiv.org/abs/2405.04620) * [Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics](https://arxiv.org/abs/2405.14806) + * [PIPPIN: Generating variable length full events from partons](https://arxiv.org/abs/2406.13074) ??? example "Physics-inspired" @@ -1834,6 +1883,9 @@ const expandElements = shouldExpand => { * [Anomaly detection with flow-based fast calorimeter simulators](https://arxiv.org/abs/2312.11618) * [Incorporating Physical Priors into Weakly-Supervised Anomaly Detection](https://arxiv.org/abs/2405.08889) * [Accelerating Resonance Searches via Signature-Oriented Pre-training](https://arxiv.org/abs/2405.12972) + * [Anomaly-aware summary statistic from data batches](https://arxiv.org/abs/2407.01249) + * [Accelerating template generation in resonant anomaly detection searches with optimal transport](https://arxiv.org/abs/2407.19818) + * [Anomaly Detection Based on Machine Learning for the CMS Electromagnetic Calorimeter Online Data Quality Monitoring](https://arxiv.org/abs/2407.20278) ## Foundation Models, LLMs. @@ -1923,6 +1975,7 @@ const expandElements = shouldExpand => { * [End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics](https://arxiv.org/abs/2305.10399) * [Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusion](https://arxiv.org/abs/2404.14332) * [The Landscape of Unfolding with Machine Learning](https://arxiv.org/abs/2404.18807) + * [Moment Unfolding](https://arxiv.org/abs/2407.11284) ??? example "Domain adaptation" @@ -1975,6 +2028,8 @@ const expandElements = shouldExpand => { * [Probing intractable beyond-standard-model parameter spaces armed with Machine Learning](https://arxiv.org/abs/2404.02698) * [Boosted four-top production at the LHC : a window to Randall-Sundrum or extended color symmetry](https://arxiv.org/abs/2404.04409) * [Magnetic Monopole Phenomenology at Future Hadron Colliders](https://arxiv.org/abs/2404.10871) + * [Exploring Exotic Decays of the Higgs Boson to Multi-Photons at the LHC via Multimodal Learning Approaches](https://arxiv.org/abs/2405.18834) + * [Refinable modeling for unbinned SMEFT analyses](https://arxiv.org/abs/2406.19076) ??? example "Differentiable Simulation" @@ -2018,6 +2073,8 @@ const expandElements = shouldExpand => { * [Interpretable Machine Learning Methods Applied to Jet Background Subtraction in Heavy Ion Collisions](https://arxiv.org/abs/2303.08275) [[DOI](https://doi.org/10.1103/PhysRevC.108.L021901)] * [Interpretable deep learning models for the inference and classification of LHC data](https://arxiv.org/abs/2312.12330) [[DOI](https://doi.org/10.1007/JHEP05(2024)004)] * [Statistical divergences in high-dimensional hypothesis testing and a modern technique for estimating them](https://arxiv.org/abs/2405.06397) + * [Interpretable machine learning approach for electron antineutrino selection in a large liquid scintillator detector](https://arxiv.org/abs/2406.12901) + * [Explainable AI classification for parton density theory](https://arxiv.org/abs/2407.03411) ??? example "Estimation" @@ -2034,6 +2091,7 @@ const expandElements = shouldExpand => { * [Deep Neural Network Uncertainty Quantification for LArTPC Reconstruction](https://arxiv.org/abs/2302.03787) [[DOI](https://doi.org/10.1088/1748-0221/18/12/P12013)] * [The DL Advocate: Playing the devil's advocate with hidden systematic uncertainties](https://arxiv.org/abs/2303.15956) [[DOI](https://doi.org/10.1140/epjc/s10052-023-11925-w)] * [Smartpixels: Towards on-sensor inference of charged particle track parameters and uncertainties](https://arxiv.org/abs/2312.11676) + * [Calibrating Bayesian Generative Machine Learning for Bayesiamplification](https://arxiv.org/abs/2408.00838) ??? example "Mitigation" @@ -2079,6 +2137,7 @@ const expandElements = shouldExpand => { * [Black holes and the loss landscape in machine learning](https://arxiv.org/abs/2306.14817) [[DOI](https://doi.org/10.1007/JHEP10(2023)107)] * [Neural Network Field Theories: Non-Gaussianity, Actions, and Locality](https://arxiv.org/abs/2307.03223) [[DOI](https://doi.org/10.1088/2632-2153/ad17d3)] * [Metric Flows with Neural Networks](https://arxiv.org/abs/2310.19870) + * [Neural Scaling Laws From Large-N Field Theory: Solvable Model Beyond the Ridgeless Limit](https://arxiv.org/abs/2405.19398) ??? example "ML for theory" @@ -2143,6 +2202,7 @@ const expandElements = shouldExpand => { * [Simultaneous energy and mass calibration of large-radius jets with the ATLAS detector using a deep neural network](https://arxiv.org/abs/2311.08885) * [Neural Network Applications to Improve Drift Chamber Track Position Measurements](https://arxiv.org/abs/2311.15541) * [Particle identification with machine learning from incomplete data in the ALICE experiment](https://arxiv.org/abs/2403.17436) + * [A Search for Leptonic Photon, $Z_{l}$, at All Three CLIC Energy Stages by Using Artificial Neural Networks (ANN)](https://arxiv.org/abs/2406.10097) [[DOI](https://doi.org/10.5506/APhysPolB.55.6-A4)] ??? example "Searches and measurements where ML reconstruction is a core component" @@ -2187,6 +2247,11 @@ const expandElements = shouldExpand => { * [ATLAS searches for additional scalars and exotic Higgs boson decays with the LHC Run 2 dataset](https://arxiv.org/abs/2405.04914) * [Test of light-lepton universality in $\tau$ decays with the Belle II experiment](https://arxiv.org/abs/2405.14625) * [Dark sector searches with the CMS experiment](https://arxiv.org/abs/2405.13778) + * [A simultaneous unbinned differential cross section measurement of twenty-four $Z$+jets kinematic observables with the ATLAS detector](https://arxiv.org/abs/2405.20041) + * [Improving neutrino energy estimation of charged-current interaction events with recurrent neural networks in MicroBooNE](https://arxiv.org/abs/2406.10123) + * [Shower Separation in Five Dimensions for Highly Granular Calorimeters using Machine Learning](https://arxiv.org/abs/2407.00178) + * [Measurement of boosted Higgs bosons produced via vector boson fusion or gluon fusion in the H $\to$$\mathrm{b\bar{b}}$ decay mode using LHC proton-proton collision data at $\sqrt{s}$](https://arxiv.org/abs/2407.08012) + * [Accuracy versus precision in boosted top tagging with the ATLAS detector](https://arxiv.org/abs/2407.20127) ??? example "Final analysis discriminate for searches" diff --git a/docs/recent.md b/docs/recent.md index f8ecfc3..ed5bce0 100644 --- a/docs/recent.md +++ b/docs/recent.md @@ -9,7 +9,91 @@ search: This is an automatically compiled list of papers which have been added to the living review that were made public within the previous 4 months at the time of updating. This is not an exhaustive list of released papers, and is only able to find those which have both year and month data provided in the bib reference. +## August 2024 +* [Calibrating Bayesian Generative Machine Learning for Bayesiamplification](https://arxiv.org/abs/2408.00838) +* [Interplay of Traditional Methods and Machine Learning Algorithms for Tagging Boosted Objects](https://arxiv.org/abs/2408.01138) [[DOI](https://doi.org/10.1140/epjs/s11734-024-01256-6)] + +## July 2024 +* [AI for Nuclear Physics: the EXCLAIM project](https://arxiv.org/abs/2408.00163) +* [TASI Lectures on Physics for Machine Learning](https://arxiv.org/abs/2408.00082) +* [Improved Precision in $Vh(\rightarrow b\bar b)$ via Boosted Decision Trees](https://arxiv.org/abs/2407.21239) +* [Universal New Physics Latent Space](https://arxiv.org/abs/2407.20315) +* [Bayesian technique to combine independently-trained Machine-Learning models applied to direct dark matter detection](https://arxiv.org/abs/2407.21008) +* [Anomaly Detection Based on Machine Learning for the CMS Electromagnetic Calorimeter Online Data Quality Monitoring](https://arxiv.org/abs/2407.20278) +* [Accelerating template generation in resonant anomaly detection searches with optimal transport](https://arxiv.org/abs/2407.19818) +* [Probing Charm Yukawa through $ch$ Associated Production at the Hadron Collider](https://arxiv.org/abs/2407.19797) +* [Accuracy versus precision in boosted top tagging with the ATLAS detector](https://arxiv.org/abs/2407.20127) +* [Comparison of Geometrical Layouts for Next-Generation Large-volume Cherenkov Neutrino Telescopes](https://arxiv.org/abs/2407.19010) +* [The Observation of a 95 GeV Scalar at Future Electron-Positron Colliders](https://arxiv.org/abs/2407.16806) +* [Applying generative neural networks for fast simulations of the ALICE (CERN) experiment](https://arxiv.org/abs/2407.16704) +* [EggNet: An Evolving Graph-based Graph Attention Network for Particle Track Reconstruction](https://arxiv.org/abs/2407.13925) +* [Exploring Top-Quark Signatures of Heavy Flavor-Violating Scalars at the LHC with Parametrized Neural Networks](https://arxiv.org/abs/2407.12118) + +## June 2024 +* [Study of the mass of pseudoscalar glueball with a deep neural network](https://arxiv.org/abs/2407.12010) + +## July 2024 +* [Graph Neural Network-Based Track Finding in the LHCb Vertex Detector](https://arxiv.org/abs/2407.12119) +* [Moment Unfolding](https://arxiv.org/abs/2407.11284) +* [Phenomenology of photons-enriched semi-visible jets](https://arxiv.org/abs/2407.08276) +* [Jet Tagging with More-Interaction Particle Transformer](https://arxiv.org/abs/2407.08682) +* [Measurement of boosted Higgs bosons produced via vector boson fusion or gluon fusion in the H $\to$$\mathrm{b\bar{b}}$ decay mode using LHC proton-proton collision data at $\sqrt{s}$](https://arxiv.org/abs/2407.08012) +* [A Two-Stage Machine Learning-Aided Approach for Quench Identification at the European XFEL](https://arxiv.org/abs/2407.08408) +* [Graph Reinforcement Learning for Exploring BSM Model Spaces](https://arxiv.org/abs/2407.07203) +* [TrackFormers: In Search of Transformer-Based Particle Tracking for the High-Luminosity LHC Era](https://arxiv.org/abs/2407.07179) +* [Deep(er) Reconstruction of Imaging Cherenkov Detectors with Swin Transformers and Normalizing Flow Models](https://arxiv.org/abs/2407.07376) +* [A unified machine learning approach for reconstructing hadronically decaying tau leptons](https://arxiv.org/abs/2407.06788) +* [Disordered Lattice Glass $\phi^{4}$ Quantum Field Theory](https://arxiv.org/abs/2407.06569) +* [Learning Group Invariant Calabi-Yau Metrics by Fundamental Domain Projections](https://arxiv.org/abs/2407.06914) +* [QCD Masterclass Lectures on Jet Physics and Machine Learning](https://arxiv.org/abs/2407.04897) +* [Extraction of fissile isotope antineutrino spectra using feedforward neural network](https://arxiv.org/abs/2407.05834) +* [Physics-informed machine learning approaches to reactor antineutrino detection](https://arxiv.org/abs/2407.06139) +* [Explainable AI classification for parton density theory](https://arxiv.org/abs/2407.03411) +* [A multicategory jet image classification framework using deep neural network](https://arxiv.org/abs/2407.03524) +* [Exotic and physics-informed support vector machines for high energy physics](https://arxiv.org/abs/2407.03538) +* [Hadronic Top Quark Polarimetry with ParticleNet](https://arxiv.org/abs/2407.01663) + +## June 2024 +* [Top-philic Machine Learning](https://arxiv.org/abs/2407.00183) [[DOI](https://doi.org/10.1140/epjs/s11734-024-01237-9)] + +## July 2024 +* [Effects of saturation and fluctuating hotspots for flow observables in ultrarelativistic heavy-ion collisions](https://arxiv.org/abs/2407.01338) +* [Anomaly-aware summary statistic from data batches](https://arxiv.org/abs/2407.01249) + +## June 2024 +* [Calculation of crystal defects induced in CaWO$_{4}$ by 100 eV displacement cascades using a linear Machine Learning interatomic potential](https://arxiv.org/abs/2407.00133) +* [Shower Separation in Five Dimensions for Highly Granular Calorimeters using Machine Learning](https://arxiv.org/abs/2407.00178) +* [A Bayesian Framework to Investigate Radiation Reaction in Strong Fields](https://arxiv.org/abs/2406.19420) +* [$\overline{\text{D}}$arkRayNet: Emulation of cosmic-ray antideuteron fluxes from dark matter](https://arxiv.org/abs/2406.18642) +* [Refinable modeling for unbinned SMEFT analyses](https://arxiv.org/abs/2406.19076) +* [Accelerating Graph-based Tracking Tasks with Symbolic Regression](https://arxiv.org/abs/2406.16752) +* [Berezinskii--Kosterlitz--Thouless transition of the two-dimensional $XY$ model on the honeycomb lattice](https://arxiv.org/abs/2406.14812) +* [Smart Pixels: In-pixel AI for on-sensor data filtering](https://arxiv.org/abs/2406.14860) +* [PIPPIN: Generating variable length full events from partons](https://arxiv.org/abs/2406.13074) +* [A Comprehensive Evaluation of Generative Models in Calorimeter Shower Simulation](https://arxiv.org/abs/2406.12898) +* [Interpretable machine learning approach for electron antineutrino selection in a large liquid scintillator detector](https://arxiv.org/abs/2406.12901) +* [QCD Phase Diagram at finite Magnetic Field and Chemical Potential: A Holographic Approach Using Machine Learning](https://arxiv.org/abs/2406.12772) +* [Using graph neural networks to reconstruct charged pion showers in the CMS High Granularity Calorimeter](https://arxiv.org/abs/2406.11937) +* [Implementing dynamic high-performance computing supported workflows on Scanning Transmission Electron Microscope](https://arxiv.org/abs/2406.11018) +* [HackAnalysis 2: A powerful and hackable recasting tool](https://arxiv.org/abs/2406.10042) +* [Improving neutrino energy estimation of charged-current interaction events with recurrent neural networks in MicroBooNE](https://arxiv.org/abs/2406.10123) +* [Extraction of Information from Polarized Deep Exclusive Scattering with Machine Learning](https://arxiv.org/abs/2406.09258) +* [Jet Flavour Tagging at FCC-ee with a Transformer-based Neural Network: DeepJetTransformer](https://arxiv.org/abs/2406.08590) +* [Comprehensive Machine Learning Model Comparison for Cherenkov and Scintillation Light Separation due to Particle Interactions](https://arxiv.org/abs/2406.09191) +* [Holographic reconstruction of black hole spacetime: machine learning and entanglement entropy](https://arxiv.org/abs/2406.07395) +* [Holographic complex potential of a quarkonium from deep learning](https://arxiv.org/abs/2406.06285) +* [Neural Networks Assisted Metropolis-Hastings for Bayesian Estimation of Critical Exponent on Elliptic Black Hole Solution in 4D Using Quantum Perturbation Theory](https://arxiv.org/abs/2406.04310) +* [Learning to see R-parity violating scalar top decays](https://arxiv.org/abs/2406.03096) +* [Generative Diffusion Models for Fast Simulations of Particle Collisions at CERN](https://arxiv.org/abs/2406.03233) +* [A generalized statistical model for fits to parton distributions](https://arxiv.org/abs/2406.01664) +* [NNPDF4.0 aN$^3$LO PDFs with QED corrections](https://arxiv.org/abs/2406.01779) + ## May 2024 +* [Parnassus: An Automated Approach to Accurate, Precise, and Fast Detector Simulation and Reconstruction](https://arxiv.org/abs/2406.01620) +* [Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows](https://arxiv.org/abs/2405.20407) +* [A simultaneous unbinned differential cross section measurement of twenty-four $Z$+jets kinematic observables with the ATLAS detector](https://arxiv.org/abs/2405.20041) +* [Neural Scaling Laws From Large-N Field Theory: Solvable Model Beyond the Ridgeless Limit](https://arxiv.org/abs/2405.19398) +* [Exploring Exotic Decays of the Higgs Boson to Multi-Photons at the LHC via Multimodal Learning Approaches](https://arxiv.org/abs/2405.18834) * [Improving Neutrino Energy Reconstruction with Machine Learning](https://arxiv.org/abs/2405.15867) * [Constraining the Higgs Potential with Neural Simulation-based Inference for Di-Higgs Production](https://arxiv.org/abs/2405.15847) * [Boosting probes of CP violation in the top Yukawa coupling with Deep Learning](https://arxiv.org/abs/2405.16499) @@ -41,69 +125,3 @@ This is an automatically compiled list of papers which have been added to the li * [Measurement of atmospheric neutrino oscillation parameters using convolutional neural networks with 9.3 years of data in IceCube DeepCore](https://arxiv.org/abs/2405.02163) * [Search for new resonances decaying to pairs of merged diphotons in proton-proton collisions at $\sqrt{s}$](https://arxiv.org/abs/2405.00834) -## April 2024 -* [Unifying Simulation and Inference with Normalizing Flows](https://arxiv.org/abs/2404.18992) -* [Exploring Transport Properties of Quark-Gluon Plasma with a Machine-Learning assisted Holographic Approach](https://arxiv.org/abs/2404.18217) -* [The Landscape of Unfolding with Machine Learning](https://arxiv.org/abs/2404.18807) -* [Bayesian Active Search on Parameter Space: a 95 GeV Spin-0 Resonance in the ($B-L$)SSM](https://arxiv.org/abs/2404.18653) -* [Flow-based Nonperturbative Simulation of First-order Phase Transitions](https://arxiv.org/abs/2404.18323) -* [Classical integrability in the presence of a cosmological constant: analytic and machine learning results](https://arxiv.org/abs/2404.18247) -* [BUFF: Boosted Decision Tree based Ultra-Fast Flow matching](https://arxiv.org/abs/2404.18219) -* [OmniLearn: A Method to Simultaneously Facilitate All Jet Physics Tasks](https://arxiv.org/abs/2404.16091) -* [Robust Independent Validation of Experiment and Theory: Rivet version 4 release note](https://arxiv.org/abs/2404.15984) -* [Foundations of automatic feature extraction at LHC--point clouds and graphs](https://arxiv.org/abs/2404.16207) -* [Using analytic models to describe effective PDFs](https://arxiv.org/abs/2404.15175) -* [Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusion](https://arxiv.org/abs/2404.14332) -* [Search for a resonance decaying into a scalar particle and a Higgs boson in the final state with two bottom quarks and two photons in proton-proton collisions at a center of mass energy of 13 TeV with the ATLAS detector](https://arxiv.org/abs/2404.12915) -* [On Machine Learning Complete Intersection Calabi-Yau 3-folds](https://arxiv.org/abs/2404.11710) -* [Magnetic Monopole Phenomenology at Future Hadron Colliders](https://arxiv.org/abs/2404.10871) -* [Strategies for Machine Learning Applied to Noisy HEP Datasets: Modular Solid State Detectors from SuperCDMS](https://arxiv.org/abs/2404.10971) -* [A machine learning-based study of open-charm hadrons in proton-proton collisions at the Large Hadron Collider](https://arxiv.org/abs/2404.09839) -* [Xiwu: A Basis Flexible and Learnable LLM for High Energy Physics](https://arxiv.org/abs/2404.08001) -* [Search for Higgs Boson Pair Production with One Associated Vector Boson in Proton-Proton Collisions at $\sqrt{s}$](https://arxiv.org/abs/2404.08462) -* [Complete Optimal Non-Resonant Anomaly Detection](https://arxiv.org/abs/2404.07258) -* [Determination of $K^0_S$ Fragmentation Functions including BESIII Measurements and using Neural Networks](https://arxiv.org/abs/2404.07334) -* [Trials Factor for Semi-Supervised NN Classifiers in Searches for Narrow Resonances at the LHC](https://arxiv.org/abs/2404.07822) -* [Physics Event Classification Using Large Language Models](https://arxiv.org/abs/2404.05752) -* [Boosted four-top production at the LHC : a window to Randall-Sundrum or extended color symmetry](https://arxiv.org/abs/2404.04409) -* [Helicity-dependent parton distribution functions at next-to-next-to-leading order accuracy from inclusive and semi-inclusive deep-inelastic scattering data](https://arxiv.org/abs/2404.04712) -* [Deep learning for flow observables in high energy heavy-ion collisions](https://arxiv.org/abs/2404.02602) -* [Probing intractable beyond-standard-model parameter spaces armed with Machine Learning](https://arxiv.org/abs/2404.02698) -* [Machine Learning in High Energy Physics: A review of heavy-flavor jet tagging at the LHC](https://arxiv.org/abs/2404.01071) - -## March 2024 -* [Meson mass and width: Deep learning approach](https://arxiv.org/abs/2404.00448) -* [Differentiable nuclear deexcitation simulation for low energy neutrino physics](https://arxiv.org/abs/2404.00180) -* [Probing Heavy Charged Higgs Boson Using Multivariate Technique at Gamma-Gamma Collider](https://arxiv.org/abs/2403.20293) -* [Feynman Diagrams as Computational Graphs](https://arxiv.org/abs/2403.18840) -* [A Deep Learning Framework for Disentangling Triangle Singularity and Pole-Based Enhancements](https://arxiv.org/abs/2403.18265) -* [One flow to correct them all: improving simulations in high-energy physics with a single normalising flow and a switch](https://arxiv.org/abs/2403.18582) -* [Particle identification with machine learning from incomplete data in the ALICE experiment](https://arxiv.org/abs/2403.17436) -* [Deep Probabilistic Direction Prediction in 3D with Applications to Directional Dark Matter Detectors](https://arxiv.org/abs/2403.15949) -* [Predicting Feynman periods in $\phi^4$-theory](https://arxiv.org/abs/2403.16217) -* [CaloPointFlow II Generating Calorimeter Showers as Point Clouds](https://arxiv.org/abs/2403.15782) -* [Gravitational Duals from Equations of State](https://arxiv.org/abs/2403.14763) -* [Normalizing Flows for Domain Adaptation when Identifying $\Lambda$ Hyperon Events](https://arxiv.org/abs/2403.14804) -* [Improving $\Lambda$ Signal Extraction with Domain Adaptation via Normalizing Flows](https://arxiv.org/abs/2403.14076) -* [Quantum chaos in the sparse SYK model](https://arxiv.org/abs/2403.13884) -* [ML-based Calibration and Control of the GlueX Central Drift Chamber](https://arxiv.org/abs/2403.13823) -* [Deep Generative Models for Ultra-High Granularity Particle Physics Detector Simulation: A Voyage From Emulation to Extrapolation](https://arxiv.org/abs/2403.13825) -* [Unsupervised and lightly supervised learning in particle physics](https://arxiv.org/abs/2403.13676) -* [Improving tracking algorithms with machine learning: a case for line-segment tracking at the High Luminosity LHC](https://arxiv.org/abs/2403.13166) -* [CapsLorentzNet: Integrating Physics Inspired Features with Graph Convolution](https://arxiv.org/abs/2403.11826) -* [High-energy physics image classification: A Survey of Jet Applications](https://arxiv.org/abs/2403.11934) -* [NuGraph2: A Graph Neural Network for Neutrino Physics Event Reconstruction](https://arxiv.org/abs/2403.11872) -* [Neural network representation of quantum systems](https://arxiv.org/abs/2403.11420) -* [Moments of Clarity: Streamlining Latent Spaces in Machine Learning using Moment Pooling](https://arxiv.org/abs/2403.08854) -* [Exploration at the high-energy frontier: ATLAS Run 2 searches investigating the exotic jungle beyond the Standard Model](https://arxiv.org/abs/2403.09292) -* [Re-Simulation-based Self-Supervised Learning for Pre-Training Foundation Models](https://arxiv.org/abs/2403.07066) -* [Dark Matter-induced electron excitations in silicon and germanium with Deep Learning](https://arxiv.org/abs/2403.07053) -* [OmniJet-$\alpha$: The first cross-task foundation model for particle physics](https://arxiv.org/abs/2403.05618) -* [New Pathways in Neutrino Physics via Quantum-Encoded Data Analysis](https://arxiv.org/abs/2402.19306) -* [Heavy quarkonium spectral function in an anisotropic background](https://arxiv.org/abs/2403.04966) [[DOI](https://doi.org/10.1103/PhysRevD.109.086010)] -* [Jet Discrimination with Quantum Complete Graph Neural Network](https://arxiv.org/abs/2403.04990) -* [Observation of electroweak production of $W^+W^-$ in association with jets in proton-proton collisions at $\sqrt{s}](https://arxiv.org/abs/2403.04869) -* [Real-Time Charged Track Reconstruction for CLAS12](https://arxiv.org/abs/2403.04020) -* [Neural Network Learning and Quantum Gravity](https://arxiv.org/abs/2403.03245) -* [Higgs couplings in SMEFT via Zh production at the HL-LHC](https://arxiv.org/abs/2403.03001) -