From 3fa500ed3b9a8c2d80958743788e377635829e24 Mon Sep 17 00:00:00 2001 From: Ramon Winterhalder Date: Tue, 22 Aug 2023 14:56:54 +0200 Subject: [PATCH] Update nucl-th from may and june --- HEPML.bib | 140 +++++++++++++++++++++++++++++++++++++++++++++++-- HEPML.tex | 10 ++-- README.md | 16 +++++- docs/index.md | 16 +++++- docs/recent.md | 9 +++- 5 files changed, 179 insertions(+), 12 deletions(-) diff --git a/HEPML.bib b/HEPML.bib index 64424fb..96dbf14 100644 --- a/HEPML.bib +++ b/HEPML.bib @@ -314,6 +314,16 @@ @article{Kronheim:2023jrl } % Jun. 23, 2023 +@article{Wen:2023oju, + author = "Wen, Pengsheng and Holt, Jeremy W. and Li, Maggie", + title = "{Generative modeling of nucleon-nucleon interactions}", + eprint = "2306.13007", + archivePrefix = "arXiv", + primaryClass = "nucl-th", + month = "6", + year = "2023" +} + @article{Dubinski:2023fsy, author = "Dubi\'nski, Jan and Deja, Kamil and Wenzel, Sandro and Rokita, Przemys\l{}aw and Trzci\'nski, Tomasz", title = "{Machine Learning methods for simulating particle response in the Zero Degree Calorimeter at the ALICE experiment, CERN}", @@ -336,6 +346,27 @@ @article{Anzalone:2023ugq } % Jun. 21, 2023 +@article{Hizawa:2023plv, + author = "Hizawa, N. and Hagino, K. and Yoshida, K.", + title = "{Analysis of a Skyrme energy density functional with deep learning}", + eprint = "2306.11314", + archivePrefix = "arXiv", + primaryClass = "nucl-th", + reportNumber = "KUNS-2970", + month = "6", + year = "2023" +} + +@article{Liu:2023xgl, + author = "Liu, Siyu and Gao, Zepeng and Liao, Zehong and Yang, Yu and Su, Jun and Wang, Yongjia and Zhu, Long", + title = "{Constraining the Woods-Saxon potential in fusion reactions based on a physics-informed neural network}", + eprint = "2306.11236", + archivePrefix = "arXiv", + primaryClass = "nucl-th", + month = "6", + year = "2023" +} + @article{Heinrich:2023bmt, author = "Heinrich, Lukas and Mishra-Sharma, Siddharth and Pollard, Chris and Windischhofer, Philipp", title = "{Hierarchical Neural Simulation-Based Inference Over Event Ensembles}", @@ -379,6 +410,41 @@ @article{Knipfer:2023zrv year = "2023" } +% Jun. 16, 2023 +@article{Yoshida:2023wrb, + author = "Yoshida, Sota", + title = "{IMSRG-Net: A machine learning-based solver for In-Medium Similarity Renormalization Group}", + eprint = "2306.08878", + archivePrefix = "arXiv", + primaryClass = "nucl-th", + month = "6", + year = "2023" +} + +@article{Lasseri:2023dhi, + author = {Lasseri, Rapha\"el-David and Regnier, David and Frosini, Mika\"el and Verriere, Marc and Schunck, Nicolas}, + title = "{Generative deep-learning reveals collective variables of Fermionic systems}", + eprint = "2306.08348", + archivePrefix = "arXiv", + primaryClass = "nucl-th", + month = "6", + year = "2023" +} + +@article{Cai:2023gol, + author = "Cai, Bao-Jun and Li, Bao-An and Zhang, Zhen", + title = "{Core States of Neutron Stars from Anatomizing Their Scaled Structure Equations}", + eprint = "2306.08202", + archivePrefix = "arXiv", + primaryClass = "nucl-th", + doi = "10.3847/1538-4357/acdef0", + journal = "Astrophys. J.", + volume = "952", + number = "2", + pages = "147", + year = "2023" +} + % Jun. 14, 2023 @article{Grossi:2023fqq, author = "Grossi, Michele and Incudini, Massimiliano and Pellen, Mathieu and Pelliccioli, Giovanni", @@ -390,6 +456,29 @@ @article{Grossi:2023fqq year = "2023" } +% Jun. 13, 2023 +@article{Carvalho:2023ele, + author = "Carvalho, Val\'eria and Ferreira, M\'arcio and Malik, Tuhin and Provid\^encia, Constan\c{c}a", + title = "{Decoding Neutron Star Observations: Revealing Composition through Bayesian Neural Networks}", + eprint = "2306.06929", + archivePrefix = "arXiv", + primaryClass = "nucl-th", + month = "6", + year = "2023" +} + +% June 08, 2023 +@article{Yiu:2023ido, + author = "Yiu, To Chung and Liang, Haozhao and Lee, Jenny", + title = "{Nuclear mass predictions based on deep neural network and finite-range droplet model (2012)}", + eprint = "2306.04171", + archivePrefix = "arXiv", + primaryClass = "nucl-th", + reportNumber = "RIKEN-iTHEMS-Report-23", + month = "6", + year = "2023" +} + % June 07, 2023 @article{Zuniga-Galindo:2023uwp, author = "Z\'u\~niga-Galindo, W. A.", @@ -488,6 +577,21 @@ @article{Chan:2023ume year = "2023" } +%May 29, 2023 +@article{Wang:2023kcg, + author = "Wang, Yongjia and Li, Qingfeng", + title = "{Machine learning transforms the inference of the nuclear equation of state}", + eprint = "2305.16686", + archivePrefix = "arXiv", + primaryClass = "nucl-th", + doi = "10.1007/s11467-023-1313-3", + journal = "Front. Phys. (Beijing)", + volume = "18", + number = "6", + pages = "64402", + year = "2023" +} + %May 26, 2023 @article{Singh:2023yvj, author = "Singh, Jaswant and Toll, Tobias", @@ -639,13 +743,17 @@ @article{Nachman:2023clf year = "2023" } -@article{Hammal:2023njz, - author = "Hammal, O. Al and Martini, M. and Frontera-Pons, J. and Nguyen, T. H. and Perez-Ramos, R.", - title = "{Neural Network predictions of inclusive electron-nucleus cross sections}", +@article{AlHammal:2023svo, + author = "Al Hammal, O. and Martini, M. and Frontera-Pons, J. and Nguyen, T. H. and P\'erez-Ramos, R.", + title = "{Neural network predictions of inclusive electron-nucleus cross sections}", eprint = "2305.08217", archivePrefix = "arXiv", primaryClass = "nucl-th", - month = "5", + doi = "10.1103/PhysRevC.107.065501", + journal = "Phys. Rev. C", + volume = "107", + number = "6", + pages = "065501", year = "2023" } @@ -673,6 +781,16 @@ @article{Buhmann:2023bwk year = "2023" } +@article{Dellen:2023avd, + author = "Dellen, Babette and Jaekel, Uwe and Freitas, Paulo S. A. and Clark, John W.", + title = "{Predicting nuclear masses with product-unit networks}", + eprint = "2305.04675", + archivePrefix = "arXiv", + primaryClass = "nucl-th", + month = "5", + year = "2023" +} + % May 8, 2023 @article{Sengupta:2023xqy, author = "Sengupta, Debajyoti and Klein, Samuel and Raine, John Andrew and Golling, Tobias", @@ -684,6 +802,20 @@ @article{Sengupta:2023xqy year = "2023" } +@article{Zhou:2023cfs, + author = "Zhou, Wenjie and Hu, Jinniu and Zhang, Ying and Shen, Hong", + title = "{Nonparametric Model for the Equations of State of a Neutron Star from Deep Neural Network}", + eprint = "2305.03323", + archivePrefix = "arXiv", + primaryClass = "nucl-th", + doi = "10.3847/1538-4357/acd335", + journal = "Astrophys. J.", + volume = "950", + number = "2", + pages = "186", + year = "2023" +} + %May 7, 2023 @article{Aguilar-Saavedra:2023pde, author = "Aguilar-Saavedra, J. A. and Arganda, E. and Joaquim, F. R. and Sand\'a Seoane, R. M. and Seabra, J. F.", diff --git a/HEPML.tex b/HEPML.tex index b8cfed8..8b14631 100644 --- a/HEPML.tex +++ b/HEPML.tex @@ -94,11 +94,11 @@ \\\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} \\\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,Kim:2023wuk} + \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,Kim:2023wuk,Zhou:2023cfs,Carvalho:2023ele,Cai:2023gol} \\\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} \\\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,Mallick:2022alr,Steffanic:2023cyx,Mallick:2023vgi,Hirvonen:2023lqy,Biro:2023kyx,He:2023zin,Zhou:2023pti,CrispimRomao:2023ssj,Basak:2023wzq,Shi:2023xfz,Soleymaninia:2023dds,Lin:2023bmy,Wang:2023muv,Ai:2023azx,Karmakar:2023mhy} + \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,Mallick:2022alr,Steffanic:2023cyx,Mallick:2023vgi,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} \\\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} @@ -145,7 +145,7 @@ \\\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} \\\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,Qiu:2023ihi,Hammal:2023njz,Shi:2023xfz} + \item \textbf{Parameter estimation}~\cite{Lei:2020ucb,1808105,Lazzarin:2020uvv,Kim:2021pcz,Alda:2021rgt,Craven:2021ems,Castro:2022zpq,Meng:2022lmd,Qiu:2023ihi,AlHammal:2023svo,Shi:2023xfz} \\\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} \\\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.} @@ -167,9 +167,9 @@ \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,Diefenbacher:2023prl,Chan:2023ume,Dubinski:2023fsy,Alghamdi:2023emm} \\\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{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} + \item \textbf{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} \\\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} + \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} \\\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,Imani:2023blb,Amram:2023onf} \\\textit{These approaches learn the gradient of the density instead of the density directly.} diff --git a/README.md b/README.md index 1239c0c..55e3cd4 100644 --- a/README.md +++ b/README.md @@ -485,6 +485,9 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [Inferring subhalo effective density slopes from strong lensing observations with neural likelihood-ratio estimation](https://arxiv.org/abs/2208.13796) [[DOI](https://doi.org/10.1093/mnras/stac3014)] * [Uncovering dark matter density profiles in dwarf galaxies with graph neural networks](https://arxiv.org/abs/2208.12825) * [Probing Cosmological Particle Production and Pairwise Hotspots with Deep Neural Networks](https://arxiv.org/abs/2303.08869) +* [Nonparametric Model for the Equations of State of a Neutron Star from Deep Neural Network](https://arxiv.org/abs/2305.03323) [[DOI](https://doi.org/10.3847/1538-4357/acd335)] +* [Decoding Neutron Star Observations: Revealing Composition through Bayesian Neural Networks](https://arxiv.org/abs/2306.06929) +* [Core States of Neutron Stars from Anatomizing Their Scaled Structure Equations](https://arxiv.org/abs/2306.08202) [[DOI](https://doi.org/10.3847/1538-4357/acdef0)] #### Tracking @@ -567,9 +570,18 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [$\Sigma$ Resonances from a Neural Network-based Partial Wave Analysis on $K^-p$ Scattering](https://arxiv.org/abs/2305.01852) * [Nuclear corrections on the charged hadron fragmentation functions in a Neural Network global QCD analysis](https://arxiv.org/abs/2305.02664) * [Demonstration of Sub-micron UCN Position Resolution using Room-temperature CMOS Sensor](https://arxiv.org/abs/2305.09562) +* [Predicting nuclear masses with product-unit networks](https://arxiv.org/abs/2305.04675) +* [Neural network predictions of inclusive electron-nucleus cross sections](https://arxiv.org/abs/2305.08217) [[DOI](https://doi.org/10.1103/PhysRevC.107.065501)] * [A machine learning study to identify collective flow in small and large colliding systems](https://arxiv.org/abs/2305.09937) +* [Machine learning transforms the inference of the nuclear equation of state](https://arxiv.org/abs/2305.16686) [[DOI](https://doi.org/10.1007/s11467-023-1313-3)] * [Label-free timing analysis of modularized nuclear detectors with physics-constrained deep learning](https://arxiv.org/abs/2304.11930) +* [Nuclear mass predictions based on deep neural network and finite-range droplet model (2012)](https://arxiv.org/abs/2306.04171) * [Neutron-Gamma Pulse Shape Discrimination for Organic Scintillation Detector using 2D CNN based Image Classification](https://arxiv.org/abs/2306.09356) +* [Generative deep-learning reveals collective variables of Fermionic systems](https://arxiv.org/abs/2306.08348) +* [IMSRG-Net: A machine learning-based solver for In-Medium Similarity Renormalization Group](https://arxiv.org/abs/2306.08878) +* [Constraining the Woods-Saxon potential in fusion reactions based on a physics-informed neural network](https://arxiv.org/abs/2306.11236) +* [Analysis of a Skyrme energy density functional with deep learning](https://arxiv.org/abs/2306.11314) +* [Generative modeling of nucleon-nucleon interactions](https://arxiv.org/abs/2306.13007) ### Learning strategies @@ -828,7 +840,7 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [LHC EFT WG Report: Experimental Measurements and Observables](https://arxiv.org/abs/2211.08353) * [Machine Learning Assisted Vector Atomic Magnetometry](https://arxiv.org/abs/2301.05707) * [Parton Labeling without Matching: Unveiling Emergent Labelling Capabilities in Regression Models](https://arxiv.org/abs/2304.09208) -* [Neural Network predictions of inclusive electron-nucleus cross sections](https://arxiv.org/abs/2305.08217) +* [Neural network predictions of inclusive electron-nucleus cross sections](https://arxiv.org/abs/2305.08217) [[DOI](https://doi.org/10.1103/PhysRevC.107.065501)] * [$\Sigma$ Resonances from a Neural Network-based Partial Wave Analysis on $K^-p$ Scattering](https://arxiv.org/abs/2305.01852) ### Parton Distribution Functions (and related) @@ -1065,6 +1077,7 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds](https://arxiv.org/abs/2211.15380) * [Nanosecond anomaly detection with decision trees for high energy physics and real-time application to exotic Higgs decays](https://arxiv.org/abs/2304.03836) * [Triggering Dark Showers with Conditional Dual Auto-Encoders](https://arxiv.org/abs/2306.12955) +* [Generative deep-learning reveals collective variables of Fermionic systems](https://arxiv.org/abs/2306.08348) ### Normalizing flows @@ -1112,6 +1125,7 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [ELSA - Enhanced latent spaces for improved collider simulations](https://arxiv.org/abs/2305.07696) * [$\nu^2$-Flows: Fast and improved neutrino reconstruction in multi-neutrino final states with conditional normalizing flows](https://arxiv.org/abs/2307.02405) * [The Interplay of Machine Learning--based Resonant Anomaly Detection Methods](https://arxiv.org/abs/2307.11157) +* [Generative modeling of nucleon-nucleon interactions](https://arxiv.org/abs/2306.13007) ### Diffusion Models diff --git a/docs/index.md b/docs/index.md index 74b16ed..f19766c 100644 --- a/docs/index.md +++ b/docs/index.md @@ -541,6 +541,9 @@ const expandElements = shouldExpand => { * [Inferring subhalo effective density slopes from strong lensing observations with neural likelihood-ratio estimation](https://arxiv.org/abs/2208.13796) [[DOI](https://doi.org/10.1093/mnras/stac3014)] * [Uncovering dark matter density profiles in dwarf galaxies with graph neural networks](https://arxiv.org/abs/2208.12825) * [Probing Cosmological Particle Production and Pairwise Hotspots with Deep Neural Networks](https://arxiv.org/abs/2303.08869) + * [Nonparametric Model for the Equations of State of a Neutron Star from Deep Neural Network](https://arxiv.org/abs/2305.03323) [[DOI](https://doi.org/10.3847/1538-4357/acd335)] + * [Decoding Neutron Star Observations: Revealing Composition through Bayesian Neural Networks](https://arxiv.org/abs/2306.06929) + * [Core States of Neutron Stars from Anatomizing Their Scaled Structure Equations](https://arxiv.org/abs/2306.08202) [[DOI](https://doi.org/10.3847/1538-4357/acdef0)] #### Tracking @@ -623,9 +626,18 @@ const expandElements = shouldExpand => { * [$\Sigma$ Resonances from a Neural Network-based Partial Wave Analysis on $K^-p$ Scattering](https://arxiv.org/abs/2305.01852) * [Nuclear corrections on the charged hadron fragmentation functions in a Neural Network global QCD analysis](https://arxiv.org/abs/2305.02664) * [Demonstration of Sub-micron UCN Position Resolution using Room-temperature CMOS Sensor](https://arxiv.org/abs/2305.09562) + * [Predicting nuclear masses with product-unit networks](https://arxiv.org/abs/2305.04675) + * [Neural network predictions of inclusive electron-nucleus cross sections](https://arxiv.org/abs/2305.08217) [[DOI](https://doi.org/10.1103/PhysRevC.107.065501)] * [A machine learning study to identify collective flow in small and large colliding systems](https://arxiv.org/abs/2305.09937) + * [Machine learning transforms the inference of the nuclear equation of state](https://arxiv.org/abs/2305.16686) [[DOI](https://doi.org/10.1007/s11467-023-1313-3)] * [Label-free timing analysis of modularized nuclear detectors with physics-constrained deep learning](https://arxiv.org/abs/2304.11930) + * [Nuclear mass predictions based on deep neural network and finite-range droplet model (2012)](https://arxiv.org/abs/2306.04171) * [Neutron-Gamma Pulse Shape Discrimination for Organic Scintillation Detector using 2D CNN based Image Classification](https://arxiv.org/abs/2306.09356) + * [Generative deep-learning reveals collective variables of Fermionic systems](https://arxiv.org/abs/2306.08348) + * [IMSRG-Net: A machine learning-based solver for In-Medium Similarity Renormalization Group](https://arxiv.org/abs/2306.08878) + * [Constraining the Woods-Saxon potential in fusion reactions based on a physics-informed neural network](https://arxiv.org/abs/2306.11236) + * [Analysis of a Skyrme energy density functional with deep learning](https://arxiv.org/abs/2306.11314) + * [Generative modeling of nucleon-nucleon interactions](https://arxiv.org/abs/2306.13007) ??? example "Learning strategies" @@ -919,7 +931,7 @@ const expandElements = shouldExpand => { * [LHC EFT WG Report: Experimental Measurements and Observables](https://arxiv.org/abs/2211.08353) * [Machine Learning Assisted Vector Atomic Magnetometry](https://arxiv.org/abs/2301.05707) * [Parton Labeling without Matching: Unveiling Emergent Labelling Capabilities in Regression Models](https://arxiv.org/abs/2304.09208) - * [Neural Network predictions of inclusive electron-nucleus cross sections](https://arxiv.org/abs/2305.08217) + * [Neural network predictions of inclusive electron-nucleus cross sections](https://arxiv.org/abs/2305.08217) [[DOI](https://doi.org/10.1103/PhysRevC.107.065501)] * [$\Sigma$ Resonances from a Neural Network-based Partial Wave Analysis on $K^-p$ Scattering](https://arxiv.org/abs/2305.01852) @@ -1197,6 +1209,7 @@ const expandElements = shouldExpand => { * [CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds](https://arxiv.org/abs/2211.15380) * [Nanosecond anomaly detection with decision trees for high energy physics and real-time application to exotic Higgs decays](https://arxiv.org/abs/2304.03836) * [Triggering Dark Showers with Conditional Dual Auto-Encoders](https://arxiv.org/abs/2306.12955) + * [Generative deep-learning reveals collective variables of Fermionic systems](https://arxiv.org/abs/2306.08348) ??? example "Normalizing flows" @@ -1249,6 +1262,7 @@ const expandElements = shouldExpand => { * [ELSA - Enhanced latent spaces for improved collider simulations](https://arxiv.org/abs/2305.07696) * [$\nu^2$-Flows: Fast and improved neutrino reconstruction in multi-neutrino final states with conditional normalizing flows](https://arxiv.org/abs/2307.02405) * [The Interplay of Machine Learning--based Resonant Anomaly Detection Methods](https://arxiv.org/abs/2307.11157) + * [Generative modeling of nucleon-nucleon interactions](https://arxiv.org/abs/2306.13007) ??? example "Diffusion Models" diff --git a/docs/recent.md b/docs/recent.md index bec6e3f..b2e0a6f 100644 --- a/docs/recent.md +++ b/docs/recent.md @@ -42,13 +42,20 @@ This is an automatically compiled list of papers which have been added to the li ## June 2023 * [Black holes and the loss landscape in machine learning](https://arxiv.org/abs/2306.14817) * [Implicit Quantile Networks For Emulation in Jet Physics](https://arxiv.org/abs/2306.15053) +* [Generative modeling of nucleon-nucleon interactions](https://arxiv.org/abs/2306.13007) * [Machine Learning methods for simulating particle response in the Zero Degree Calorimeter at the ALICE experiment, CERN](https://arxiv.org/abs/2306.13606) * [Triggering Dark Showers with Conditional Dual Auto-Encoders](https://arxiv.org/abs/2306.12955) +* [Analysis of a Skyrme energy density functional with deep learning](https://arxiv.org/abs/2306.11314) +* [Constraining the Woods-Saxon potential in fusion reactions based on a physics-informed neural network](https://arxiv.org/abs/2306.11236) * [Hierarchical Neural Simulation-Based Inference Over Event Ensembles](https://arxiv.org/abs/2306.12584) * [Principles for Initialization and Architecture Selection in Graph Neural Networks with ReLU Activations](https://arxiv.org/abs/2306.11668) * [Neutron-Gamma Pulse Shape Discrimination for Organic Scintillation Detector using 2D CNN based Image Classification](https://arxiv.org/abs/2306.09356) * [Deep Learning-Based Spatiotemporal Multi-Event Reconstruction for Delay Line Detectors](https://arxiv.org/abs/2306.09359) +* [IMSRG-Net: A machine learning-based solver for In-Medium Similarity Renormalization Group](https://arxiv.org/abs/2306.08878) +* [Generative deep-learning reveals collective variables of Fermionic systems](https://arxiv.org/abs/2306.08348) * [Amplitude-assisted tagging of longitudinally polarised bosons using wide neural networks](https://arxiv.org/abs/2306.07726) +* [Decoding Neutron Star Observations: Revealing Composition through Bayesian Neural Networks](https://arxiv.org/abs/2306.06929) +* [Nuclear mass predictions based on deep neural network and finite-range droplet model (2012)](https://arxiv.org/abs/2306.04171) * [A Correspondence Between Deep Boltzmann Machines and p-Adic Statistical Field Theories](https://arxiv.org/abs/2306.03751) * [High-dimensional and Permutation Invariant Anomaly Detection](https://arxiv.org/abs/2306.03933) * [Combining lattice QCD and phenomenological inputs on generalised parton distributions at moderate skewness](https://arxiv.org/abs/2306.01647) @@ -74,9 +81,9 @@ This is an automatically compiled list of papers which have been added to the li * [Assessment of few-hits machine learning classification algorithms for low energy physics in liquid argon detectors](https://arxiv.org/abs/2305.09744) * [Demonstration of Sub-micron UCN Position Resolution using Room-temperature CMOS Sensor](https://arxiv.org/abs/2305.09562) * [ELSA - Enhanced latent spaces for improved collider simulations](https://arxiv.org/abs/2305.07696) -* [Neural Network predictions of inclusive electron-nucleus cross sections](https://arxiv.org/abs/2305.08217) * [Is infrared-collinear safe information all you need for jet classification?](https://arxiv.org/abs/2305.08979) * [CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation](https://arxiv.org/abs/2305.04847) +* [Predicting nuclear masses with product-unit networks](https://arxiv.org/abs/2305.04675) * [CURTAINs Flows For Flows: Constructing Unobserved Regions with Maximum Likelihood Estimation](https://arxiv.org/abs/2305.04646) * [Gradient Boosting MUST taggers for highly-boosted jets](https://arxiv.org/abs/2305.04957) * [Searching for dark jets with displaced vertices using weakly supervised machine learning](https://arxiv.org/abs/2305.04372)