From 3faf6b86d53c55610f9932ce9b3265fc97f16216 Mon Sep 17 00:00:00 2001 From: Jeff Jennings Date: Mon, 13 Nov 2023 14:06:51 -0500 Subject: [PATCH 01/18] paper template --- paper/paper.md | 82 ++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 82 insertions(+) create mode 100644 paper/paper.md diff --git a/paper/paper.md b/paper/paper.md new file mode 100644 index 00000000..ce9e968a --- /dev/null +++ b/paper/paper.md @@ -0,0 +1,82 @@ +--- +title: 'MPoL: A Python package for interferometric imaging' +tags: + - Python + - astronomy + - imaging + - fourier +authors: + - name: xx + orcid: 0000-0000-0000-0000 + equal-contrib: true + affiliation: "1, 2" # (Multiple affiliations must be quoted) + - name: Author Without ORCID + equal-contrib: true # (This is how you can denote equal contributions between multiple authors) + affiliation: 2 + - name: Author with no affiliation + corresponding: true # (This is how to denote the corresponding author) + affiliation: 3 +# - given-names: Ludwig +# dropping-particle: van +# surname: Beethoven +# affiliation: 3 +affiliations: + - name: xx + index: 1 + - name: Institution Name, Country + index: 2 +date: xx November 2023 +bibliography: paper.bib +--- + +# Summary + +# Statement of need + +`Million Points of Light` (`MPoL`) + +# Mathematics + + + +# Citations + + + +# Figures + + +# Acknowledgements + + + +# References \ No newline at end of file From c844976d3f814351d3bcf33e1c5d543ce33244de Mon Sep 17 00:00:00 2001 From: Jeff Jennings Date: Mon, 13 Nov 2023 22:54:20 -0500 Subject: [PATCH 02/18] joss paper: add bib entries --- paper/paper.bib | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) create mode 100644 paper/paper.bib diff --git a/paper/paper.bib b/paper/paper.bib new file mode 100644 index 00000000..43010db6 --- /dev/null +++ b/paper/paper.bib @@ -0,0 +1,25 @@ +@article{1974A&AS...15..417H, + author = {{H{\"o}gbom}, J.~A.}, + title = "{Aperture Synthesis with a Non-Regular Distribution of Interferometer Baselines}", + journal = {\aaps}, + year = 1974, + month = jun, + volume = 15, + pages = {417}, + adsurl = {https://ui.adsabs.harvard.edu/abs/1974A%26AS...15..417H}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} + +@book{2007ASPC..376..127M, + author = {{McMullin}, J.~P. and {Waters}, B. and {Schiebel}, D. and {Young}, W. and + {Golap}, K.}, + title = "{CASA Architecture and Applications}", + booktitle = {Astronomical Data Analysis Software and Systems XVI ASP Conference Series, Vol. 376, proceedings of the conference held 15-18 October 2006 in Tucson, Arizona, USA. Edited by Richard A. Shaw, Frank Hill and David J. Bell., p.127}, + year = "2007", + editor = {{Shaw}, R.~A. and {Hill}, F. and {Bell}, D.~J.}, + volume = {376}, + series = {Astronomical Society of the Pacific Conference Series}, + pages = {127}, + adsurl = {https://ui.adsabs.harvard.edu/abs/2007ASPC..376..127M}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} \ No newline at end of file From 539c9c20546fab05b45c3ab4655dc8214926125a Mon Sep 17 00:00:00 2001 From: Jeff Jennings Date: Mon, 13 Nov 2023 22:58:05 -0500 Subject: [PATCH 03/18] joss paper: authors --- paper/paper.md | 38 ++++++++++++++++++++++---------------- 1 file changed, 22 insertions(+), 16 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index ce9e968a..98f663a2 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -6,26 +6,32 @@ tags: - imaging - fourier authors: - - name: xx - orcid: 0000-0000-0000-0000 - equal-contrib: true - affiliation: "1, 2" # (Multiple affiliations must be quoted) - - name: Author Without ORCID - equal-contrib: true # (This is how you can denote equal contributions between multiple authors) + - name: Ian Czekala + orcid: 0000-0002-1483-8811 + # equal-contrib: true # (This is how you can denote equal contributions between multiple authors) + # corresponding: true + affiliation: 1 + # affiliation: "1, 2" # (Multiple affiliations must be quoted) + - name:Jeff Jennings + orcid: 0000-0002-7032-2350 + corresponding: true affiliation: 2 - - name: Author with no affiliation - corresponding: true # (This is how to denote the corresponding author) - affiliation: 3 -# - given-names: Ludwig -# dropping-particle: van -# surname: Beethoven -# affiliation: 3 + # - name:Brianna Zawadzki + # orcid: 0000-0001-9319-1296 + # affiliation: 3 + # - name:Ryan Loomis + # orcid: 0000-0002-8932-1219 + # affiliation: 4 affiliations: - - name: xx + - name: University of St Andrews, Scotland index: 1 - - name: Institution Name, Country + - name: Pennsylvania State University, USA index: 2 -date: xx November 2023 +# - name: Wesleyan University, USA +# index: 3 +# - name: National Radio Astronomy Observatory, USA +# index: 4 +date: 14 November 2023 bibliography: paper.bib --- From 5dab28b018db5c5f8475cc09f04c474016350984 Mon Sep 17 00:00:00 2001 From: Jeff Jennings Date: Mon, 13 Nov 2023 22:58:19 -0500 Subject: [PATCH 04/18] joss paper: summary --- paper/paper.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/paper/paper.md b/paper/paper.md index 98f663a2..b58699f1 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -37,6 +37,8 @@ bibliography: paper.bib # Summary +Interferometric imaging is the process of recovering a spatial domain image from a Fourier domain signal that is only partially sampled. The technique is applied in a large number of fields from medical imaging to remote sensing, optics and astronomy. Within astronomy, interferometry conducted at radio, infrared and optical frequencies yields unparalleled spatial resolution in an image, corresponding to physical scales that are otherwise inaccessibly small. `Million Points of Light` (`MPoL`) is a Python package for astronomical interferometric imaging. It couples a statistical modeling framework with an efficient computational implementation to reconstruct images of astronomical sources from data measured by large telescopes such as the Atacama Large Millimeter/Submillimeter Array (ALMA). + # Statement of need `Million Points of Light` (`MPoL`) From 4088da1cc01b0334197de220b4e987b682d20aa6 Mon Sep 17 00:00:00 2001 From: Jeff Jennings Date: Mon, 13 Nov 2023 22:59:09 -0500 Subject: [PATCH 05/18] joss paper: statement of need --- paper/paper.md | 42 +++--------------------------------------- 1 file changed, 3 insertions(+), 39 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index b58699f1..fae99f0a 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -41,47 +41,11 @@ Interferometric imaging is the process of recovering a spatial domain image from # Statement of need -`Million Points of Light` (`MPoL`) +Accurately reconstructing an image from sparse Fourier data is an ill-posed problem that remains an outstanding challenge in astronomical research, particularly in sub-mm astronomy. There, the current standard approach to interferometric imaging is `CLEAN` [@1974A&AS...15..417H], an empirical, algorithmic procedure that requires a high degree of user intervention. The algorithm is not computationally efficient and thus not practical for large datasets (~100 GB) that are becoming increasingly common in the field. And the enclosing software lacks the accessibility and up-to-date documentation to easily modify the algorithm for custom use cases [@2007ASPC..376..127M]. Collectively these limitations necessitate an alternative imaging formalism and software implementation. -# Mathematics +`MPoL` is a statistically robust, nonparametric modeling approach to interferometric imaging in a user-friendly, well-documented package that is computationally performant. The software is designed to be applied to reconstruction of an individual image or an entire 'cube' of tens to hundreds of images of an astronomical source observed at different frequencies. The images obtained are of simultaneously higher spatial resolution and sensitivity than their counterparts produced by `CLEAN`. Programatically, `MPoL` is built on `PyTorch`, using its auto-differentiation capabilities to drive likelihood optimization with gradient descent and its parallelization support to optionally accelerate the imaging workflow on GPUs and TPUs. The imaging framework in `MPoL` is also flexible, with the ability to easily add alternative or additional priors into likelihood calculation. Extensions to the core functionality are actively developed, such as the recent implementation of parametric inference with `Pyro`, as are further optimizations to the core routines. - - -# Citations - - - -# Figures - +`MPoL` is used in astrophysical research to image and study objects such as protoplanetary disks and Solar System bodies. It is currently being applied to multiple projects, from individual use cases to large collaborations. The software could be applied without modification to research in other subfields of astronomy that use data from sub-mm interferometers, including cosmology, extragalactic astronomy, and star formation. With a reasonable amount of modification, it could be adopted for datasets obtained by infrared and optical interferometers, or to interferometric imaging problems beyond astronomy. # Acknowledgements From 559554fa0039f0e17773a0857e3bcc796ae732c5 Mon Sep 17 00:00:00 2001 From: Jeff Jennings Date: Mon, 13 Nov 2023 22:59:21 -0500 Subject: [PATCH 06/18] joss paper: acknowledgements --- paper/paper.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index fae99f0a..10c16a1e 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -49,6 +49,6 @@ Accurately reconstructing an image from sparse Fourier data is an ill-posed prob # Acknowledgements - +We acknowledge contributions from Brianna Zawadzki, Ryan Loomis, Kadri Nizam, Megan Delamer, Kaylee de Soto, Robert Frazier, Hannah Grzybowski, Mary Ogborn, and Tyler Quinn. -# References \ No newline at end of file +# References From f284b8a1165b61615c60fb52b68070eece276545 Mon Sep 17 00:00:00 2001 From: Jeff Jennings Date: Mon, 13 Nov 2023 23:29:36 -0500 Subject: [PATCH 07/18] add workflow to test joss paper compile --- .github/workflows/draft-pdf.yml | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) create mode 100644 .github/workflows/draft-pdf.yml diff --git a/.github/workflows/draft-pdf.yml b/.github/workflows/draft-pdf.yml new file mode 100644 index 00000000..8c85897b --- /dev/null +++ b/.github/workflows/draft-pdf.yml @@ -0,0 +1,23 @@ +on: [push] + +jobs: + paper: + runs-on: ubuntu-latest + name: JOSS paper draft + steps: + - name: Checkout + uses: actions/checkout@v4 + - name: Build draft PDF + uses: openjournals/openjournals-draft-action@master + with: + journal: joss + # This should be the path to the paper within your repo. + paper-path: paper/paper.md + - name: Upload + uses: actions/upload-artifact@v1 + with: + name: paper + # This is the output path where Pandoc will write the compiled + # PDF. Note, this should be the same directory as the input + # paper.md + path: paper/paper.pdf \ No newline at end of file From c629df436e965e3ac3f9acbe6f423bb607523c40 Mon Sep 17 00:00:00 2001 From: Jeff Jennings Date: Tue, 14 Nov 2023 00:45:46 -0500 Subject: [PATCH 08/18] paper.md: typo --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index 10c16a1e..45d38254 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -12,7 +12,7 @@ authors: # corresponding: true affiliation: 1 # affiliation: "1, 2" # (Multiple affiliations must be quoted) - - name:Jeff Jennings + - name: Jeff Jennings orcid: 0000-0002-7032-2350 corresponding: true affiliation: 2 From 8654a155e07c00fffed82de6a06e2880f2f07fb7 Mon Sep 17 00:00:00 2001 From: Jeff Jennings Date: Tue, 14 Nov 2023 01:12:51 -0500 Subject: [PATCH 09/18] joss paper: add refs --- paper/paper.bib | 36 ++++++++++++++++++++++++++++++++++-- paper/paper.md | 6 +++--- 2 files changed, 37 insertions(+), 5 deletions(-) diff --git a/paper/paper.bib b/paper/paper.bib index 43010db6..26b3afd7 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -1,4 +1,4 @@ -@article{1974A&AS...15..417H, +@ARTICLE{Hogbom_1974, author = {{H{\"o}gbom}, J.~A.}, title = "{Aperture Synthesis with a Non-Regular Distribution of Interferometer Baselines}", journal = {\aaps}, @@ -10,7 +10,20 @@ @article{1974A&AS...15..417H adsnote = {Provided by the SAO/NASA Astrophysics Data System} } -@book{2007ASPC..376..127M, +@ARTICLE{Clark_1980, + author = {{Clark}, B.~G.}, + title = "{An efficient implementation of the algorithm 'CLEAN'}", + journal = {\aap}, + keywords = {Algorithms, Data Processing, Image Processing, Radio Astronomy, Run Time (Computers), Astronomical Photography, Computer Techniques, Fast Fourier Transformations, Iterative Solution, Very Large Array (Vla)}, + year = 1980, + month = sep, + volume = 89, + pages = {377}, + adsurl = {https://ui.adsabs.harvard.edu/abs/1980A%26A....89..377C}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} + +@INPROCEEDINGS{McMullin_2007, author = {{McMullin}, J.~P. and {Waters}, B. and {Schiebel}, D. and {Young}, W. and {Golap}, K.}, title = "{CASA Architecture and Applications}", @@ -22,4 +35,23 @@ @book{2007ASPC..376..127M pages = {127}, adsurl = {https://ui.adsabs.harvard.edu/abs/2007ASPC..376..127M}, adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} + +@ARTICLE{CASA_2022, + author = {{CASA Team} and {Bean}, Ben and {Bhatnagar}, Sanjay and {Castro}, Sandra and {Donovan Meyer}, Jennifer and {Emonts}, Bjorn and {Garcia}, Enrique and {Garwood}, Robert and {Golap}, Kumar and {Gonzalez Villalba}, Justo and {Harris}, Pamela and {Hayashi}, Yohei and {Hoskins}, Josh and {Hsieh}, Mingyu and {Jagannathan}, Preshanth and {Kawasaki}, Wataru and {Keimpema}, Aard and {Kettenis}, Mark and {Lopez}, Jorge and {Marvil}, Joshua and {Masters}, Joseph and {McNichols}, Andrew and {Mehringer}, David and {Miel}, Renaud and {Moellenbrock}, George and {Montesino}, Federico and {Nakazato}, Takeshi and {Ott}, Juergen and {Petry}, Dirk and {Pokorny}, Martin and {Raba}, Ryan and {Rau}, Urvashi and {Schiebel}, Darrell and {Schweighart}, Neal and {Sekhar}, Srikrishna and {Shimada}, Kazuhiko and {Small}, Des and {Steeb}, Jan-Willem and {Sugimoto}, Kanako and {Suoranta}, Ville and {Tsutsumi}, Takahiro and {van Bemmel}, Ilse M. and {Verkouter}, Marjolein and {Wells}, Akeem and {Xiong}, Wei and {Szomoru}, Arpad and {Griffith}, Morgan and {Glendenning}, Brian and {Kern}, Jeff}, + title = "{CASA, the Common Astronomy Software Applications for Radio Astronomy}", + journal = {\pasp}, + keywords = {Single-dish antennas, Aperture synthesis, Radio astronomy, Radio interferometry, Long baseline interferometry, Astronomy software, Open source software, Software documentation, Astronomy data reduction, Astronomy data analysis, 1460, 53, 1338, 1346, 932, 1855, 1866, 1869, 1861, 1858, Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Astrophysics of Galaxies, Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Solar and Stellar Astrophysics}, + year = 2022, + month = nov, + volume = {134}, + number = {1041}, + eid = {114501}, + pages = {114501}, + doi = {10.1088/1538-3873/ac9642}, +archivePrefix = {arXiv}, + eprint = {2210.02276}, + primaryClass = {astro-ph.IM}, + adsurl = {https://ui.adsabs.harvard.edu/abs/2022PASP..134k4501C}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} } \ No newline at end of file diff --git a/paper/paper.md b/paper/paper.md index 45d38254..56594382 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -1,5 +1,5 @@ --- -title: 'MPoL: A Python package for interferometric imaging' +title: 'MPoL: A Python package for scalable, nonparametric interferometric imaging' tags: - Python - astronomy @@ -14,7 +14,7 @@ authors: # affiliation: "1, 2" # (Multiple affiliations must be quoted) - name: Jeff Jennings orcid: 0000-0002-7032-2350 - corresponding: true + # corresponding: true affiliation: 2 # - name:Brianna Zawadzki # orcid: 0000-0001-9319-1296 @@ -41,7 +41,7 @@ Interferometric imaging is the process of recovering a spatial domain image from # Statement of need -Accurately reconstructing an image from sparse Fourier data is an ill-posed problem that remains an outstanding challenge in astronomical research, particularly in sub-mm astronomy. There, the current standard approach to interferometric imaging is `CLEAN` [@1974A&AS...15..417H], an empirical, algorithmic procedure that requires a high degree of user intervention. The algorithm is not computationally efficient and thus not practical for large datasets (~100 GB) that are becoming increasingly common in the field. And the enclosing software lacks the accessibility and up-to-date documentation to easily modify the algorithm for custom use cases [@2007ASPC..376..127M]. Collectively these limitations necessitate an alternative imaging formalism and software implementation. +Accurately reconstructing an image from sparse Fourier data is an ill-posed problem that remains an outstanding challenge in astronomical research, particularly in sub-mm astronomy. There, the current standard approach to interferometric imaging is `CLEAN` [@Hogbom_1974; @Clark_1980], an empirical, algorithmic procedure that requires a high degree of user intervention. The algorithm is not computationally efficient and thus not practical for large datasets (~100 GB) that are becoming increasingly common in the field. And the enclosing software lacks the accessibility and up-to-date documentation to easily modify the algorithm for custom use cases [@McMullin_2007; @CASA_2022]. Collectively these limitations necessitate an alternative imaging formalism and software implementation. `MPoL` is a statistically robust, nonparametric modeling approach to interferometric imaging in a user-friendly, well-documented package that is computationally performant. The software is designed to be applied to reconstruction of an individual image or an entire 'cube' of tens to hundreds of images of an astronomical source observed at different frequencies. The images obtained are of simultaneously higher spatial resolution and sensitivity than their counterparts produced by `CLEAN`. Programatically, `MPoL` is built on `PyTorch`, using its auto-differentiation capabilities to drive likelihood optimization with gradient descent and its parallelization support to optionally accelerate the imaging workflow on GPUs and TPUs. The imaging framework in `MPoL` is also flexible, with the ability to easily add alternative or additional priors into likelihood calculation. Extensions to the core functionality are actively developed, such as the recent implementation of parametric inference with `Pyro`, as are further optimizations to the core routines. From c2e9c1bc634302ff865f201052d4f9f395077ee5 Mon Sep 17 00:00:00 2001 From: Jeff Jennings Date: Tue, 14 Nov 2023 01:25:19 -0500 Subject: [PATCH 10/18] joss paper: bib full journal names --- paper/paper.bib | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/paper/paper.bib b/paper/paper.bib index 26b3afd7..4fe81318 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -1,7 +1,7 @@ @ARTICLE{Hogbom_1974, author = {{H{\"o}gbom}, J.~A.}, title = "{Aperture Synthesis with a Non-Regular Distribution of Interferometer Baselines}", - journal = {\aaps}, + journal = {Astronomy and Astrophysics Supplement}, year = 1974, month = jun, volume = 15, @@ -13,7 +13,7 @@ @ARTICLE{Hogbom_1974 @ARTICLE{Clark_1980, author = {{Clark}, B.~G.}, title = "{An efficient implementation of the algorithm 'CLEAN'}", - journal = {\aap}, + journal = {Astronomy and Astrophysics}, keywords = {Algorithms, Data Processing, Image Processing, Radio Astronomy, Run Time (Computers), Astronomical Photography, Computer Techniques, Fast Fourier Transformations, Iterative Solution, Very Large Array (Vla)}, year = 1980, month = sep, @@ -27,7 +27,7 @@ @INPROCEEDINGS{McMullin_2007 author = {{McMullin}, J.~P. and {Waters}, B. and {Schiebel}, D. and {Young}, W. and {Golap}, K.}, title = "{CASA Architecture and Applications}", - booktitle = {Astronomical Data Analysis Software and Systems XVI ASP Conference Series, Vol. 376, proceedings of the conference held 15-18 October 2006 in Tucson, Arizona, USA. Edited by Richard A. Shaw, Frank Hill and David J. Bell., p.127}, + booktitle = {Astronomical Data Analysis Software and Systems XVI ASP Conference Series}, year = "2007", editor = {{Shaw}, R.~A. and {Hill}, F. and {Bell}, D.~J.}, volume = {376}, @@ -40,7 +40,7 @@ @INPROCEEDINGS{McMullin_2007 @ARTICLE{CASA_2022, author = {{CASA Team} and {Bean}, Ben and {Bhatnagar}, Sanjay and {Castro}, Sandra and {Donovan Meyer}, Jennifer and {Emonts}, Bjorn and {Garcia}, Enrique and {Garwood}, Robert and {Golap}, Kumar and {Gonzalez Villalba}, Justo and {Harris}, Pamela and {Hayashi}, Yohei and {Hoskins}, Josh and {Hsieh}, Mingyu and {Jagannathan}, Preshanth and {Kawasaki}, Wataru and {Keimpema}, Aard and {Kettenis}, Mark and {Lopez}, Jorge and {Marvil}, Joshua and {Masters}, Joseph and {McNichols}, Andrew and {Mehringer}, David and {Miel}, Renaud and {Moellenbrock}, George and {Montesino}, Federico and {Nakazato}, Takeshi and {Ott}, Juergen and {Petry}, Dirk and {Pokorny}, Martin and {Raba}, Ryan and {Rau}, Urvashi and {Schiebel}, Darrell and {Schweighart}, Neal and {Sekhar}, Srikrishna and {Shimada}, Kazuhiko and {Small}, Des and {Steeb}, Jan-Willem and {Sugimoto}, Kanako and {Suoranta}, Ville and {Tsutsumi}, Takahiro and {van Bemmel}, Ilse M. and {Verkouter}, Marjolein and {Wells}, Akeem and {Xiong}, Wei and {Szomoru}, Arpad and {Griffith}, Morgan and {Glendenning}, Brian and {Kern}, Jeff}, title = "{CASA, the Common Astronomy Software Applications for Radio Astronomy}", - journal = {\pasp}, + journal = {Publications of the Astronomical Society of the Pacific}, keywords = {Single-dish antennas, Aperture synthesis, Radio astronomy, Radio interferometry, Long baseline interferometry, Astronomy software, Open source software, Software documentation, Astronomy data reduction, Astronomy data analysis, 1460, 53, 1338, 1346, 932, 1855, 1866, 1869, 1861, 1858, Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Astrophysics of Galaxies, Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Solar and Stellar Astrophysics}, year = 2022, month = nov, From a914b340d7a2d8c25d18dea398b2b9b35c7108db Mon Sep 17 00:00:00 2001 From: Jeff Jennings Date: Tue, 14 Nov 2023 03:26:49 -0500 Subject: [PATCH 11/18] joss paper: add authors --- paper/paper.md | 44 ++++++++++++++++++++++++++++++++------------ 1 file changed, 32 insertions(+), 12 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index 56594382..3cb13a5d 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -14,23 +14,43 @@ authors: # affiliation: "1, 2" # (Multiple affiliations must be quoted) - name: Jeff Jennings orcid: 0000-0002-7032-2350 - # corresponding: true affiliation: 2 - # - name:Brianna Zawadzki - # orcid: 0000-0001-9319-1296 - # affiliation: 3 - # - name:Ryan Loomis - # orcid: 0000-0002-8932-1219 - # affiliation: 4 + - name: Brianna Zawadzki + orcid: 0000-0001-9319-1296 + affiliation: 3 + - name: Ryan Loomis + orcid: 0000-0002-8932-1219 + affiliation: 4 + - name: Kadri Nizam + orcid: 0000-0002-7217-446X + affiliation: 2 + - name: Megan Delamer + # orcid: # can't find + affiliation: 2 + - name: Kaylee de Soto + orcid: 0000-0002-9886-2834 + affiliation: 2 + - name: Robert Frazier + orcid: 0000-0001-6569-3731 + affiliation: 2 + - name: Hannah Grzybowski + # orcid: # can't find + affiliation: 2 + - name: Mary Ogborn + orcid: 0000-0001-9741-2703 + affiliation: 2 + - name: Tyler Quinn + orcid: 0000-0002-8974-8095 + affiliation: 2 affiliations: - - name: University of St Andrews, Scotland + - name: University of St Andrews, Scotland # "institution, country" format suggested by JOSS index: 1 - name: Pennsylvania State University, USA index: 2 -# - name: Wesleyan University, USA -# index: 3 -# - name: National Radio Astronomy Observatory, USA -# index: 4 + - name: Wesleyan University, USA + index: 3 + - name: National Radio Astronomy Observatory, USA + index: 4 date: 14 November 2023 bibliography: paper.bib --- From 6e21b41f332b09bb46c4adaa74ba9926d4a701e0 Mon Sep 17 00:00:00 2001 From: Jeff Jennings Date: Tue, 14 Nov 2023 03:27:07 -0500 Subject: [PATCH 12/18] joss paper: name pdf compile workflow --- .github/workflows/draft-pdf.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/draft-pdf.yml b/.github/workflows/draft-pdf.yml index 8c85897b..ccc3e9c3 100644 --- a/.github/workflows/draft-pdf.yml +++ b/.github/workflows/draft-pdf.yml @@ -1,3 +1,5 @@ +name: build JOSS pdf + on: [push] jobs: From 684317e699ec28af8a6667093a824e284ec9d75f Mon Sep 17 00:00:00 2001 From: Jeff Jennings Date: Tue, 14 Nov 2023 03:29:55 -0500 Subject: [PATCH 13/18] joss paper: update acknowledgements --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index 3cb13a5d..f5b5667a 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -69,6 +69,6 @@ Accurately reconstructing an image from sparse Fourier data is an ill-posed prob # Acknowledgements -We acknowledge contributions from Brianna Zawadzki, Ryan Loomis, Kadri Nizam, Megan Delamer, Kaylee de Soto, Robert Frazier, Hannah Grzybowski, Mary Ogborn, and Tyler Quinn. + # References From 5438d94ad3f79c9358ca49b88cd5bdd93ea085fd Mon Sep 17 00:00:00 2001 From: Jeff Jennings Date: Tue, 14 Nov 2023 03:36:38 -0500 Subject: [PATCH 14/18] joss paper: only trigger pdf build on this branch --- .github/workflows/draft-pdf.yml | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/.github/workflows/draft-pdf.yml b/.github/workflows/draft-pdf.yml index ccc3e9c3..7086c3c3 100644 --- a/.github/workflows/draft-pdf.yml +++ b/.github/workflows/draft-pdf.yml @@ -1,6 +1,9 @@ name: build JOSS pdf -on: [push] +on: + push: + branches: + - joss_paper jobs: paper: From 16bfe576ffbd23d59514ffbdeb3f58c0cbc9e26c Mon Sep 17 00:00:00 2001 From: Ian Czekala Date: Wed, 4 Dec 2024 22:13:18 +0000 Subject: [PATCH 15/18] updated artifacts action. --- .github/workflows/draft-pdf.yml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/.github/workflows/draft-pdf.yml b/.github/workflows/draft-pdf.yml index 7086c3c3..a7bce32e 100644 --- a/.github/workflows/draft-pdf.yml +++ b/.github/workflows/draft-pdf.yml @@ -1,9 +1,9 @@ name: build JOSS pdf on: - push: - branches: - - joss_paper + push: + branches: + - joss_paper jobs: paper: @@ -19,7 +19,7 @@ jobs: # This should be the path to the paper within your repo. paper-path: paper/paper.md - name: Upload - uses: actions/upload-artifact@v1 + uses: actions/upload-artifact@v4 with: name: paper # This is the output path where Pandoc will write the compiled From c56d597bf758998395c460f66523f81d71b99fb6 Mon Sep 17 00:00:00 2001 From: Ian Czekala Date: Fri, 13 Dec 2024 11:27:40 +0000 Subject: [PATCH 16/18] added IM Lup figure, some NeurIPS text. --- .gitignore | 5 +- paper/.gitignore | 2 + paper/fig.pdf | Bin 0 -> 272068 bytes paper/paper.bib | 49 ++++++++++++++++++-- paper/paper.md | 117 ++++++++++++++++++++++++++++------------------- 5 files changed, 121 insertions(+), 52 deletions(-) create mode 100644 paper/.gitignore create mode 100644 paper/fig.pdf diff --git a/.gitignore b/.gitignore index 0e72b548..c62f946a 100644 --- a/.gitignore +++ b/.gitignore @@ -146,4 +146,7 @@ runs # hatch-generated version file src/mpol/mpol_version.py -.ruff_cache \ No newline at end of file +.ruff_cache + +build_joss.sh +prof \ No newline at end of file diff --git a/paper/.gitignore b/paper/.gitignore new file mode 100644 index 00000000..2cec151d --- /dev/null +++ b/paper/.gitignore @@ -0,0 +1,2 @@ +jats +paper.pdf \ No newline at end of file diff --git a/paper/fig.pdf b/paper/fig.pdf new file mode 100644 index 0000000000000000000000000000000000000000..2efd2d9ebaecf871d0a26f037f7f191f4aed8251 GIT binary patch literal 272068 zcmb@s1z40_*DtOjASDt*3j;_FIdn_6^e}V|&Crd4l%xof(kU@?w}61Obb}z>-RghP z=Xu}feBXQi-?`3pm}^~Y@B7|+uh?s^{oDJ7PE|^Vm6MGdlMXQtEN;Z)1abf!Ol>fQ zg@NpvUXCyzyQGP$iJgNbkX_Zp66OK~BO9m#MMW`TaPzx{{Qu}6;{bOBa{X?=4plL= 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z$oM$KRgk3qU*E^)=hGjWS2s5|hbB)I8}9)F`zyu!r{CW2#ZJ&C^a9H26O515iD;-3 zL!B7vcmhdhC2C|TIz4=gkC^ebwYIFpy!?1V-Qt==kcd&2FlYPeNtV!Jqt&4RA7Ps8E%P_@^YSTj{DMi10`tm~e>a_y=a@cw5~; zpPxZ2hZ%JAu%aMv0_$gwFbuMcK5W|oMkI^{4Tcbp5SKrWVlY#lEnt}A^_XzNI7zn# zLZWUQT$B)*-2EYhkp%iOA43G`FpLBp#uFX_nQIHqViEdjxfl^eiP!i+L>u(l^2Z@W zY@&`ABI@}g98~jpYXG5kE%($nyKc{zCpud!NII^tsJDgm(C|n7)%zw&M8ga9m%vHM z;{|d<8*|7PlIuq}=uujIL5B7+?kT-K$UoM4S5@hepo2oDq NCrV6txm@pN{s60)UNry! literal 0 HcmV?d00001 diff --git a/paper/paper.bib b/paper/paper.bib index 4fe81318..45ff104a 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -1,4 +1,23 @@ -@ARTICLE{Hogbom_1974, +@article{andrews18, + author = {{Andrews}, Sean M. and {Huang}, Jane and {P{\'e}rez}, Laura M. and {Isella}, Andrea and {Dullemond}, Cornelis P. and {Kurtovic}, Nicol{\'a}s T. and {Guzm{\'a}n}, Viviana V. and {Carpenter}, John M. and {Wilner}, David J. and {Zhang}, Shangjia and {Zhu}, Zhaohuan and {Birnstiel}, Tilman and {Bai}, Xue-Ning and {Benisty}, Myriam and {Hughes}, A. Meredith and {{\"O}berg}, Karin I. and {Ricci}, Luca}, + title = {{The Disk Substructures at High Angular Resolution Project (DSHARP). I. Motivation, Sample, Calibration, and Overview}}, + journal = {Astrophysical Journal Letters}, + keywords = {circumstellar matter, planets and satellites: formation, protoplanetary disks, Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Earth and Planetary Astrophysics}, + year = 2018, + month = dec, + volume = {869}, + number = {2}, + eid = {L41}, + pages = {L41}, + doi = {10.3847/2041-8213/aaf741}, + archiveprefix = {arXiv}, + eprint = {1812.04040}, + primaryclass = {astro-ph.SR}, + adsurl = {https://ui.adsabs.harvard.edu/abs/2018ApJ...869L..41A}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} + +@ARTICLE{hogbom_1974, author = {{H{\"o}gbom}, J.~A.}, title = "{Aperture Synthesis with a Non-Regular Distribution of Interferometer Baselines}", journal = {Astronomy and Astrophysics Supplement}, @@ -10,7 +29,7 @@ @ARTICLE{Hogbom_1974 adsnote = {Provided by the SAO/NASA Astrophysics Data System} } -@ARTICLE{Clark_1980, +@ARTICLE{clark_1980, author = {{Clark}, B.~G.}, title = "{An efficient implementation of the algorithm 'CLEAN'}", journal = {Astronomy and Astrophysics}, @@ -23,7 +42,7 @@ @ARTICLE{Clark_1980 adsnote = {Provided by the SAO/NASA Astrophysics Data System} } -@INPROCEEDINGS{McMullin_2007, +@INPROCEEDINGS{mcmullin_2007, author = {{McMullin}, J.~P. and {Waters}, B. and {Schiebel}, D. and {Young}, W. and {Golap}, K.}, title = "{CASA Architecture and Applications}", @@ -37,7 +56,7 @@ @INPROCEEDINGS{McMullin_2007 adsnote = {Provided by the SAO/NASA Astrophysics Data System} } -@ARTICLE{CASA_2022, +@ARTICLE{casa_2022, author = {{CASA Team} and {Bean}, Ben and {Bhatnagar}, Sanjay and {Castro}, Sandra and {Donovan Meyer}, Jennifer and {Emonts}, Bjorn and {Garcia}, Enrique and {Garwood}, Robert and {Golap}, Kumar and {Gonzalez Villalba}, Justo and {Harris}, Pamela and {Hayashi}, Yohei and {Hoskins}, Josh and {Hsieh}, Mingyu and {Jagannathan}, Preshanth and {Kawasaki}, Wataru and {Keimpema}, Aard and {Kettenis}, Mark and {Lopez}, Jorge and {Marvil}, Joshua and {Masters}, Joseph and {McNichols}, Andrew and {Mehringer}, David and {Miel}, Renaud and {Moellenbrock}, George and {Montesino}, Federico and {Nakazato}, Takeshi and {Ott}, Juergen and {Petry}, Dirk and {Pokorny}, Martin and {Raba}, Ryan and {Rau}, Urvashi and {Schiebel}, Darrell and {Schweighart}, Neal and {Sekhar}, Srikrishna and {Shimada}, Kazuhiko and {Small}, Des and {Steeb}, Jan-Willem and {Sugimoto}, Kanako and {Suoranta}, Ville and {Tsutsumi}, Takahiro and {van Bemmel}, Ilse M. and {Verkouter}, Marjolein and {Wells}, Akeem and {Xiong}, Wei and {Szomoru}, Arpad and {Griffith}, Morgan and {Glendenning}, Brian and {Kern}, Jeff}, title = "{CASA, the Common Astronomy Software Applications for Radio Astronomy}", journal = {Publications of the Astronomical Society of the Pacific}, @@ -54,4 +73,24 @@ @ARTICLE{CASA_2022 primaryClass = {astro-ph.IM}, adsurl = {https://ui.adsabs.harvard.edu/abs/2022PASP..134k4501C}, adsnote = {Provided by the SAO/NASA Astrophysics Data System} -} \ No newline at end of file +} + +@article{zawadzki_2023, + author = {{Zawadzki}, Brianna and {Czekala}, Ian and {Loomis}, Ryan A. and {Quinn}, Tyler and {Grzybowski}, Hannah and {Frazier}, Robert C. and {Jennings}, Jeff and {Nizam}, Kadri M. and {Jian}, Yina}, + title = {{Regularized Maximum Likelihood Image Synthesis and Validation for ALMA Continuum Observations of Protoplanetary Disks}}, + journal = {\pasp}, + keywords = {Protoplanetary disks, Submillimeter astronomy, Radio interferometry, Deconvolution, Open source software, 1300, 1647, 1346, 1910, 1866, Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics}, + year = 2023, + month = jun, + volume = {135}, + number = {1048}, + eid = {064503}, + pages = {064503}, + doi = {10.1088/1538-3873/acdf84}, + archiveprefix = {arXiv}, + eprint = {2209.11813}, + primaryclass = {astro-ph.EP}, + adsurl = {https://ui.adsabs.harvard.edu/abs/2023PASP..135f4503Z}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} + diff --git a/paper/paper.md b/paper/paper.md index f5b5667a..31830e18 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -1,74 +1,99 @@ --- -title: 'MPoL: A Python package for scalable, nonparametric interferometric imaging' +title: 'Million Points of Light (MPoL): A Python package for imaging and inference with astronomical interferometric data' tags: - Python - astronomy - imaging - fourier + - radio astronomy + - radio interferometry authors: - name: Ian Czekala orcid: 0000-0002-1483-8811 - # equal-contrib: true # (This is how you can denote equal contributions between multiple authors) - # corresponding: true + corresponding: true affiliation: 1 - # affiliation: "1, 2" # (Multiple affiliations must be quoted) - - name: Jeff Jennings - orcid: 0000-0002-7032-2350 - affiliation: 2 - - name: Brianna Zawadzki - orcid: 0000-0001-9319-1296 - affiliation: 3 - - name: Ryan Loomis - orcid: 0000-0002-8932-1219 - affiliation: 4 - - name: Kadri Nizam - orcid: 0000-0002-7217-446X - affiliation: 2 - - name: Megan Delamer - # orcid: # can't find - affiliation: 2 - - name: Kaylee de Soto - orcid: 0000-0002-9886-2834 - affiliation: 2 - - name: Robert Frazier - orcid: 0000-0001-6569-3731 - affiliation: 2 - - name: Hannah Grzybowski - # orcid: # can't find - affiliation: 2 - - name: Mary Ogborn - orcid: 0000-0001-9741-2703 - affiliation: 2 - - name: Tyler Quinn - orcid: 0000-0002-8974-8095 - affiliation: 2 + - name: co-authors + # - name: Jeff Jennings + # orcid: 0000-0002-7032-2350 + # affiliation: 2 + # - name: Brianna Zawadzki + # orcid: 0000-0001-9319-1296 + # affiliation: 3 + # - name: Kadri Nizam + # orcid: 0000-0002-7217-446X + # affiliation: 2 + # - name: Ryan Loomis + # orcid: 0000-0002-8932-1219 + # affiliation: 4 + # - name: Megan Delamer + # orcid: 0000-0003-1439-2781 + # affiliation: 2 + # - name: Kaylee de Soto + # orcid: 0000-0002-9886-2834 + # affiliation: 2 + # - name: Robert Frazier + # orcid: 0000-0001-6569-3731 + # affiliation: 2 + # - name: Hannah Grzybowski + # # orcid: # can't find + # affiliation: 2 + # - name: Mary Ogborn + # orcid: 0000-0001-9741-2703 + # affiliation: 2 + # - name: Tyler Quinn + # orcid: 0000-0002-8974-8095 + # affiliation: 2 affiliations: - - name: University of St Andrews, Scotland # "institution, country" format suggested by JOSS + - name: University of St Andrews, Scotland index: 1 - - name: Pennsylvania State University, USA - index: 2 - - name: Wesleyan University, USA - index: 3 - - name: National Radio Astronomy Observatory, USA - index: 4 -date: 14 November 2023 +# - name: Pennsylvania State University, USA +# index: 2 +# - name: Wesleyan University, USA +# index: 3 +# - name: National Radio Astronomy Observatory, Charlottesville, VA, USA +# index: 4 +date: 12 December 2024 bibliography: paper.bib +aas-journal: Astrophysical Journal --- +* 1000 words +* A summary describing the high-level functionality and purpose of the software for a diverse, non-specialist audience. +* A clear statement of need that illustrates the purpose of the software. +* A description of how this software compares to other commonly-used packages in this research area. +* Mentions (if applicable) of any ongoing research projects using the software or recent scholarly publications enabled by it. +* A list of key references including a link to the software archive. + # Summary -Interferometric imaging is the process of recovering a spatial domain image from a Fourier domain signal that is only partially sampled. The technique is applied in a large number of fields from medical imaging to remote sensing, optics and astronomy. Within astronomy, interferometry conducted at radio, infrared and optical frequencies yields unparalleled spatial resolution in an image, corresponding to physical scales that are otherwise inaccessibly small. `Million Points of Light` (`MPoL`) is a Python package for astronomical interferometric imaging. It couples a statistical modeling framework with an efficient computational implementation to reconstruct images of astronomical sources from data measured by large telescopes such as the Atacama Large Millimeter/Submillimeter Array (ALMA). +Interferometric imaging is the process of recovering a spatial domain image from a Fourier domain signal that is only partially sampled. The technique is applied in a large number of fields from medical imaging to remote sensing, optics and astronomy. Within astronomy, interferometry conducted at radio, infrared and optical frequencies yields unparalleled spatial resolution in an image, corresponding to physical scales that are otherwise inaccessibly small. *Million Points of Light* (`MPoL`) is a Python package for astronomical interferometric imaging. It couples a statistical modeling framework with an efficient computational implementation to reconstruct images of astronomical sources from data measured by large telescopes such as the Atacama Large Millimeter/Submillimeter Array (ALMA). # Statement of need -Accurately reconstructing an image from sparse Fourier data is an ill-posed problem that remains an outstanding challenge in astronomical research, particularly in sub-mm astronomy. There, the current standard approach to interferometric imaging is `CLEAN` [@Hogbom_1974; @Clark_1980], an empirical, algorithmic procedure that requires a high degree of user intervention. The algorithm is not computationally efficient and thus not practical for large datasets (~100 GB) that are becoming increasingly common in the field. And the enclosing software lacks the accessibility and up-to-date documentation to easily modify the algorithm for custom use cases [@McMullin_2007; @CASA_2022]. Collectively these limitations necessitate an alternative imaging formalism and software implementation. +Accurately reconstructing an image from sparse Fourier data is an ill-posed problem that remains an outstanding challenge in astronomical research, particularly in sub-mm astronomy. There, the current standard approach to interferometric imaging is `CLEAN` [@hogbom_1974; @clark_1980], an empirical, algorithmic procedure that requires a high degree of user intervention. The algorithm is not computationally efficient and thus not practical for large datasets (~100 GB) that are becoming increasingly common in the field. And the enclosing software lacks the accessibility and up-to-date documentation to easily modify the algorithm for custom use cases [@mcmullin_2007; @casa_2022]. Collectively these limitations necessitate an alternative imaging formalism and software implementation. + +# The Million Points of Light library + +Built on PyTorch for idiomatic expression of radio interferometry problems, including using stochastic gradient descent. `MPoL` is a statistically robust, nonparametric modeling approach to interferometric imaging in a user-friendly, well-documented package that is computationally performant. The software is designed to be applied to reconstruction of an individual image or an entire 'cube' of tens to hundreds of images of an astronomical source observed at different frequencies. The images obtained are of simultaneously higher spatial resolution and sensitivity than their counterparts produced by `CLEAN`. Programatically, `MPoL` is built on `PyTorch`, using its auto-differentiation capabilities to drive likelihood optimization with gradient descent and its parallelization support to optionally accelerate the imaging workflow on GPUs and TPUs. The imaging framework in `MPoL` is also flexible, with the ability to easily add alternative or additional priors into likelihood calculation. Extensions to the core functionality are actively developed, such as the recent implementation of parametric inference with `Pyro`, as are further optimizations to the core routines. -`MPoL` is used in astrophysical research to image and study objects such as protoplanetary disks and Solar System bodies. It is currently being applied to multiple projects, from individual use cases to large collaborations. The software could be applied without modification to research in other subfields of astronomy that use data from sub-mm interferometers, including cosmology, extragalactic astronomy, and star formation. With a reasonable amount of modification, it could be adopted for datasets obtained by infrared and optical interferometers, or to interferometric imaging problems beyond astronomy. +# Documentation and case studies + +The main documentation is available at https://mpol-dev.github.io/MPoL/ + +The codebase has been used in [@zawadzki_2023]. + +![Left: the synthesized image produced by the DSHARP ALMA Large Program [@andrews18] using \texttt{CASA/tclean}. Right: The regularized maximum likelihood image produced using \texttt{MPoL} on the same data. Both images are displayed using a `sqrt` stretch, with upper limit truncated to 70\% and 40\% of max value for CLEAN and \texttt{MPoL}, respectively, to emphasize faint features. The CLEAN algorithm permits negative intensity values, while the `MPoL` algorithm enforces image positivity by construction. Image dimensions are 3 arcseconds to a side. Intensity units are shown in units of Jy/arcsec^2^.](fig.pdf) + +# Similar tools + +EHT imagers, BASP group, MaxEnt Carcamo and various adherences to the framework in CASA. MPoL is focused as a PyTorch library, and works as glue to integrate with the rich systems of neural networks such as autodifferentiation and probabilistic programming languages. + +Frankenstein is a 1D non-parametric tool and integrates with MPoL. # Acknowledgements - +We acknowledge funding from an ALMA Development Cycle 8 grant number AST-1519126. ALMA is a partnership of ESO (representing its member states), NSF (USA) and NINS (Japan), together with NRC (Canada), MOST and ASIAA (Taiwan), and KASI (Republic of Korea), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, AUI/NRAO and NAOJ. The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc. # References From 31802e2f599e1b486a0ca83646c2ba1859b8db4b Mon Sep 17 00:00:00 2001 From: Ian Czekala Date: Tue, 24 Dec 2024 22:29:46 +0000 Subject: [PATCH 17/18] sketched out general structure. --- paper/paper.bib | 355 +++++++++++++++++++++++++++++++++++++++++++++++- paper/paper.md | 47 ++++--- 2 files changed, 379 insertions(+), 23 deletions(-) diff --git a/paper/paper.bib b/paper/paper.bib index 45ff104a..3a7a1b15 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -17,7 +17,7 @@ @article{andrews18 adsnote = {Provided by the SAO/NASA Astrophysics Data System} } -@ARTICLE{hogbom_1974, +@ARTICLE{hogbom74, author = {{H{\"o}gbom}, J.~A.}, title = "{Aperture Synthesis with a Non-Regular Distribution of Interferometer Baselines}", journal = {Astronomy and Astrophysics Supplement}, @@ -29,7 +29,7 @@ @ARTICLE{hogbom_1974 adsnote = {Provided by the SAO/NASA Astrophysics Data System} } -@ARTICLE{clark_1980, +@ARTICLE{clark80, author = {{Clark}, B.~G.}, title = "{An efficient implementation of the algorithm 'CLEAN'}", journal = {Astronomy and Astrophysics}, @@ -42,7 +42,7 @@ @ARTICLE{clark_1980 adsnote = {Provided by the SAO/NASA Astrophysics Data System} } -@INPROCEEDINGS{mcmullin_2007, +@INPROCEEDINGS{mcmullin07, author = {{McMullin}, J.~P. and {Waters}, B. and {Schiebel}, D. and {Young}, W. and {Golap}, K.}, title = "{CASA Architecture and Applications}", @@ -56,7 +56,7 @@ @INPROCEEDINGS{mcmullin_2007 adsnote = {Provided by the SAO/NASA Astrophysics Data System} } -@ARTICLE{casa_2022, +@ARTICLE{casa22, author = {{CASA Team} and {Bean}, Ben and {Bhatnagar}, Sanjay and {Castro}, Sandra and {Donovan Meyer}, Jennifer and {Emonts}, Bjorn and {Garcia}, Enrique and {Garwood}, Robert and {Golap}, Kumar and {Gonzalez Villalba}, Justo and {Harris}, Pamela and {Hayashi}, Yohei and {Hoskins}, Josh and {Hsieh}, Mingyu and {Jagannathan}, Preshanth and {Kawasaki}, Wataru and {Keimpema}, Aard and {Kettenis}, Mark and {Lopez}, Jorge and {Marvil}, Joshua and {Masters}, Joseph and {McNichols}, Andrew and {Mehringer}, David and {Miel}, Renaud and {Moellenbrock}, George and {Montesino}, Federico and {Nakazato}, Takeshi and {Ott}, Juergen and {Petry}, Dirk and {Pokorny}, Martin and {Raba}, Ryan and {Rau}, Urvashi and {Schiebel}, Darrell and {Schweighart}, Neal and {Sekhar}, Srikrishna and {Shimada}, Kazuhiko and {Small}, Des and {Steeb}, Jan-Willem and {Sugimoto}, Kanako and {Suoranta}, Ville and {Tsutsumi}, Takahiro and {van Bemmel}, Ilse M. and {Verkouter}, Marjolein and {Wells}, Akeem and {Xiong}, Wei and {Szomoru}, Arpad and {Griffith}, Morgan and {Glendenning}, Brian and {Kern}, Jeff}, title = "{CASA, the Common Astronomy Software Applications for Radio Astronomy}", journal = {Publications of the Astronomical Society of the Pacific}, @@ -75,10 +75,10 @@ @ARTICLE{casa_2022 adsnote = {Provided by the SAO/NASA Astrophysics Data System} } -@article{zawadzki_2023, +@article{zawadzki23, author = {{Zawadzki}, Brianna and {Czekala}, Ian and {Loomis}, Ryan A. and {Quinn}, Tyler and {Grzybowski}, Hannah and {Frazier}, Robert C. and {Jennings}, Jeff and {Nizam}, Kadri M. and {Jian}, Yina}, title = {{Regularized Maximum Likelihood Image Synthesis and Validation for ALMA Continuum Observations of Protoplanetary Disks}}, - journal = {\pasp}, + journal = {Publications of the Astronomical Society of the Pacific}, keywords = {Protoplanetary disks, Submillimeter astronomy, Radio interferometry, Deconvolution, Open source software, 1300, 1647, 1346, 1910, 1866, Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics}, year = 2023, month = jun, @@ -94,3 +94,346 @@ @article{zawadzki_2023 adsnote = {Provided by the SAO/NASA Astrophysics Data System} } +@article{campagne23, + author = {{Campagne}, Jean-Eric and {Lanusse}, Fran{\c{c}}ois and {Zuntz}, Joe and {Boucaud}, Alexandre and {Casas}, Santiago and {Karamanis}, Minas and {Kirkby}, David and {Lanzieri}, Denise and {Peel}, Austin and {Li}, Yin}, + title = {{JAX-COSMO: An End-to-End Differentiable and GPU Accelerated Cosmology Library}}, + journal = {The Open Journal of Astrophysics}, + keywords = {Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics}, + year = 2023, + month = apr, + volume = {6}, + eid = {15}, + pages = {15}, + doi = {10.21105/astro.2302.05163}, + archiveprefix = {arXiv}, + eprint = {2302.05163}, + primaryclass = {astro-ph.CO}, + adsurl = {https://ui.adsabs.harvard.edu/abs/2023OJAp....6E..15C}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} + +@article{bedell19, + author = {{Bedell}, Megan and {Hogg}, David W. and {Foreman-Mackey}, Daniel and {Montet}, Benjamin T. and {Luger}, Rodrigo}, + title = {{WOBBLE: A Data-driven Analysis Technique for Time-series Stellar Spectra}}, + journal = {The Astronomical Journal}, + keywords = {atmospheric effects, methods: data analysis, planets and satellites: detection, stars: individual: 51 Pegasi, Barnard's Star, HD 189733, techniques: radial velocities, Astrophysics - 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Image and Video Processing, Electrical Engineering and Systems Science - Signal Processing}, + year = 2024, + month = may, + volume = {966}, + number = {2}, + eid = {L34}, + pages = {L34}, + doi = {10.3847/2041-8213/ad41df}, + archiveprefix = {arXiv}, + eprint = {2309.03291}, + primaryclass = {astro-ph.IM}, + adsurl = {https://ui.adsabs.harvard.edu/abs/2024ApJ...966L..34D}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} + +@article{wang23, + author = {{Wang}, Ruoqi and {Chen}, Zhuoyang and {Luo}, Qiong and {Wang}, Feng}, + title = {{A Conditional Denoising Diffusion Probabilistic Model for Radio Interferometric Image Reconstruction}}, + journal = {arXiv e-prints}, + keywords = {Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Astrophysics of Galaxies, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing}, + year = 2023, + month = may, + eid = {arXiv:2305.09121}, + pages = {arXiv:2305.09121}, + doi = {10.48550/arXiv.2305.09121}, + archiveprefix = {arXiv}, + eprint = {2305.09121}, + primaryclass = {astro-ph.IM}, + adsurl = {https://ui.adsabs.harvard.edu/abs/2023arXiv230509121W}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} + + +@article{dia23, + author = {{Dia}, Noe and {Yantovski-Barth}, M.~J. and {Adam}, Alexandre and {Bowles}, Micah and {Lemos}, Pablo and {Scaife}, Anna M.~M. and {Hezaveh}, Yashar and {Perreault-Levasseur}, Laurence}, + title = {{Bayesian Imaging for Radio Interferometry with Score-Based Priors}}, + journal = {arXiv e-prints}, + keywords = {Astrophysics - 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Instrumentation and Methods for Astrophysics}, + year = 2020, + month = jul, + volume = {495}, + number = {3}, + pages = {3209-3232}, + doi = {10.1093/mnras/staa1365}, + archiveprefix = {arXiv}, + eprint = {2005.07709}, + primaryclass = {astro-ph.EP}, + adsurl = {https://ui.adsabs.harvard.edu/abs/2020MNRAS.495.3209J}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} + + + +@article{eht19a, + author = {{Event Horizon Telescope Collaboration} and {Akiyama}, Kazunori and {Alberdi}, Antxon and {Alef}, Walter and {Asada}, Keiichi and {Azulay}, Rebecca and {Baczko}, Anne-Kathrin and {Ball}, David and {Balokovi{\'c}}, Mislav and {Barrett}, John and {Bintley}, Dan and {Blackburn}, Lindy and {Boland}, Wilfred and {Bouman}, Katherine L. and {Bower}, Geoffrey C. and {Bremer}, Michael and {Brinkerink}, Christiaan D. and {Brissenden}, Roger and {Britzen}, Silke and {Broderick}, Avery E. and {Broguiere}, Dominique and {Bronzwaer}, Thomas and {Byun}, Do-Young and {Carlstrom}, John E. and {Chael}, Andrew and {Chan}, Chi-kwan and {Chatterjee}, Shami and {Chatterjee}, Koushik and {Chen}, Ming-Tang and {Chen}, Yongjun and {Cho}, Ilje and {Christian}, Pierre and {Conway}, John E. and {Cordes}, James M. and {Crew}, Geoffrey B. and {Cui}, Yuzhu and {Davelaar}, Jordy and {De Laurentis}, Mariafelicia and {Deane}, Roger and {Dempsey}, Jessica and {Desvignes}, Gregory and {Dexter}, Jason and {Doeleman}, Sheperd S. and {Eatough}, Ralph P. and {Falcke}, Heino and {Fish}, Vincent L. and {Fomalont}, Ed and {Fraga-Encinas}, Raquel and {Freeman}, William T. and {Friberg}, Per and {Fromm}, Christian M. and {G{\'o}mez}, Jos{\'e} L. and {Galison}, Peter and {Gammie}, Charles F. and {Garc{\'\i}a}, Roberto and {Gentaz}, Olivier and {Georgiev}, Boris and {Goddi}, Ciriaco and {Gold}, Roman and {Gu}, Minfeng and {Gurwell}, Mark and {Hada}, Kazuhiro and {Hecht}, Michael H. and {Hesper}, Ronald and {Ho}, Luis C. and {Ho}, Paul and {Honma}, Mareki and {Huang}, Chih-Wei L. and {Huang}, Lei and {Hughes}, David H. and {Ikeda}, Shiro and {Inoue}, Makoto and {Issaoun}, Sara and {James}, David J. and {Jannuzi}, Buell T. and {Janssen}, Michael and {Jeter}, Britton and {Jiang}, Wu and {Johnson}, Michael D. and {Jorstad}, Svetlana and {Jung}, Taehyun and {Karami}, Mansour and {Karuppusamy}, Ramesh and {Kawashima}, Tomohisa and {Keating}, Garrett K. and {Kettenis}, Mark and {Kim}, Jae-Young and {Kim}, Junhan and {Kim}, Jongsoo and {Kino}, Motoki and {Koay}, Jun Yi and {Koch}, Patrick M. and {Koyama}, Shoko and {Kramer}, Michael and {Kramer}, Carsten and {Krichbaum}, Thomas P. and {Kuo}, Cheng-Yu and {Lauer}, Tod R. and {Lee}, Sang-Sung and {Li}, Yan-Rong and {Li}, Zhiyuan and {Lindqvist}, Michael and {Liu}, Kuo and {Liuzzo}, Elisabetta and {Lo}, Wen-Ping and {Lobanov}, Andrei P. and {Loinard}, Laurent and {Lonsdale}, Colin and {Lu}, Ru-Sen and {MacDonald}, Nicholas R. and {Mao}, Jirong and {Markoff}, Sera and {Marrone}, Daniel P. and {Marscher}, Alan P. and {Mart{\'\i}-Vidal}, Iv{\'a}n and {Matsushita}, Satoki and {Matthews}, Lynn D. and {Medeiros}, Lia and {Menten}, Karl M. and {Mizuno}, Yosuke and {Mizuno}, Izumi and {Moran}, James M. and {Moriyama}, Kotaro and {Moscibrodzka}, Monika and {M{\"u}ller}, Cornelia and {Nagai}, Hiroshi and {Nagar}, Neil M. and {Nakamura}, Masanori and {Narayan}, Ramesh and {Narayanan}, Gopal and {Natarajan}, Iniyan and {Neri}, Roberto and {Ni}, Chunchong and {Noutsos}, Aristeidis and {Okino}, Hiroki and {Olivares}, H{\'e}ctor and {Ortiz-Le{\'o}n}, Gisela N. and {Oyama}, Tomoaki and {{\"O}zel}, Feryal and {Palumbo}, Daniel C.~M. and {Patel}, Nimesh and {Pen}, Ue-Li and {Pesce}, Dominic W. and {Pi{\'e}tu}, Vincent and {Plambeck}, Richard and {PopStefanija}, Aleksandar and {Porth}, Oliver and {Prather}, Ben and {Preciado-L{\'o}pez}, Jorge A. and {Psaltis}, Dimitrios and {Pu}, Hung-Yi and {Ramakrishnan}, Venkatessh and {Rao}, Ramprasad and {Rawlings}, Mark G. and {Raymond}, Alexander W. and {Rezzolla}, Luciano and {Ripperda}, Bart and {Roelofs}, Freek and {Rogers}, Alan and {Ros}, Eduardo and {Rose}, Mel and {Roshanineshat}, Arash and {Rottmann}, Helge and {Roy}, Alan L. and {Ruszczyk}, Chet and {Ryan}, Benjamin R. and {Rygl}, Kazi L.~J. and {S{\'a}nchez}, Salvador and {S{\'a}nchez-Arguelles}, David and {Sasada}, Mahito and {Savolainen}, Tuomas and {Schloerb}, F. 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Anton and {Zhao}, Guangyao and {Zhao}, Shan-Shan and {Zhu}, Ziyan and {Algaba}, Juan-Carlos and {Allardi}, Alexander and {Amestica}, Rodrigo and {Anczarski}, Jadyn and {Bach}, Uwe and {Baganoff}, Frederick K. and {Beaudoin}, Christopher and {Benson}, Bradford A. and {Berthold}, Ryan and {Blanchard}, Jay M. and {Blundell}, Ray and {Bustamente}, Sandra and {Cappallo}, Roger and {Castillo-Dom{\'\i}nguez}, Edgar and {Chang}, Chih-Cheng and {Chang}, Shu-Hao and {Chang}, Song-Chu and {Chen}, Chung-Chen and {Chilson}, Ryan and {Chuter}, Tim C. and {C{\'o}rdova Rosado}, Rodrigo and {Coulson}, Iain M. and {Crawford}, Thomas M. and {Crowley}, Joseph and {David}, John and {Derome}, Mark and {Dexter}, Matthew and {Dornbusch}, Sven and {Dudevoir}, Kevin A. and {Dzib}, Sergio A. and {Eckart}, Andreas and {Eckert}, Chris and {Erickson}, Neal R. and {Everett}, Wendeline B. and {Faber}, Aaron and {Farah}, Joseph R. and {Fath}, Vernon and {Folkers}, Thomas W. and {Forbes}, David C. and {Freund}, Robert and {G{\'o}mez-Ruiz}, Arturo I. and {Gale}, David M. and {Gao}, Feng and {Geertsema}, Gertie and {Graham}, David A. and {Greer}, Christopher H. and {Grosslein}, Ronald and {Gueth}, Fr{\'e}d{\'e}ric and {Haggard}, Daryl and {Halverson}, Nils W. and {Han}, Chih-Chiang and {Han}, Kuo-Chang and {Hao}, Jinchi and {Hasegawa}, Yutaka and {Henning}, Jason W. and {Hern{\'a}ndez-G{\'o}mez}, Antonio and {Herrero-Illana}, Rub{\'e}n and {Heyminck}, Stefan and {Hirota}, Akihiko and {Hoge}, James and {Huang}, Yau-De and {Impellizzeri}, C.~M. Violette and {Jiang}, Homin and {Kamble}, Atish and {Keisler}, Ryan and {Kimura}, Kimihiro and {Kono}, Yusuke and {Kubo}, Derek and {Kuroda}, John and {Lacasse}, Richard and {Laing}, Robert A. and {Leitch}, Erik M. and {Li}, Chao-Te and {Lin}, Lupin C. -C. and {Liu}, Ching-Tang and {Liu}, Kuan-Yu and {Lu}, Li-Ming and {Marson}, Ralph G. and {Martin-Cocher}, Pierre L. and {Massingill}, Kyle D. and {Matulonis}, Callie and {McColl}, Martin P. and {McWhirter}, Stephen R. and {Messias}, Hugo and {Meyer-Zhao}, Zheng and {Michalik}, Daniel and {Monta{\~n}a}, Alfredo and {Montgomerie}, William and {Mora-Klein}, Matias and {Muders}, Dirk and {Nadolski}, Andrew and {Navarro}, Santiago and {Neilsen}, Joseph and {Nguyen}, Chi H. and {Nishioka}, Hiroaki and {Norton}, Timothy and {Nowak}, Michael A. and {Nystrom}, George and {Ogawa}, Hideo and {Oshiro}, Peter and {Oyama}, Tomoaki and {Parsons}, Harriet and {Paine}, Scott N. and {Pe{\~n}alver}, Juan and {Phillips}, Neil M. and {Poirier}, Michael and {Pradel}, Nicolas and {Primiani}, Rurik A. and {Raffin}, Philippe A. and {Rahlin}, Alexandra S. and {Reiland}, George and {Risacher}, Christopher and {Ruiz}, Ignacio and {S{\'a}ez-Mada{\'\i}n}, Alejandro F. and {Sassella}, Remi and {Schellart}, Pim and {Shaw}, Paul and {Silva}, Kevin M. and {Shiokawa}, Hotaka and {Smith}, David R. and {Snow}, William and {Souccar}, Kamal and {Sousa}, Don and {Sridharan}, T.~K. and {Srinivasan}, Ranjani and {Stahm}, William and {Stark}, Anthony A. and {Story}, Kyle and {Timmer}, Sjoerd T. and {Vertatschitsch}, Laura and {Walther}, Craig and {Wei}, Ta-Shun and {Whitehorn}, Nathan and {Whitney}, Alan R. and {Woody}, David P. and {Wouterloot}, Jan G.~A. and {Wright}, Melvin and {Yamaguchi}, Paul and {Yu}, Chen-Yu and {Zeballos}, Milagros and {Zhang}, Shuo and {Ziurys}, Lucy}, + title = {{First M87 Event Horizon Telescope Results. I. The Shadow of the Supermassive Black Hole}}, + journal = {The Astrophysical Journal Letters}, + keywords = {accretion, accretion disks, black hole physics, galaxies: active, galaxies: individual: M87, galaxies: jets, gravitation, Astrophysics - Astrophysics of Galaxies, Astrophysics - High Energy Astrophysical Phenomena, General Relativity and Quantum Cosmology}, + year = 2019, + month = apr, + volume = {875}, + number = {1}, + eid = {L1}, + pages = {L1}, + doi = {10.3847/2041-8213/ab0ec7}, + archiveprefix = {arXiv}, + eprint = {1906.11238}, + primaryclass = {astro-ph.GA}, + adsurl = {https://ui.adsabs.harvard.edu/abs/2019ApJ...875L...1E}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} + +@article{eht19d, + author = {{Event Horizon Telescope Collaboration} and {Akiyama}, Kazunori and {Alberdi}, Antxon and {Alef}, Walter and {Asada}, Keiichi and {Azulay}, Rebecca and {Baczko}, Anne-Kathrin and {Ball}, David and {Balokovi{\'c}}, Mislav and {Barrett}, John and {Bintley}, Dan and {Blackburn}, Lindy and {Boland}, Wilfred and {Bouman}, Katherine L. and {Bower}, Geoffrey C. and {Bremer}, Michael and {Brinkerink}, Christiaan D. and {Brissenden}, Roger and {Britzen}, Silke and {Broderick}, Avery E. and {Broguiere}, Dominique and {Bronzwaer}, Thomas and {Byun}, Do-Young and {Carlstrom}, John E. and {Chael}, Andrew and {Chan}, Chi-kwan and {Chatterjee}, Shami and {Chatterjee}, Koushik and {Chen}, Ming-Tang and {Chen}, Yongjun and {Cho}, Ilje and {Christian}, Pierre and {Conway}, John E. and {Cordes}, James M. and {Crew}, Geoffrey B. and {Cui}, Yuzhu and {Davelaar}, Jordy and {De Laurentis}, Mariafelicia and {Deane}, Roger and {Dempsey}, Jessica and {Desvignes}, Gregory and {Dexter}, Jason and {Doeleman}, Sheperd S. and {Eatough}, Ralph P. and {Falcke}, Heino and {Fish}, Vincent L. and {Fomalont}, Ed and {Fraga-Encinas}, Raquel and {Freeman}, William T. and {Friberg}, Per and {Fromm}, Christian M. and {G{\'o}mez}, Jos{\'e} L. and {Galison}, Peter and {Gammie}, Charles F. and {Garc{\'\i}a}, Roberto and {Gentaz}, Olivier and {Georgiev}, Boris and {Goddi}, Ciriaco and {Gold}, Roman and {Gu}, Minfeng and {Gurwell}, Mark and {Hada}, Kazuhiro and {Hecht}, Michael H. and {Hesper}, Ronald and {Ho}, Luis C. and {Ho}, Paul and {Honma}, Mareki and {Huang}, Chih-Wei L. and {Huang}, Lei and {Hughes}, David H. and {Ikeda}, Shiro and {Inoue}, Makoto and {Issaoun}, Sara and {James}, David J. and {Jannuzi}, Buell T. and {Janssen}, Michael and {Jeter}, Britton and {Jiang}, Wu and {Johnson}, Michael D. and {Jorstad}, Svetlana and {Jung}, Taehyun and {Karami}, Mansour and {Karuppusamy}, Ramesh and {Kawashima}, Tomohisa and {Keating}, Garrett K. and {Kettenis}, Mark and {Kim}, Jae-Young and {Kim}, Junhan and {Kim}, Jongsoo and {Kino}, Motoki and {Koay}, Jun Yi and {Koch}, Patrick M. and {Koyama}, Shoko and {Kramer}, Michael and {Kramer}, Carsten and {Krichbaum}, Thomas P. and {Kuo}, Cheng-Yu and {Lauer}, Tod R. and {Lee}, Sang-Sung and {Li}, Yan-Rong and {Li}, Zhiyuan and {Lindqvist}, Michael and {Liu}, Kuo and {Liuzzo}, Elisabetta and {Lo}, Wen-Ping and {Lobanov}, Andrei P. and {Loinard}, Laurent and {Lonsdale}, Colin and {Lu}, Ru-Sen and {MacDonald}, Nicholas R. and {Mao}, Jirong and {Markoff}, Sera and {Marrone}, Daniel P. and {Marscher}, Alan P. and {Mart{\'\i}-Vidal}, Iv{\'a}n and {Matsushita}, Satoki and {Matthews}, Lynn D. and {Medeiros}, Lia and {Menten}, Karl M. and {Mizuno}, Yosuke and {Mizuno}, Izumi and {Moran}, James M. and {Moriyama}, Kotaro and {Moscibrodzka}, Monika and {M{\"u}ller}, Cornelia and {Nagai}, Hiroshi and {Nagar}, Neil M. and {Nakamura}, Masanori and {Narayan}, Ramesh and {Narayanan}, Gopal and {Natarajan}, Iniyan and {Neri}, Roberto and {Ni}, Chunchong and {Noutsos}, Aristeidis and {Okino}, Hiroki and {Olivares}, H{\'e}ctor and {Oyama}, Tomoaki and {{\"O}zel}, Feryal and {Palumbo}, Daniel C.~M. and {Patel}, Nimesh and {Pen}, Ue-Li and {Pesce}, Dominic W. and {Pi{\'e}tu}, Vincent and {Plambeck}, Richard and {PopStefanija}, Aleksandar and {Porth}, Oliver and {Prather}, Ben and {Preciado-L{\'o}pez}, Jorge A. and {Psaltis}, Dimitrios and {Pu}, Hung-Yi and {Ramakrishnan}, Venkatessh and {Rao}, Ramprasad and {Rawlings}, Mark G. and {Raymond}, Alexander W. and {Rezzolla}, Luciano and {Ripperda}, Bart and {Roelofs}, Freek and {Rogers}, Alan and {Ros}, Eduardo and {Rose}, Mel and {Roshanineshat}, Arash and {Rottmann}, Helge and {Roy}, Alan L. and {Ruszczyk}, Chet and {Ryan}, Benjamin R. and {Rygl}, Kazi L.~J. and {S{\'a}nchez}, Salvador and {S{\'a}nchez-Arguelles}, David and {Sasada}, Mahito and {Savolainen}, Tuomas and {Schloerb}, F. 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Imaging the Central Supermassive Black Hole}}, + journal = {The Astrophysical Journal Letters}, + keywords = {black hole physics, galaxies: individual: M87, galaxies: jets, techniques: high angular resolution, techniques: image processing, techniques: interferometric, Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics, General Relativity and Quantum Cosmology}, + year = 2019, + month = apr, + volume = {875}, + number = {1}, + eid = {L4}, + pages = {L4}, + doi = {10.3847/2041-8213/ab0e85}, + archiveprefix = {arXiv}, + eprint = {1906.11241}, + primaryclass = {astro-ph.GA}, + adsurl = {https://ui.adsabs.harvard.edu/abs/2019ApJ...875L...4E}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} + + +@article{benisty21, + author = {{Benisty}, Myriam and {Bae}, Jaehan and {Facchini}, Stefano and {Keppler}, Miriam and {Teague}, Richard and {Isella}, Andrea and {Kurtovic}, Nicolas T. and {P{\'e}rez}, Laura M. and {Sierra}, Anibal and {Andrews}, Sean M. and {Carpenter}, John and {Czekala}, Ian and {Dominik}, Carsten and {Henning}, Thomas and {Menard}, Francois and {Pinilla}, Paola and {Zurlo}, Alice}, + title = {{A Circumplanetary Disk around PDS70c}}, + journal = {The Astrophysical Journal Letters}, + keywords = {Protoplanetary disks, 1300, Astrophysics - 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Earth and Planetary Astrophysics, Astrophysics - Solar and Stellar Astrophysics}, + year = 2024, + month = nov, + volume = {976}, + number = {1}, + eid = {132}, + pages = {132}, + doi = {10.3847/1538-4357/ad84df}, + archiveprefix = {arXiv}, + eprint = {2410.03823}, + primaryclass = {astro-ph.EP}, + adsurl = {https://ui.adsabs.harvard.edu/abs/2024ApJ...976..132H}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} + +@book{thompson17, + author = {{Thompson}, A. 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Smith and Robert Kern and Matti Picus + and Stephan Hoyer and Marten H. van Kerkwijk and Matthew + Brett and Allan Haldane and Jaime Fern{\'{a}}ndez del + R{\'{i}}o and Mark Wiebe and Pearu Peterson and Pierre + G{\'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and + Warren Weckesser and Hameer Abbasi and Christoph Gohlke and + Travis E. Oliphant}, + year = {2020}, + month = sep, + journal = {Nature}, + volume = {585}, + number = {7825}, + pages = {357--362}, + doi = {10.1038/s41586-020-2649-2}, + publisher = {Springer Science and Business Media {LLC}}, + url = {https://doi.org/10.1038/s41586-020-2649-2} +} \ No newline at end of file diff --git a/paper/paper.md b/paper/paper.md index 31830e18..944a3ae6 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -1,5 +1,5 @@ --- -title: 'Million Points of Light (MPoL): A Python package for imaging and inference with astronomical interferometric data' +title: 'Million Points of Light (MPoL): a PyTorch library for radio interferometric imaging and inference' tags: - Python - astronomy @@ -7,6 +7,8 @@ tags: - fourier - radio astronomy - radio interferometry + - machine learning + - neural networks authors: - name: Ian Czekala orcid: 0000-0002-1483-8811 @@ -52,46 +54,57 @@ affiliations: # index: 3 # - name: National Radio Astronomy Observatory, Charlottesville, VA, USA # index: 4 -date: 12 December 2024 +date: 16 December 2024 bibliography: paper.bib aas-journal: Astrophysical Journal --- -* 1000 words -* A summary describing the high-level functionality and purpose of the software for a diverse, non-specialist audience. -* A clear statement of need that illustrates the purpose of the software. -* A description of how this software compares to other commonly-used packages in this research area. -* Mentions (if applicable) of any ongoing research projects using the software or recent scholarly publications enabled by it. -* A list of key references including a link to the software archive. - # Summary -Interferometric imaging is the process of recovering a spatial domain image from a Fourier domain signal that is only partially sampled. The technique is applied in a large number of fields from medical imaging to remote sensing, optics and astronomy. Within astronomy, interferometry conducted at radio, infrared and optical frequencies yields unparalleled spatial resolution in an image, corresponding to physical scales that are otherwise inaccessibly small. *Million Points of Light* (`MPoL`) is a Python package for astronomical interferometric imaging. It couples a statistical modeling framework with an efficient computational implementation to reconstruct images of astronomical sources from data measured by large telescopes such as the Atacama Large Millimeter/Submillimeter Array (ALMA). +Astronomical radio interferometers achieve exquisite angular resolution by cross-correlating signal from a cosmic source simultaneously observed by distant pairs of radio telescopes to produce a Fourier-type measurement called a visibility. *Million Points of Light* (`MPoL`) is a Python library supporting feed-forward modeling of interferometric visibility datasets for synthesis imaging and parametric Bayesian inference, built using the autodifferentiable machine learning framework PyTorch. Neural network components provide a rich set of modular and composable building blocks that can be used to express the physical relationships between latent model parameters and observed data following the radio interferometric measurement equation. Industry-grade optimizers make it straightforward to simultaneously solve for the synthesized image and calibration parameters using stochastic gradient descent. # Statement of need -Accurately reconstructing an image from sparse Fourier data is an ill-posed problem that remains an outstanding challenge in astronomical research, particularly in sub-mm astronomy. There, the current standard approach to interferometric imaging is `CLEAN` [@hogbom_1974; @clark_1980], an empirical, algorithmic procedure that requires a high degree of user intervention. The algorithm is not computationally efficient and thus not practical for large datasets (~100 GB) that are becoming increasingly common in the field. And the enclosing software lacks the accessibility and up-to-date documentation to easily modify the algorithm for custom use cases [@mcmullin_2007; @casa_2022]. Collectively these limitations necessitate an alternative imaging formalism and software implementation. +When an astrophysical source is observed by a radio interferometer, it is typical for there to be gaps in the spatial frequency coverage. Therefore, rather than a direct Fourier inversion, images must be synthesized from the visibility data using an imaging algorithm; it is common for the incomplete sampling to severely hamper image reconstruction. CLEAN is the traditional image synthesis algorithm of the radio interferometry community [@hogbom74; see @thompson17, Ch. 11, for a review], with a modern implementation in the facility software CASA [@mcmullin07; @casa22]. CLEAN excels at the rapid imaging of astronomical fields comprising unresolved point sources (e.g. quasars) and marginally resolved sources, but often struggles to achieve the desired imaging performance for spatially extended sources [@disk20, §3]. + +In the field of planet formation alone, spatially resolved observations from the Atacama Large Millimeter Array (ALMA) have rapidly advanced our understanding of protoplanetary disk structures [@andrews20], kinematic signatures of embedded protoplanets [@pinte18], and circumplanetary disks [@benisty21]. High fidelity imaging algorithms for spatially resolved sources are needed to realize the full scientific potential of groundbreaking observatories like ALMA [@wootten09], the Event Horizon Telescope [@eht19a], and the Square Kilometer Array [@dewdney09] as they deliver significantly improved sensitivity and resolving power compared to previous generation instruments. Moreover, there is an opportunity for a flexible, open-source platform to interface with advanced algorithms provided by machine learning and computational imaging software from non-astronomy fields. + +# The Million Points of Light (MPoL) library + + +`MPoL` is a library designed for the feed-forward modeling of interferometric datasets using Python, Numpy [@harris20], and the computationally performant machine learning framework PyTorch [@paszke19]. `MPoL` implements a set of primatives and foundational components using PyTorch `nn.module` which can be easily arranged to meet the requirements of the forward modeling problem at hand. Rather than provide a standalone, end-to-end imaging solution, `MPoL`'s philosophy is to closely integrate with the PyTorch ecosystem so that users can leverage powerful PyTorch idioms for machine learning workflows. Because of this design decision, the user can transparently tap into the rich PyTorch ecosystem: GPU acceleration, parametric inference, and other trained neural network models. + + +Thus far, MPoL has focused on workflows related to the synthesis of images of astrophysical sources using continuum, spectral line data. Thus far, our group has worked with protoplanetary disks, since that's our speciality, but code is more general to everything. These work using Regularized Maximum Likelihood (RML) principles. Primatives for spherical coordinates. In a feed-forward sense, Foundational components for Pixel basis / image layer, Fourier layers to transform, which can either be gridded or Interpolation to individual baselines possible using NuFFT using kbnufft [@nufft20]. Then, comparison to data using common loss functions. However, because of the aforementioned sparsity of spatial frequency samples, there are a number of images fully consistent with the data, and so regularization is required. Regularization in the form of entropy, sparsity, or custom forms, as presently used in the field (cite EHT paper for summary). Demonstrated that higher resolution images can be obtained than CLEAN (cite Zawavzkdi). It is expected users will write programs, define objective functions, which will then be optimized using PyTorch optimization, with stochastic gradient descent or industry-grade algorithms like adam (CITE). -# The Million Points of Light library + +Also, parametric work akin to . Enables parametric inference, workflows similar to Galario (**CITE**), but with automatic differentiation enabled, these can be done much faster using Hamiltonian Monte Carlo. And, 1D work as well. More exotic forms of regularization, such as diffusion models, score based priors, etc. are in theory possible using the framework. -Built on PyTorch for idiomatic expression of radio interferometry problems, including using stochastic gradient descent. +The library also provides convenience routines like classic dirty imager (varying robust values and taper) to determine starting point image and to check export of data. -`MPoL` is a statistically robust, nonparametric modeling approach to interferometric imaging in a user-friendly, well-documented package that is computationally performant. The software is designed to be applied to reconstruction of an individual image or an entire 'cube' of tens to hundreds of images of an astronomical source observed at different frequencies. The images obtained are of simultaneously higher spatial resolution and sensitivity than their counterparts produced by `CLEAN`. Programatically, `MPoL` is built on `PyTorch`, using its auto-differentiation capabilities to drive likelihood optimization with gradient descent and its parallelization support to optionally accelerate the imaging workflow on GPUs and TPUs. The imaging framework in `MPoL` is also flexible, with the ability to easily add alternative or additional priors into likelihood calculation. Extensions to the core functionality are actively developed, such as the recent implementation of parametric inference with `Pyro`, as are further optimizations to the core routines. +Machine learning frameworks provide a natural language with which to build an expressive and realistic and differentiable forward-model of a radio interferometric dataset, which can then be optimized using stochastic gradient descent to perform image synthesis. We believe this workflow is especially powerful for scientific applications because it enables fine-scale, residual calibration physics to be parameterized and optimized simultaneously with image synthesis. Differentiable physical models have been employed to great success in other astrophysical settings such as exoplanet discovery [@bedell19] and cosmology [@campagne23]. # Documentation and case studies -The main documentation is available at https://mpol-dev.github.io/MPoL/ -The codebase has been used in [@zawadzki_2023]. +MPoL is licensed under an MIT license. The main documentation is available at https://mpol-dev.github.io/MPoL/ and the code is developed in the open at https://github.com/MPoL-dev/MPoL. -![Left: the synthesized image produced by the DSHARP ALMA Large Program [@andrews18] using \texttt{CASA/tclean}. Right: The regularized maximum likelihood image produced using \texttt{MPoL} on the same data. Both images are displayed using a `sqrt` stretch, with upper limit truncated to 70\% and 40\% of max value for CLEAN and \texttt{MPoL}, respectively, to emphasize faint features. The CLEAN algorithm permits negative intensity values, while the `MPoL` algorithm enforces image positivity by construction. Image dimensions are 3 arcseconds to a side. Intensity units are shown in units of Jy/arcsec^2^.](fig.pdf) +Rather than an end-to-end, binary, the user is expected to use MPoL and PyTorch primitives to write short imaging scripts, in much the same way that users write PyTorch scripts for machine learning workflows [official PyTorch examples](https://github.com/pytorch/examples). + +Examples are at **LINK** examples and built on a slower cadence. The codebase has been used in [@zawadzki23]. Huang for parametric modeling [@huang24]. + +![Left: the synthesized image produced by the DSHARP ALMA Large Program [@andrews18] using `CASA/tclean`. Right: The regularized maximum likelihood image produced using `MPoL` on the same data. Both images are displayed using a `sqrt` stretch, with upper limit truncated to 70\% and 40\% of max value for CLEAN and `MPoL`, respectively, to emphasize faint features. The CLEAN algorithm permits negative intensity values, while the `MPoL` algorithm enforces image positivity by construction. Each side of the image is 3 arcseconds. Intensity units are shown in units of Jy/arcsec^2^.](fig.pdf) # Similar tools +Recently, there has been significant work to design robust algorithms to image spatially resolved sources. A non-exhaustive list includes the `RESOLVE` family of algorithms, which impose Gaussian random field image priors, the multi-algorithm approach of the Event Horizon Telescope Collaboration [@eht19d] including regularized maximum likelihood techniques, and domain-specific non-parametric 1D approaches like `frank` [@jennings20]. Several approaches have leveraged deep-learning, such as score-based priors [@dia23], denoising diffusion probabilistic models [@wang23], and residual-to-residual deep neural networks [@dabbech24]. A commonality shared by most imaging alternatives is the way in which a model of the synthesized image is fed-forward to be evaluated against the visibility dataset, which contrasts with the inverse imaging and deconvolution approach employed by CLEAN. + EHT imagers, BASP group, MaxEnt Carcamo and various adherences to the framework in CASA. MPoL is focused as a PyTorch library, and works as glue to integrate with the rich systems of neural networks such as autodifferentiation and probabilistic programming languages. Frankenstein is a 1D non-parametric tool and integrates with MPoL. +By contrast, MPoL aims to be a library, and therefore could be leveraged with the PyTorch ecosystem. + # Acknowledgements We acknowledge funding from an ALMA Development Cycle 8 grant number AST-1519126. ALMA is a partnership of ESO (representing its member states), NSF (USA) and NINS (Japan), together with NRC (Canada), MOST and ASIAA (Taiwan), and KASI (Republic of Korea), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, AUI/NRAO and NAOJ. The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc. From e0d477a4e904a3b48b5f21d8fcaebe9a71aa429c Mon Sep 17 00:00:00 2001 From: Ian Czekala Date: Thu, 26 Dec 2024 18:27:26 +0000 Subject: [PATCH 18/18] first draft to co-authors. --- LICENSE | 2 +- paper/paper.bib | 195 ++++++++++++++++++++++++++++++++++++++++++++++++ paper/paper.md | 35 +++------ 3 files changed, 207 insertions(+), 25 deletions(-) diff --git a/LICENSE b/LICENSE index 12fd66ef..99ebf233 100644 --- a/LICENSE +++ b/LICENSE @@ -1,6 +1,6 @@ MIT License -Copyright (c) 2019 - 2023 Ian Czekala and contributors +Copyright (c) 2019 - 2025 Ian Czekala and contributors Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal diff --git a/paper/paper.bib b/paper/paper.bib index 3a7a1b15..0e166ad6 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -436,4 +436,199 @@ @article{harris20 doi = {10.1038/s41586-020-2649-2}, publisher = {Springer Science and Business Media {LLC}}, url = {https://doi.org/10.1038/s41586-020-2649-2} +} + +@book{condon16, + author = {{Condon}, James J. and {Ransom}, Scott M.}, + title = {{Essential Radio Astronomy}}, + year = 2016, + adsurl = {https://ui.adsabs.harvard.edu/abs/2016era..book.....C}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} + +@article{baydin18, + author = {Atilim Gunes Baydin and Barak A. Pearlmutter and Alexey Andreyevich Radul and Jeffrey Mark Siskind}, + title = {Automatic Differentiation in Machine Learning: a Survey}, + journal = {Journal of Machine Learning Research}, + year = {2018}, + volume = {18}, + number = {153}, + pages = {1--43}, + url = {http://jmlr.org/papers/v18/17-468.html} +} + +@article{loshchilov17, + author = {{Loshchilov}, Ilya and {Hutter}, Frank}, + title = {{Decoupled Weight Decay Regularization}}, + journal = {arXiv e-prints}, + keywords = {Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Mathematics - Optimization and Control}, + year = 2017, + month = nov, + eid = {arXiv:1711.05101}, + pages = {arXiv:1711.05101}, + doi = {10.48550/arXiv.1711.05101}, + archiveprefix = {arXiv}, + eprint = {1711.05101}, + primaryclass = {cs.LG}, + adsurl = {https://ui.adsabs.harvard.edu/abs/2017arXiv171105101L}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} + +@article{pyro19, + author = {Bingham, Eli and Chen, Jonathan P. and Jankowiak, Martin and Obermeyer, Fritz and Pradhan, Neeraj and Karaletsos, Theofanis and Singh, Rohit and Szerlip, Paul and Horsfall, Paul and Goodman, Noah D.}, + title = {Pyro: deep universal probabilistic programming}, + year = {2019}, + issue_date = {January 2019}, + publisher = {JMLR.org}, + volume = {20}, + number = {1}, + issn = {1532-4435}, + abstract = {Pyro is a probabilistic programming language built on Python as a platform for developing advanced probabilistic models in AI research. To scale to large data sets and high-dimensional models, Pyro uses stochastic variational inference algorithms and probability distributions built on top of PyTorch, a modern GPU-accelerated deep learning framework. To accommodate complex or model-specific algorithmic behavior, Pyro leverages Poutine, a library of composable building blocks for modifying the behavior of probabilistic programs.}, + journal = {J. Mach. Learn. Res.}, + month = {jan}, + pages = {973–978}, + numpages = {6}, + keywords = {probabilistic programming, graphical models, generative models, deep learning, approximate Bayesian inference} +} + +@article{tazzari18, + author = {{Tazzari}, M. and {Beaujean}, F. and {Testi}, L.}, + title = {{GALARIO: a GPU accelerated library for analysing radio interferometer observations}}, + journal = {\mnras}, + archiveprefix = {arXiv}, + eprint = {1709.06999}, + primaryclass = {astro-ph.IM}, + keywords = {methods: numerical, techniques: interferometric, submillimetre: general}, + year = 2018, + month = jun, + volume = 476, + pages = {4527-4542}, + doi = {10.1093/mnras/sty409}, + adsurl = {http://adsabs.harvard.edu/abs/2018MNRAS.476.4527T}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} + + +@article{guzman18, + author = {{Guzm{\'a}n}, Viviana V. and {Huang}, Jane and {Andrews}, Sean M. and {Isella}, Andrea and {P{\'e}rez}, Laura M. and {Carpenter}, John M. and {Dullemond}, Cornelis P. and {Ricci}, Luca and {Birnstiel}, Tilman and {Zhang}, Shangjia and {Zhu}, Zhaohuan and {Bai}, Xue-Ning and {Benisty}, Myriam and {{\"O}berg}, Karin I. and {Wilner}, David J.}, + title = {{The Disk Substructures at High Angular Resolution Program (DSHARP). VIII. The Rich Ringed Substructures in the AS 209 Disk}}, + journal = {\apjl}, + keywords = {dust, extinction, protoplanetary disks, techniques: high angular resolution, Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Astrophysics of Galaxies}, + year = 2018, + month = dec, + volume = {869}, + number = {2}, + eid = {L48}, + pages = {L48}, + doi = {10.3847/2041-8213/aaedae}, + archiveprefix = {arXiv}, + eprint = {1812.04046}, + primaryclass = {astro-ph.SR}, + adsurl = {https://ui.adsabs.harvard.edu/abs/2018ApJ...869L..48G}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} + + +@article{dia23, + author = {{Dia}, Noe and {Yantovski-Barth}, M.~J. and {Adam}, Alexandre and {Bowles}, Micah and {Lemos}, Pablo and {Scaife}, Anna M.~M. and {Hezaveh}, Yashar and {Perreault-Levasseur}, Laurence}, + title = {{Bayesian Imaging for Radio Interferometry with Score-Based Priors}}, + journal = {arXiv e-prints}, + keywords = {Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Computer Vision and Pattern Recognition}, + year = 2023, + month = nov, + eid = {arXiv:2311.18012}, + pages = {arXiv:2311.18012}, + doi = {10.48550/arXiv.2311.18012}, + archiveprefix = {arXiv}, + eprint = {2311.18012}, + primaryclass = {astro-ph.IM}, + adsurl = {https://ui.adsabs.harvard.edu/abs/2023arXiv231118012D}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} + + +@article{huang18b, + author = {{Huang}, Jane and {Andrews}, Sean M. and {P{\'e}rez}, Laura M. and {Zhu}, Zhaohuan and {Dullemond}, Cornelis P. and {Isella}, Andrea and {Benisty}, Myriam and {Bai}, Xue-Ning and {Birnstiel}, Tilman and {Carpenter}, John M. and {Guzm{\'a}n}, Viviana V. and {Hughes}, A. Meredith and {{\"O}berg}, Karin I. and {Ricci}, Luca and {Wilner}, David J. and {Zhang}, Shangjia}, + title = {{The Disk Substructures at High Angular Resolution Project (DSHARP). III. Spiral Structures in the Millimeter Continuum of the Elias 27, IM Lup, and WaOph 6 Disks}}, + journal = {\apjl}, + keywords = {dust, extinction, protoplanetary disks, techniques: high angular resolution, Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Astrophysics of Galaxies}, + year = 2018, + month = dec, + volume = {869}, + number = {2}, + eid = {L43}, + pages = {L43}, + doi = {10.3847/2041-8213/aaf7a0}, + archiveprefix = {arXiv}, + eprint = {1812.04193}, + primaryclass = {astro-ph.SR}, + adsurl = {https://ui.adsabs.harvard.edu/abs/2018ApJ...869L..43H}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} + +@article{foreman-mackey13, + author = {{Foreman-Mackey}, Daniel and {Hogg}, David W. and {Lang}, Dustin and {Goodman}, Jonathan}, + title = {{emcee: The MCMC Hammer}}, + journal = {\pasp}, + keywords = {Astrophysics - Instrumentation and Methods for Astrophysics, Physics - Computational Physics, Statistics - Computation}, + year = 2013, + month = mar, + volume = {125}, + number = {925}, + pages = {306}, + doi = {10.1086/670067}, + archiveprefix = {arXiv}, + eprint = {1202.3665}, + primaryclass = {astro-ph.IM}, + adsurl = {https://ui.adsabs.harvard.edu/abs/2013PASP..125..306F}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} + +@article{hoffman14, + title = {The No-U-Turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo.}, + author = {Hoffman, Matthew D and Gelman, Andrew and others}, + journal = {J. Mach. Learn. Res.}, + volume = {15}, + number = {1}, + pages = {1593--1623}, + year = {2014} +} + +@article{neal12, + title = {MCMC using Hamiltonian dynamics}, + author = {Neal, Radford M}, + journal = {arXiv preprint arXiv:1206.1901}, + year = {2012} +} + +@article{hamaker96, + author = {{Hamaker}, J.~P. and {Bregman}, J.~D. and {Sault}, R.~J.}, + title = {{Understanding radio polarimetry. I. Mathematical foundations.}}, + journal = {\aaps}, + keywords = {METHODS: ANALYTICAL, METHODS: DATA ANALYSIS, TECHNIQUES: INTERFEROMETERS, TECHNIQUES: POLARIMETERS}, + year = 1996, + month = may, + volume = {117}, + pages = {137-147}, + adsurl = {https://ui.adsabs.harvard.edu/abs/1996A&AS..117..137H}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} + + +@article{smirnov11a, + author = {{Smirnov}, O.~M.}, + title = {{Revisiting the radio interferometer measurement equation. I. A full-sky Jones formalism}}, + journal = {\aap}, + keywords = {methods: numerical, methods: analytical, methods: data analysis, techniques: interferometric, techniques: polarimetric, Astrophysics - Instrumentation and Methods for Astrophysics}, + year = 2011, + month = mar, + volume = {527}, + eid = {A106}, + pages = {A106}, + doi = {10.1051/0004-6361/201016082}, + archiveprefix = {arXiv}, + eprint = {1101.1764}, + primaryclass = {astro-ph.IM}, + adsurl = {https://ui.adsabs.harvard.edu/abs/2011A&A...527A.106S}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} } \ No newline at end of file diff --git a/paper/paper.md b/paper/paper.md index 944a3ae6..2cdb63c0 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -65,45 +65,32 @@ Astronomical radio interferometers achieve exquisite angular resolution by cross # Statement of need -When an astrophysical source is observed by a radio interferometer, it is typical for there to be gaps in the spatial frequency coverage. Therefore, rather than a direct Fourier inversion, images must be synthesized from the visibility data using an imaging algorithm; it is common for the incomplete sampling to severely hamper image reconstruction. CLEAN is the traditional image synthesis algorithm of the radio interferometry community [@hogbom74; see @thompson17, Ch. 11, for a review], with a modern implementation in the facility software CASA [@mcmullin07; @casa22]. CLEAN excels at the rapid imaging of astronomical fields comprising unresolved point sources (e.g. quasars) and marginally resolved sources, but often struggles to achieve the desired imaging performance for spatially extended sources [@disk20, §3]. +When an astrophysical source is observed by a radio interferometer, there are typically gaps in the spatial frequency coverage. Therefore, rather than perform a direct Fourier inversion, images must be synthesized from the visibility data using an imaging algorithm; it is common for the incomplete sampling to severely hamper image fidelity [@condon16; @thompson17]. CLEAN is the traditional image synthesis algorithm of the radio interferometry community [@hogbom74], with a modern implementation in the facility software CASA [@mcmullin07; @casa22]. CLEAN excels at the rapid imaging of astronomical fields comprising unresolved point sources (e.g. quasars) and marginally resolved sources, but often struggles to achieve the desired imaging performance for spatially extended sources [@disk20, §3]. -In the field of planet formation alone, spatially resolved observations from the Atacama Large Millimeter Array (ALMA) have rapidly advanced our understanding of protoplanetary disk structures [@andrews20], kinematic signatures of embedded protoplanets [@pinte18], and circumplanetary disks [@benisty21]. High fidelity imaging algorithms for spatially resolved sources are needed to realize the full scientific potential of groundbreaking observatories like ALMA [@wootten09], the Event Horizon Telescope [@eht19a], and the Square Kilometer Array [@dewdney09] as they deliver significantly improved sensitivity and resolving power compared to previous generation instruments. Moreover, there is an opportunity for a flexible, open-source platform to interface with advanced algorithms provided by machine learning and computational imaging software from non-astronomy fields. +In the field of planet formation alone, spatially resolved observations from the Atacama Large Millimeter Array (ALMA; @wootten09) have rapidly advanced our understanding of protoplanetary disk structures [@andrews20], kinematic signatures of embedded protoplanets [@pinte18], and circumplanetary disks [@benisty21]. High fidelity imaging algorithms for spatially resolved sources are needed to realize the full scientific potential of groundbreaking observatories like ALMA, the Event Horizon Telescope [@eht19a], and the Square Kilometer Array [@dewdney09] as they deliver significantly improved sensitivity and resolving power compared to previous generation instruments. Moreover, there is an opportunity for a flexible, open-source platform to interface with machine learning algorithms and computational imaging software from non-astronomy fields. # The Million Points of Light (MPoL) library - -`MPoL` is a library designed for the feed-forward modeling of interferometric datasets using Python, Numpy [@harris20], and the computationally performant machine learning framework PyTorch [@paszke19]. `MPoL` implements a set of primatives and foundational components using PyTorch `nn.module` which can be easily arranged to meet the requirements of the forward modeling problem at hand. Rather than provide a standalone, end-to-end imaging solution, `MPoL`'s philosophy is to closely integrate with the PyTorch ecosystem so that users can leverage powerful PyTorch idioms for machine learning workflows. Because of this design decision, the user can transparently tap into the rich PyTorch ecosystem: GPU acceleration, parametric inference, and other trained neural network models. +`MPoL` is a library designed for feed-forward modeling of interferometric datasets using Python, Numpy [@harris20], and the computationally performant machine learning framework PyTorch [@paszke19], which debuted with @zawadzki23. `MPoL` implements a set of foundational interferometry components using PyTorch `nn.module`, which can be easily combined to build a forward-model of the interferometric dataset(s) at hand. We strive to seamlessly integrate with the PyTorch ecosystem so that users can easily leverage well-established machine learning workflows: optimization with stochastic gradient descent, straightforward acceleration with GPU(s), and integration with common neural network architectures. - -Thus far, MPoL has focused on workflows related to the synthesis of images of astrophysical sources using continuum, spectral line data. Thus far, our group has worked with protoplanetary disks, since that's our speciality, but code is more general to everything. These work using Regularized Maximum Likelihood (RML) principles. Primatives for spherical coordinates. In a feed-forward sense, Foundational components for Pixel basis / image layer, Fourier layers to transform, which can either be gridded or Interpolation to individual baselines possible using NuFFT using kbnufft [@nufft20]. Then, comparison to data using common loss functions. However, because of the aforementioned sparsity of spatial frequency samples, there are a number of images fully consistent with the data, and so regularization is required. Regularization in the form of entropy, sparsity, or custom forms, as presently used in the field (cite EHT paper for summary). Demonstrated that higher resolution images can be obtained than CLEAN (cite Zawavzkdi). It is expected users will write programs, define objective functions, which will then be optimized using PyTorch optimization, with stochastic gradient descent or industry-grade algorithms like adam (CITE). +In a typical feed-forward workflow, `MPoL` users will use foundational components like `BaseCube` and `ImageCube` to define the true-sky model, Fourier layers like `FourierCube` or `NuFFT` [wrapping `torchkbnufft`; @nufft20] to apply the Fourier transform and sample the visibility function at the location of the array baselines, and the negative log likelihood to calculate a data loss. Backpropagation [see @baydin18 for a review] and stochastic gradient descent [e.g., AdamW; @loshchilov17] are used to find the true-sky model that minimizes the loss function. However, because of the aforementioned gaps in spatial frequency coverage, there is technically an infinite number of true-sky images fully consistent with the data likelihood, so regularization loss terms are required. `MPoL` supports Regularized Maximum Likelihood (RML) imaging with common regularizers like maximum entropy, sparsity, and others [e.g., as used in @eht19d]; users can also implement custom regularizers with PyTorch. - -Also, parametric work akin to . Enables parametric inference, workflows similar to Galario (**CITE**), but with automatic differentiation enabled, these can be done much faster using Hamiltonian Monte Carlo. And, 1D work as well. More exotic forms of regularization, such as diffusion models, score based priors, etc. are in theory possible using the framework. +`MPoL` also provides several other workflows relevant to astrophysical research. First, by seamlessly coupling with the probabilistic programming language Pyro [@pyro19], `MPoL` supports Bayesian parametric inference of astronomical sources. Second, users can implement additional data calibration components as their data requires, enabling fine-scale, residual calibration physics to be parameterized and optimized simultaneously with image synthesis [following the radio interferometric measurement equation @hamaker96; @smirnov11a]. Finally, the library also provides convenience utilities like `DirtyImager` (with Briggs robust and UV taper) to confirm the data has been loaded correctly. -The library also provides convenience routines like classic dirty imager (varying robust values and taper) to determine starting point image and to check export of data. +# Documentation, examples, and scientific results -Machine learning frameworks provide a natural language with which to build an expressive and realistic and differentiable forward-model of a radio interferometric dataset, which can then be optimized using stochastic gradient descent to perform image synthesis. We believe this workflow is especially powerful for scientific applications because it enables fine-scale, residual calibration physics to be parameterized and optimized simultaneously with image synthesis. Differentiable physical models have been employed to great success in other astrophysical settings such as exoplanet discovery [@bedell19] and cosmology [@campagne23]. +MPoL is freely available, open-source software licensed via the MIT license and is developed on GitHub at [MPoL-dev/MPoL](https://github.com/MPoL-dev/MPoL). Installation and API documentation is hosted at [https://mpol-dev.github.io/MPoL/](https://mpol-dev.github.io/MPoL/), and is continuously built with each commit to the `main` branch. As a library, `MPoL` expects researchers to write short scripts using use `MPoL` and PyTorch primitives, in much the same way that PyTorch users write scripts for machine learning workflows (e.g., as in the [official PyTorch examples](https://github.com/pytorch/examples)). `MPoL` example projects are hosted on GitHub at [MPoL-dev/examples](https://github.com/MPoL-dev/examples). These include an introduction to generating mock data, a quickstart using stochastic gradient descent, and a Pyro workflow using SVI to replicate the parametric inference done in @guzman18, among others. In Figure \ref{imlup}, we compare an image obtained with CLEAN to that using `MPoL` and RML, synthesized from the data presented in @huang18b, highlighting the improvement in resolution offered by feed-forward modeling technologies. -# Documentation and case studies +`MPoL` has already been used in a number of scientific publications. @zawadzki23 introduced `MPoL` and explored RML imaging for ALMA observations of protoplanetary disks, finding a 3x improvement in spatial resolution at comparable sensitivity. @dia23 used `MPoL` as a reference imaging implementation to evaluate the performance of their score-based prior algorithm. @huang24 used the parametric inference capabilities of `MPoL` to analyze radial dust substructures in a suite of eight protoplanetary disks in $\sigma$ Orionis stellar cluster. `MPoL` was selected as an imaging technology of the exoALMA large program, where Zawadzki et al. 2024 *submitted* used RML imaging to obtain high resolution image cubes of non-Keplerian features in protoplanetary disks. - -MPoL is licensed under an MIT license. The main documentation is available at https://mpol-dev.github.io/MPoL/ and the code is developed in the open at https://github.com/MPoL-dev/MPoL. - -Rather than an end-to-end, binary, the user is expected to use MPoL and PyTorch primitives to write short imaging scripts, in much the same way that users write PyTorch scripts for machine learning workflows [official PyTorch examples](https://github.com/pytorch/examples). - -Examples are at **LINK** examples and built on a slower cadence. The codebase has been used in [@zawadzki23]. Huang for parametric modeling [@huang24]. - -![Left: the synthesized image produced by the DSHARP ALMA Large Program [@andrews18] using `CASA/tclean`. Right: The regularized maximum likelihood image produced using `MPoL` on the same data. Both images are displayed using a `sqrt` stretch, with upper limit truncated to 70\% and 40\% of max value for CLEAN and `MPoL`, respectively, to emphasize faint features. The CLEAN algorithm permits negative intensity values, while the `MPoL` algorithm enforces image positivity by construction. Each side of the image is 3 arcseconds. Intensity units are shown in units of Jy/arcsec^2^.](fig.pdf) +![Left: the synthesized image produced by the DSHARP ALMA Large Program [@andrews18] using `CASA/tclean`. Right: The regularized maximum likelihood image produced using `MPoL` on the same data. Both images are displayed using a `sqrt` stretch, with upper limit truncated to 70\% and 40\% of max value for CLEAN and `MPoL`, respectively, to emphasize faint features. The CLEAN algorithm permits negative intensity values, while the `MPoL` algorithm enforces image positivity by construction. Each side of the image is 3 arcseconds. Intensity units are shown in units of Jy/arcsec^2^. \label{imlup}](fig.pdf) # Similar tools -Recently, there has been significant work to design robust algorithms to image spatially resolved sources. A non-exhaustive list includes the `RESOLVE` family of algorithms, which impose Gaussian random field image priors, the multi-algorithm approach of the Event Horizon Telescope Collaboration [@eht19d] including regularized maximum likelihood techniques, and domain-specific non-parametric 1D approaches like `frank` [@jennings20]. Several approaches have leveraged deep-learning, such as score-based priors [@dia23], denoising diffusion probabilistic models [@wang23], and residual-to-residual deep neural networks [@dabbech24]. A commonality shared by most imaging alternatives is the way in which a model of the synthesized image is fed-forward to be evaluated against the visibility dataset, which contrasts with the inverse imaging and deconvolution approach employed by CLEAN. - -EHT imagers, BASP group, MaxEnt Carcamo and various adherences to the framework in CASA. MPoL is focused as a PyTorch library, and works as glue to integrate with the rich systems of neural networks such as autodifferentiation and probabilistic programming languages. +Recently, there has been significant work to design robust algorithms to image spatially resolved sources. A non-exhaustive list includes the `RESOLVE` family of algorithms, which impose Gaussian random field image priors, the multi-algorithm approach of the Event Horizon Telescope Collaboration [@eht19d] including regularized maximum likelihood techniques, and domain-specific non-parametric 1D approaches like `frank` [@jennings20]. Several approaches have leveraged deep-learning, such as score-based priors [@dia23], denoising diffusion probabilistic models [@wang23], and residual-to-residual deep neural networks [@dabbech24]. By contrast to many imaging software programs, `MPoL` is designed as a library, and so in theory can support a variety of forward-modeling workflows. The MPoL-dev organization also develops the [MPoL-dev/visread](https://mpol-dev.github.io/visread/) package, which is designed to facilitate the extraction of visibility data from CASA's Measurement Set format for use in alternative imaging workflows. -Frankenstein is a 1D non-parametric tool and integrates with MPoL. +The parametric modeling capabilities of `MPoL`, provided by integration with `Pyro`, are similar to the `emcee` [@foreman-mackey13] + synthetic visibility workflow provided by the Galario software [@tazzari18]. Since PyTorch enables automatic differentiation, `Pyro` users can utilize HMC/NUTS sampling [@neal12; @hoffman14], which offers significant benefits in high dimensional spaces compared to ensemble MCMC samplers. -By contrast, MPoL aims to be a library, and therefore could be leveraged with the PyTorch ecosystem. # Acknowledgements