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Million Points of Light (MPoL): a PyTorch library for radio interferometric imaging and inference

T0 review · 0 major / 5 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read MPoL rebuilds radio interferometry imaging as a feed-forward PyTorch modeling problem.

desk verdict A solid, well-supported software description for a library already in active use; the main caveat is a cosmetic Figure 1 comparison and one over-strong abstract phrase. read the letter →

arxiv 2502.00100 v1 pith:QYP3NEP2 submitted 2025-01-31 astro-ph.IM astro-ph.EP

Ian Czekala , Jeff Jennings , Brianna Zawadzki , Kadri Nizam , Ryan Loomis , Megan Delamer , Kaylee de Soto , Robert Frazier
show 4 more authors
Hannah Grzybowski Jane Huang Mary Ogborn Tyler Quinn
This is my paper · ORCID
classification astro-ph.IMastro-ph.EP
keywords radiointerferometrysynthesisimagingregularizedmaximumlikelihoodBayesianinferenceautomaticdifferentiationprotoplanetarydisksPyTorchvisibilitymodeling
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper presents MPoL, an open-source library that lets astronomers model radio interferometer data forward: define the sky brightness, Fourier transform it, and sample the resulting visibilities at the positions the telescope actually measured. Because every step is built on PyTorch's automatic differentiation, the library can solve for an image by stochastic gradient descent, and it can do so jointly with calibration parameters. The same components support regularized maximum likelihood imaging, where priors fill in the gaps left by incomplete spatial-frequency coverage, and Bayesian parametric inference through the probabilistic language Pyro. The paper argues this approach produces higher-fidelity images of resolved sources like protoplanetary disks than the traditional CLEAN algorithm, and that its modular design lets researchers combine imaging with custom regularizers and physical models.

What carries the argument

The load-bearing object is the radio interferometric measurement equation, implemented as a chain of PyTorch nn.Module layers: an image-of-the-sky cube, a Fourier transform layer (FourierCube or a wrapper around torchkbnufft) that evaluates the visibility function at the observed baseline coordinates, and a negative log-likelihood loss. Automatic differentiation through these layers is what turns image synthesis into an optimization problem, and the modularity is what lets regularizers and calibration parameters be dropped into the same graph. The library's distinctive move is to treat the telescope's measurement process, not the image, as the thing being modeled.

What would settle it

Synthesize a mock protoplanetary disk with known compact substructure, simulate visibilities with the same uv-coverage and noise as the DSHARP data, and image it with both MPoL/RML and CLEAN; if the MPoL image does not recover the injected substructure with lower bias or better resolution at matched sensitivity, the advertised fidelity gain is not real.

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Extended reading notes

Core claim

The central claim is that synthesis imaging need not be a special-purpose reconstruction; it can be expressed as a library of composable PyTorch modules that implement the radio interferometric measurement equation. MPoL's components, such as an image cube for the true sky and a non-uniform fast Fourier transform for visibility sampling, form a differentiable path from model parameters to data likelihood. Backpropagation then provides gradients for the image and for calibration terms at once, and regularization terms handle the ill-posedness caused by gaps in the uv-plane. With Pyro, the same forward model supports posterior sampling over source parameters, and the paper's example on DSHARP data shows visibly finer structure than a CLEAN image; the authors' earlier work quantifies this as a threefold resolution improvement at comparable sensitivity.

Load-bearing premise

The load-bearing premise is that a regularized forward model recovers finer, more truthful structure than CLEAN on real resolved sources, a claim that here rests on visual comparison rather than quantified metrics.

Editorial extensions

If this is right

  • A researcher can synthesize an image from raw visibilities with a short script, using the same stochastic gradient descent optimizers standard in deep learning.
  • Image and calibration parameters can be optimized together in one pass, so residual gain, phase, or antenna errors can be included in the model.
  • Parametric Bayesian fits become feasible with gradient-based samplers, which scale better than ensemble MCMC in high-dimensional source models.
  • New regularizers and priors are just PyTorch modules, so the machinery can absorb machine-learning priors without changing the data model.
  • The library's existing use on ALMA disk observations points to a workflow that generalizes from continuum to line-emission image cubes.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If this forward-modeling workflow becomes standard, CLEAN may persist mainly as a fast diagnostic or initialization rather than the final scientific image.
  • Because the graph is differentiable end to end, the same library could fit physical disk parameters (kinematics, radial profiles) directly to visibilities, bypassing image reconstruction entirely.
  • A mock-data recovery benchmark with injected substructure and matched uv-coverage would quantify the resolution claim and separate the effect of the regularizer from the effect of the forward-modeling formulation.
  • The architecture should extend to polarized or time-variable emission by swapping the measurement-equation layer, if the corresponding Jones terms are implemented.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

0 major / 5 minor

Summary. This manuscript describes MPoL, an open-source PyTorch library for forward modeling of radio interferometric visibility data. The library provides composable components for sky models (BaseCube, ImageCube), Fourier transforms and sampling (FourierCube, NuFFT), negative log-likelihood losses, regularization terms, and Pyro integration for Bayesian parametric inference. The paper presents the library's architecture, documentation and example ecosystem, a CLEAN-versus-MPoL image comparison, and lists several peer-reviewed applications, including Zawadzki et al. (2023), Dia et al. (2023), and Huang et al. (2024).

Significance. If the central capability claim holds, MPoL fills a genuine niche by enabling automatic differentiation, GPU acceleration, and probabilistic programming in interferometric imaging, beyond the traditional CLEAN and RML tools. The claim is credible: the repository is open, the component list is concrete, and the peer-reviewed applications provide external usage validation. The primary in-paper weakness is that the resolution-improvement motivation is under-quantified, but this does not undermine the core software claim that the library exists and performs forward-modeling, RML, and Bayesian inference workflows.

minor comments (5)
  1. [Figure 1 and accompanying text] The claim that this comparison 'highlight[s] the improvement in resolution offered by feed-forward modeling technologies' is not sufficiently supported because the two images are truncated at different fractions (70% versus 40% of maximum) and no quantitative resolution or fidelity metric is reported; please add a quantitative comparison or soften the wording to indicate that the figure illustrates an MPoL RML image.
  2. [Abstract versus 'The Million Points of Light library'] The abstract states that optimizers make it 'straightforward to simultaneously solve for the synthesized image and calibration parameters,' while the body only says that users 'can implement additional data calibration components'; revise the abstract to match the current scope or explicitly describe the available calibration modules.
  3. [Documentation, examples, and scientific results] The citation 'Zawadzki et al. 2024submitted' is mentioned in the text but does not appear in the reference list; provide a complete citation or mark it as in preparation.
  4. [Documentation, examples, and scientific results] The mention of MPoL-dev/visread lacks a citation or explicit URL beyond the organization name; add a reference or link for reproducibility.
  5. [Statement of need] When citing Zawadzki et al. (2023) for the '3x improvement in spatial resolution' claim, briefly state the metric used in that work so that the claim is transparent to readers who do not consult the cited paper.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central software claim is independently supportable, and the paper's self-citations are not load-bearing.

full rationale

MPoL is a software paper, not a derivation paper: its central claim is that the library supports feed-forward interferometric modeling, RML imaging, and parametric Bayesian inference via PyTorch and Pyro. That claim is directly supported by the open-source repository, the continuously built API documentation, the worked examples, and, importantly, by independent external use: Dia et al. (2023) used MPoL as a reference imaging implementation with no author overlap, providing external grounding. The paper's own scientific-results section cites Zawadzki et al. (2023) for a 3x resolution improvement and Huang et al. (2024) for parametric inference, but these are prior published applications of the same software, not the target claim, and the central software claim does not reduce to them. The Figure 1 comparison between CLEAN and MPoL uses different display truncations (70% versus 40% of maximum) and no quantitative fidelity metric, which weakens the visual support for improved resolution but is not circular: the MPoL image is produced by the library on the same Huang et al. (2018) data and is not defined in terms of the CLEAN image. No equation-level fitting, no parameter fitted to a subset and then predicted, and no ansatz smuggled in via self-citation were found. The paper is self-contained against external benchmarks, so the appropriate finding is no significant circularity.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper introduces no free parameters or invented entities. It depends on standard theoretical and software assumptions: the measurement equation, the accuracy of the wrapped NuFFT, and the quality of the demonstration dataset. These are reasonable domain assumptions but are not independently validated in this manuscript.

assumptions (3)
  • domain assumption The radio interferometric measurement equation (Hamaker et al. 1996; Smirnov 2011) correctly describes the relation between sky brightness and observed visibilities.
    MPoL builds all forward models on this equation; the paper cites it but provides no derivation or validation.
  • domain assumption The torchkbnufft non-uniform FFT implementation produces accurate visibility samples and accurate gradients under PyTorch autodiff.
    MPoL relies on this library for Fourier sampling; the present paper does not test or benchmark its accuracy.
  • domain assumption The ALMA dataset from Huang et al. (2018) used in Figure 1 is suitably calibrated and representative, and the comparison is made on equivalent terms.
    The figure uses this public dataset to illustrate MPoL versus CLEAN, but the paper gives no details of calibration or image fidelity.

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Cite this review

Pith. "Pith review of Million Points of Light (MPoL): a PyTorch library for radio interferometric imaging and inference." pith.science (2026). https://pith.science/paper/QYP3NEP2

@misc{pith2026250200100,
  author       = {Pith},
  title        = {Pith review of: Million Points of Light (MPoL): a PyTorch library for radio interferometric imaging and inference},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QYP3NEP2}},
  note         = {Machine review of arXiv:2502.00100}
}
read the original abstract

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.

Figures

Figures reproduced from arXiv: 2502.00100 by the authors.

Figure 1
Figure 1. Left: the synthesized image produced by the DSHARP ALMA Large [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. exoALMA IX: Regularized Maximum Likelihood Imaging of Non-Keplerian Features

    astro-ph.EP 2025-04 conditional novelty 6.0 of 10

    Regularized maximum likelihood imaging reproduces all non-Keplerian features seen in CLEAN images of seven protoplanetary disks, supporting their physical reality as planet indicators.

Reference graph

Works this paper leans on

39 extracted references · 12 canonical work pages · cited by 1 Pith paper

  1. [1]

    Andrews, S. M. (2020). Observations of Protoplanetary Disk Structures . Annual Review of Astronomy and Astrophysics, 58, 483--528. https://doi.org/10.1146/annurev-astro-031220-010302

    doi:10.1146/annurev-astro-031220-010302 2020

  2. [2]

    M., Huang, J., Pérez, L

    Andrews, S. M., Huang, J., Pérez, L. M., Isella, A., Dullemond, C. P., Kurtovic, N. T., Guzmán, V. V., Carpenter, J. M., Wilner, D. J., Zhang, S., Zhu, Z., Birnstiel, T., Bai, X.-N., Benisty, M., Hughes, A. M., Öberg, K. I., & Ricci, L. (2018). The Disk Substructures at High Angular Resolution Project (DSHARP). I. Motivation, Sample, Calibration, and Over...

    doi:10.3847/2041-8213/aaf741 2018

  3. [3]

    G., Pearlmutter, B

    Baydin, A. G., Pearlmutter, B. A., Radul, A. A., & Siskind, J. M. (2018). Automatic differentiation in machine learning: A survey. Journal of Machine Learning Research, 18(153), 1--43. http://jmlr.org/papers/v18/17-468.html

    work page 2018

  4. [4]

    T., Pérez, L

    Benisty, M., Bae, J., Facchini, S., Keppler, M., Teague, R., Isella, A., Kurtovic, N. T., Pérez, L. M., Sierra, A., Andrews, S. M., Carpenter, J., Czekala, I., Dominik, C., Henning, T., Menard, F., Pinilla, P., & Zurlo, A. (2021). A Circumplanetary Disk around PDS70c . The Astrophysical Journal Letters, 916(1), L2. https://doi.org/10.3847/2041-8213/ac0f83

    doi:10.3847/2041-8213/ac0f83 2021

  5. [5]

    P., Jankowiak, M., Obermeyer, F., Pradhan, N., Karaletsos, T., Singh, R., Szerlip, P., Horsfall, P., & Goodman, N

    Bingham, E., Chen, J. P., Jankowiak, M., Obermeyer, F., Pradhan, N., Karaletsos, T., Singh, R., Szerlip, P., Horsfall, P., & Goodman, N. D. (2019). Pyro: Deep universal probabilistic programming. J. Mach. Learn. Res., 20(1), 973--978

    work page 2019

  6. [6]

    M., & Bishop, H

    Bishop, C. M., & Bishop, H. (2023). Deep learning - foundations and concepts (S. Cham, Ed.; 1st ed.). https://doi.org/https://doi.org/10.1007/978-3-031-45468-4

    doi:10.1007/978-3-031-45468-4 2023

  7. [7]

    E., Casassus, S., Moral, V., & Rannou, F

    Cárcamo, M., Román, P. E., Casassus, S., Moral, V., & Rannou, F. R. (2018). Multi-GPU maximum entropy image synthesis for radio astronomy. Astronomy and Computing, 22, 16--27. https://doi.org/https://doi.org/10.1016/j.ascom.2017.11.003

    doi:10.1016/j.ascom.2017.11.003 2018

  8. [8]

    CASA Team, Bean, B., Bhatnagar, S., Castro, S., Donovan Meyer, J., Emonts, B., Garcia, E., Garwood, R., Golap, K., Gonzalez Villalba, J., Harris, P., Hayashi, Y., Hoskins, J., Hsieh, M., Jagannathan, P., Kawasaki, W., Keimpema, A., Kettenis, M., Lopez, J., Kern, J. (2022). CASA, the Common Astronomy Software Applications for Radio Astronomy . Publications...

    doi:10.1088/1538-3873/ac9642 2022

Show all 39 references
  1. [9]

    Casassus, S., & Cárcamo, M. (2022). Variable structure in the PDS 70 disc and uncertainties in radio-interferometric image restoration . 513(4), 5790--5798. https://doi.org/10.1093/mnras/stac1285

    2022 doi

  2. [10]

    J., & Ransom, S

    Condon, J. J., & Ransom, S. M. (2016). Essential Radio Astronomy

    2016

  3. [11]

    S., & Wiaux, Y

    Dabbech, A., Aghabiglou, A., Chu, C. S., & Wiaux, Y. (2024). CLEANing Cygnus A Deep and Fast with R2D2 . The Astrophysical Journal Letters, 966(2), L34. https://doi.org/10.3847/2041-8213/ad41df

    2024 doi

  4. [12]

    E., Hall, P

    Dewdney, P. E., Hall, P. J., Schilizzi, R. T., & Lazio, T. J. L. W. (2009). The Square Kilometre Array . IEEE Proceedings, 97(8), 1482--1496. https://doi.org/10.1109/JPROC.2009.2021005

    2009

  5. [13]

    J., Adam, A., Bowles, M., Lemos, P., Scaife, A

    Dia, N., Yantovski-Barth, M. J., Adam, A., Bowles, M., Lemos, P., Scaife, A. M. M., Hezaveh, Y., & Perreault-Levasseur, L. (2023). Bayesian Imaging for Radio Interferometry with Score-Based Priors . arXiv e-Prints, arXiv:2311.18012. https://doi.org/10.48550/arXiv.2311.18012

    2023 doi arXiv

  6. [14]

    J., Bae, J., Benisty, M., Bergin, E

    Disk Dynamics Collaboration, Armitage, P. J., Bae, J., Benisty, M., Bergin, E. A., Casassus, S., Czekala, I., Facchini, S., Fung, J., Hall, C., Ilee, J. D., Keppler, M., Kuznetsova, A., Le Gal, R., Loomis, R. A., Lyra, W., Manger, N., Perez, S., Pinte, C., Zhang, K. (2020). Vi...

    2020 doi arXiv

  7. [15]

    Event Horizon Telescope Collaboration. (2019). First M87 Event Horizon Telescope Results. IV. Imaging the Central Supermassive Black Hole . The Astrophysical Journal Letters, 875(1), L4. https://doi.org/10.3847/2041-8213/ab0e85

    2019 doi

  8. [16]

    Event Horizon Telescope Collaboration. . (2019). First M87 Event Horizon Telescope Results. I. The Shadow of the Supermassive Black Hole . The Astrophysical Journal Letters, 875(1), L1. https://doi.org/10.3847/2041-8213/ab0ec7

    2019 doi

  9. [17]

    W., Lang, D., & Goodman, J

    Foreman-Mackey, D., Hogg, D. W., Lang, D., & Goodman, J. (2013). emcee: The MCMC Hammer . 125(925), 306. https://doi.org/10.1086/670067

    2013 doi

  10. [18]

    V., Huang, J., Andrews, S

    Guzmán, V. V., Huang, J., Andrews, S. M., Isella, A., Pérez, L. M., Carpenter, J. M., Dullemond, C. P., Ricci, L., Birnstiel, T., Zhang, S., Zhu, Z., Bai, X.-N., Benisty, M., Öberg, K. I., & Wilner, D. J. (2018). The Disk Substructures at High Angular Resolution Program (DSHAR...

    2018 doi

  11. [19]

    P., Bregman, J

    Hamaker, J. P., Bregman, J. D., & Sault, R. J. (1996). Understanding radio polarimetry. I. Mathematical foundations. 117, 137--147

    1996

  12. [20]

    R., Millman, K

    Harris, C. R., Millman, K. J., Walt, S. J. van der, Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., Kerkwijk, M. H. van, Brett, M., Haldane, A., Río, J. F. del, Wiebe, M., Peterson, P., Oliphant, T. E. ...

    2020 doi

  13. [21]

    D., Gelman, A., & others

    Hoffman, M. D., Gelman, A., & others. (2014). The no-u-turn sampler: Adaptively setting path lengths in hamiltonian monte carlo. J. Mach. Learn. Res., 15(1), 1593--1623

    2014

  14. [22]

    Högbom, J. A. (1974). Aperture Synthesis with a Non-Regular Distribution of Interferometer Baselines . Astronomy and Astrophysics Supplement, 15, 417

    1974

  15. [23]

    M., Pérez, L

    Huang, J., Andrews, S. M., Pérez, L. M., Zhu, Z., Dullemond, C. P., Isella, A., Benisty, M., Bai, X.-N., Birnstiel, T., Carpenter, J. M., Guzmán, V. V., Hughes, A. M., Öberg, K. I., Ricci, L., Wilner, D. J., & Zhang, S. (2018). The Disk Substructures at High Angular Resolution...

    2018 doi

  16. [24]

    Huang, J., Ansdell, M., Birnstiel, T., Czekala, I., Long, F., Williams, J., Zhang, S., & Zhu, Z. (2024). High-resolution ALMA Observations of Richly Structured Protoplanetary Disks in \( \) Orionis . The Astrophysical Journal, 976(1), 132. https://doi.org/10.3847/1538-4357/ad84df

    2024 doi

  17. [25]

    R., Indebetouw, R., Brogan, C

    Hunter, T. R., Indebetouw, R., Brogan, C. L., Berry, K., Chang, C.-S., Francke, H., Geers, V. C., Gómez, L., Hibbard, J. E., Humphreys, E. M., Kent, B. R., Kepley, A. A., Kunneriath, D., Lipnicky, A., Loomis, R. A., Mason, B. S., Masters, J. S., Maud, L. T., Muders, D., Yoon, ...

    2023 doi

  18. [26]

    A., Tazzari, M., Rosotti, G

    Jennings, J., Booth, R. A., Tazzari, M., Rosotti, G. P., & Clarke, C. J. (2020). frankenstein: protoplanetary disc brightness profile reconstruction at sub-beam resolution with a rapid Gaussian process . Monthly Notices of the RAS, 495(3), 3209--3232. https://doi.org/10.1093/m...

    2020 doi

  19. [27]

    R., Selig, M., & Enßlin, T

    Junklewitz, H., Bell, M. R., Selig, M., & Enßlin, T. A. (2016). RESOLVE: A new algorithm for aperture synthesis imaging of extended emission in radio astronomy . 586, A76. https://doi.org/10.1051/0004-6361/201323094

    2016 doi

  20. [28]

    Loshchilov, I., & Hutter, F. (2017). Decoupled Weight Decay Regularization . arXiv e-Prints, arXiv:1711.05101. https://doi.org/10.48550/arXiv.1711.05101

    2017 doi arXiv

  21. [29]

    P., Waters, B., Schiebel, D., Young, W., & Golap, K

    McMullin, J. P., Waters, B., Schiebel, D., Young, W., & Golap, K. (2007). CASA Architecture and Applications . In R. A. Shaw, F. Hill, & D. J. Bell (Eds.), Astronomical data analysis software and systems XVI ASP conference series (Vol. 376, p. 127)

    2007

  22. [30]

    J., Stern, R., Murrell, T., & Knoll, F

    Muckley, M. J., Stern, R., Murrell, T., & Knoll, F. (2020). TorchKbNufft : A high-level, hardware-agnostic non-uniform fast Fourier transform

    2020

  23. [31]

    Neal, R. M. (2012). MCMC using hamiltonian dynamics. arXiv Preprint arXiv:1206.1901

    2012 arXiv

  24. [32]

    Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Chintala, S. (2019). PyTorch: An Imperative ...

    2019 doi arXiv

  25. [33]

    J., Ménard, F., Duchêne, G., Dent, W

    Pinte, C., Price, D. J., Ménard, F., Duchêne, G., Dent, W. R. F., Hill, T., de Gregorio-Monsalvo, I., Hales, A., & Mentiplay, D. (2018). Kinematic Evidence for an Embedded Protoplanet in a Circumstellar Disk . The Astrophysical Journal Letters, 860(1), L13. https://doi.org/10....

    2018 doi

  26. [34]

    Smirnov, O. M. (2011). Revisiting the radio interferometer measurement equation. I. A full-sky Jones formalism . 527, A106. https://doi.org/10.1051/0004-6361/201016082

    2011 doi

  27. [35]

    Tazzari, M., Beaujean, F., & Testi, L. (2018). GALARIO: a GPU accelerated library for analysing radio interferometer observations . 476, 4527--4542. https://doi.org/10.1093/mnras/sty409

    2018 doi

  28. [36]

    R., Moran, J

    Thompson, A. R., Moran, J. M., & Swenson, Jr., George W. (2017). Interferometry and Synthesis in Radio Astronomy, 3rd Edition . https://doi.org/10.1007/978-3-319-44431-4

    2017 doi

  29. [37]

    Wang, R., Chen, Z., Luo, Q., & Wang, F. (2023). A Conditional Denoising Diffusion Probabilistic Model for Radio Interferometric Image Reconstruction . arXiv e-Prints, arXiv:2305.09121. https://doi.org/10.48550/arXiv.2305.09121

    2023 doi arXiv

  30. [38]

    Wootten, A., & Thompson, A. R. (2009). The Atacama Large Millimeter/Submillimeter Array . IEEE Proceedings, 97(8), 1463--1471. https://doi.org/10.1109/JPROC.2009.2020572

    2009

  31. [39]

    A., Quinn, T., Grzybowski, H., Frazier, R

    Zawadzki, B., Czekala, I., Loomis, R. A., Quinn, T., Grzybowski, H., Frazier, R. C., Jennings, J., Nizam, K. M., & Jian, Y. (2023). Regularized Maximum Likelihood Image Synthesis and Validation for ALMA Continuum Observations of Protoplanetary Disks . Publications of the Astro...

    2023 doi

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