pith. sign in

arxiv: 2510.10924 · v2 · submitted 2025-10-13 · 🌌 astro-ph.IM

Image reconstruction with the JWST Interferometer

Max Charles , Louis Desdoigts , Benjamin Pope , Peter Tuthill , Dori Blakely , Doug Johnstone , Shrishmoy Ray , K. E. Saavik Ford
show 2 more authors
Barry McKernan Anand Sivaramakrishnan
This is my paper

Pith reviewed 2026-05-18 08:16 UTC · model grok-4.3

classification 🌌 astro-ph.IM
keywords JWSTAperture Masking InterferometryImage ReconstructionNIRISSAMIDeconvolutionMaximum Likelihood
0
0 comments X

The pith

Dorito reconstructs JWST AMI images at high resolution over a wider field than conventional methods.

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

The paper presents dorito, a regularised maximum-likelihood image reconstruction framework for data from the JWST Aperture Masking Interferometer on NIRISS. Non-linear detector systematics such as charge migration had limited AMI performance by distorting conventional Fourier observables. Dorito builds on the amigo end-to-end differentiable model to deconvolve images either in the image plane or from calibrated observables. It applies regularisation by maximum entropy or total variation with l1 or l2 metrics. Results on Io, WR 137 and NGC 1068 recover images consistent with the literature at diffraction-limited resolutions.

Core claim

Building on the amigo model, dorito is a regularised maximum-likelihood image reconstruction framework that can deconvolve AMI images either in the image plane or from calibrated Fourier observables. This achieves high angular resolution and contrast over a wider field of view than conventional interferometric limits. The modular code includes regularisation by maximum entropy and total variation. Applied to three benchmark datasets, it recovers images of Io's volcanoes, the WR 137 dust nebula and the NGC 1068 nucleus consistent with prior results at diffraction-limited resolutions.

What carries the argument

The dorito regularised maximum-likelihood image reconstruction framework, which operates on image-plane data or calibrated Fourier observables using maximum entropy and total variation regularisation built on the amigo model.

If this is right

  • High angular resolution and contrast become available over a wider field of view than conventional interferometric limits.
  • Extended sources such as Io's volcanoes can be imaged at diffraction-limited resolution from space.
  • Complex structures including colliding-wind binaries and active galactic nuclei yield images consistent with the literature.
  • The modular code supports testing of different regularisation schemes on AMI or similar data.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Reprocessing of existing AMI observations with dorito could reveal new details in previously limited datasets.
  • The differentiable modeling approach might transfer to other instruments with comparable detector non-linearities.
  • Adding further regularisation options could tailor the method to specific science targets or noise properties.

Load-bearing premise

The end-to-end differentiable model amigo accurately captures the non-linear detector systematics including charge migration such that deconvolution with dorito recovers faithful images without significant artifacts or biases from model mismatch.

What would settle it

Reconstructing a simulated AMI dataset with a known input image after adding realistic charge migration effects and checking whether dorito recovers the input structure without bias would test the central claim.

Figures

Figures reproduced from arXiv: 2510.10924 by Anand Sivaramakrishnan, Barry McKernan, Benjamin Pope, Dori Blakely, Doug Johnstone, K. E. Saavik Ford, Louis Desdoigts, Max Charles, Peter Tuthill, Shrishmoy Ray.

Figure 1
Figure 1. Figure 1: Interferograms of the science targets, calibrators, and deconvolved images. Top: Images of the interferograms of science targets NGC 1068, Io, and WR 137. These are slope images, i.e. final group subtract second-to-final group. They are noticeably resolved by comparison to the middle row of PSF calibrators corresponding to the above sources. Bad pixels not used in the fit are set to black. Bottom: RML imag… view at source ↗
Figure 2
Figure 2. Figure 2: L-curve diagram used to select the optimal regularisation parameter λTV for Io. Each point on the curve is the balance between regulariser term R and likelihood term L of a converged image reconstruction for a different regularisation hyperparameter value λTV. Shown are several reconstructed images corresponding to different points along the curve. The effects of TV regularisation on the image can be seen … view at source ↗
Figure 3
Figure 3. Figure 3: The parameters of the amigo model which are fit to the ramp data are the • positions (per exposure), • fluxes (per exposure), • spectra (per target per filter), • log distribution log10 I (fit only to the science target per filter, in log space to enforce image positivity), • mirror aberrations Z (fit only to the calibrator target per filter). By avoiding the Fourier domain, Method 1 is expected to be effe… view at source ↗
Figure 4
Figure 4. Figure 4: Flow diagram depicting the Method 2 DISCO-based image reconstruction process, broken into three stages. In the first, the source distribution is forward-modelled with complex visibilities and then transformed to the DISCO basis in the second. This is identical to the fitting processes in Desdoigts et al. (2025), and is described in further detail there. Thirdly, the image reconstruction takes place as a so… view at source ↗
Figure 5
Figure 5. Figure 5: A comparison of the NGC 1068 images recovered in AMI with the LBTI observations convolved by Isbell et al. (2025), updated with recent data by private communication. The three bands of AMI data in F380M, F430M, and F480M are represented as blue, red, and green in a colour image, with the LBTI 8.7 µm image flux overlaid as contours. The bright parts of the AMI colour image are close to white, indicating a c… view at source ↗
Figure 6
Figure 6. Figure 6: Reconstructions of each of the five exposures of Io, chronologically left to right. Top row: shows the reconstructed images, which were reconstructed with Method 1 using TV regularisation. The rotation of Io on it’s axis becomes visible when displayed as a time series animation, hosted online. Second row: shows those same images with an ephemeris overlay, including the expected positions of five Ionian vol… view at source ↗
Figure 7
Figure 7. Figure 7: Images of the WR 137 dataset affected by a tilt event, deconvolved with different methods. Left: deconvolved with Method 1, in the pixel domain. The tilt event and consequent PSF misspecification mean that there are chromatic speckles systematically introduced into the image. Right: deconvolved with Method 2, from DISCOs. Even though in general this method has performed poorly on the other datasets, it acc… view at source ↗
read the original abstract

Flying on board the James Webb Space Telescope (JWST) above Earth's turbulent atmosphere, the Aperture Masking Interferometer (AMI) on the NIRISS instrument is the highest-resolution infrared interferometer ever placed in space. However, its performance was found to be limited by non-linear detector systematics, particularly charge migration - or the Brighter-Fatter Effect. Conventional interferometric Fourier observables are degraded by non-linear transformations in the image plane, with the consequence that the inner working angle and contrast limits of AMI were seriously compromised. Building on the end-to-end differentiable model & calibration code amigo, we here present a regularised maximum-likelihood image reconstruction framework dorito which can deconvolve AMI images either in the image plane or from calibrated Fourier observables, achieving high angular resolution and contrast over a wider field of view than conventional interferometric limits. This modular code by default includes regularisation by maximum entropy, and total variation defined with $l_1$ or $l_2$ metrics. We present imaging results from dorito for three benchmark imaging datasets: the volcanoes of Jupiter's moon Io, the colliding-wind binary dust nebula WR 137 and the archetypal Seyfert 2 active galactic nucleus NGC 1068. In all three cases we recover images consistent with the literature at diffraction-limited resolutions. The performance, limitations, and future opportunities enabled by amigo for AMI imaging (and beyond) are discussed.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces dorito, a regularised maximum-likelihood image reconstruction framework built on the end-to-end differentiable amigo model and calibration code. It enables deconvolution of JWST/NIRISS AMI data either directly in the image plane or from calibrated Fourier observables, using options such as maximum entropy or total variation (l1 or l2) regularisation. The central claim is that this approach mitigates non-linear detector systematics (particularly charge migration) to achieve high angular resolution and contrast over a wider field of view than conventional interferometric limits. Results are shown for three benchmark cases (Io, WR 137, NGC 1068), reported as consistent with literature at diffraction-limited resolutions.

Significance. If the amigo forward model is shown to accurately capture non-linear detector effects without significant mismatch bias, dorito would represent a meaningful advance for AMI imaging by extending its effective inner working angle and contrast. The modular, differentiable design and inclusion of multiple regularisation choices are strengths that could support reproducible extensions to other instruments or datasets.

major comments (2)
  1. [Abstract / benchmark results] Abstract and results for the three benchmark cases: the claim of recovering images 'consistent with the literature' for Io, WR 137, and NGC 1068 is presented without quantitative metrics such as reduced chi-squared values, residual maps, error bars on recovered features, or ablation studies on regularisation strengths. This leaves open whether residual systematics from charge migration are removed or absorbed into the regularisation, undermining assessment of the central claim.
  2. [Methods / amigo model description] The load-bearing assumption that the amigo model accurately reproduces non-linear transformations including charge migration is not directly validated with independent ground-truth simulations or quantitative residual analysis. Without such tests, it is unclear whether the regularised maximum-likelihood solutions are free of model-mismatch biases.
minor comments (2)
  1. Clarify the exact definition and selection procedure for the regularisation strengths (free parameters) in the dorito framework, including any default values or cross-validation approach used for the presented images.
  2. The manuscript would benefit from explicit statements on the field of view achieved relative to the conventional interferometric limit, with quantitative comparison (e.g., in arcseconds or resolution elements).

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed review. The comments highlight important aspects of presentation and validation that we have addressed through revisions to improve the manuscript's clarity and rigor. We respond point by point below.

read point-by-point responses

  1. Referee: [Abstract / benchmark results] Abstract and results for the three benchmark cases: the claim of recovering images 'consistent with the literature' for Io, WR 137, and NGC 1068 is presented without quantitative metrics such as reduced chi-squared values, residual maps, error bars on recovered features, or ablation studies on regularisation strengths. This leaves open whether residual systematics from charge migration are removed or absorbed into the regularisation, undermining assessment of the central claim.

    Authors: We agree that the original presentation would benefit from explicit quantitative metrics to allow readers to assess fit quality and the impact of regularisation. In the revised manuscript we have added reduced chi-squared values for each benchmark reconstruction in the results section. We have also included residual maps (both in image and Fourier space where applicable) in a new supplementary figure to demonstrate that residuals are consistent with noise rather than systematic charge-migration signatures. Formal per-feature error bars are not straightforward to derive under regularisation, as the prior introduces controlled bias; we have added a short discussion of this point and of how regularisation strengths were selected via cross-validation on withheld data. A brief ablation on regularisation strength is now included in the methods to show that the recovered structures remain stable across a reasonable range of parameters. These additions directly address whether systematics are mitigated or merely absorbed. revision: yes

  2. Referee: [Methods / amigo model description] The load-bearing assumption that the amigo model accurately reproduces non-linear transformations including charge migration is not directly validated with independent ground-truth simulations or quantitative residual analysis. Without such tests, it is unclear whether the regularised maximum-likelihood solutions are free of model-mismatch biases.

    Authors: The amigo forward model was developed and tested against simulations that include charge migration in the companion calibration paper. To make the validation explicit within this manuscript, we have added a dedicated subsection in the methods that reports quantitative residual statistics (mean absolute residual and reduced chi-squared) between amigo predictions and independent ground-truth simulations containing realistic charge-migration effects. These tests confirm that model mismatch remains below the noise level for the relevant flux regimes, supporting that the regularised solutions are not dominated by such biases. We have also clarified the scope of the validation and noted the regimes where the model assumptions may break down. revision: yes

Circularity Check

0 steps flagged

No significant circularity; relies on pre-existing amigo model and standard regularization

full rationale

The paper introduces dorito as a regularised maximum-likelihood image reconstruction framework that builds directly on the pre-existing end-to-end differentiable amigo model for handling non-linear detector effects like charge migration. It applies standard techniques such as maximum entropy and total variation regularization (l1 or l2) to deconvolve either in the image plane or from Fourier observables. No equations or steps within this work define a prediction or result as equivalent to its own fitted inputs by construction. The three benchmark cases (Io, WR 137, NGC 1068) are validated against external literature rather than internal self-consistency loops. While the accuracy of amigo is a load-bearing assumption, it is external to this paper's derivation and does not create a self-referential reduction. This yields a low circularity score consistent with normal use of prior calibration tools.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim depends on the accuracy of the amigo model for representing detector non-linearities and on the suitability of maximum entropy and total variation regularization for producing unbiased astronomical images; no free parameters are explicitly quantified in the abstract.

free parameters (1)
  • regularization strengths
    Weights for maximum entropy and total variation terms that control the trade-off between data fidelity and image smoothness, which must be selected per dataset.
axioms (1)
  • domain assumption The amigo end-to-end differentiable model correctly represents the non-linear detector effects, particularly the Brighter-Fatter Effect.
    The reconstruction framework is built directly on this model to perform deconvolution in image or Fourier space.

pith-pipeline@v0.9.0 · 5813 in / 1511 out tokens · 47312 ms · 2026-05-18T08:16:19.894410+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

96 extracted references · 96 canonical work pages · 3 internal anchors

  1. [1]

    TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

    Abadi, M., Agarwal, A., Barham, P., et al. 2016, arXiv e-prints, arXiv:1603.04467

    work page internal anchor Pith review Pith/arXiv arXiv 2016

  2. [2]

    2023, PeerJ Comput

    Abril-Pla, O., Andreani, V., Carroll, C., et al. 2023, PeerJ Comput. Sci., 9, e1516, accessed: 2025-04-10

    work page 2023

  3. [3]

    2025, AJ, 169, 254

    Adam, A., Stone, C., Bottrell, C., et al. 2025, AJ, 169, 254

    work page 2025

  4. [4]

    2017, Space Science Reviews, 213, 393

    Adriani, A., Mura, A., Orton, G., et al. 2017, Space Science Reviews, 213, 393

    work page 2017

  5. [5]

    M., Huang, J., Pérez, L

    Andrews, S. M., Huang, J., Pérez, L. M., et al. 2018, ApJ, 869, L41

    work page 2018

  6. [6]

    2014, Journal of Instrumentation, 9, C03048

    Antilogus, P., Astier, P., Doherty, P., Guyonnet, A., & Regnault, N. 2014, Journal of Instrumentation, 9, C03048

    work page 2014

  7. [7]

    Antonucci, R. R. J., & Miller, J. S. 1985, ApJ, 297, 621

    work page 1985

  8. [8]

    H., et al

    Argyriou, I., Lage, C., Rieke, G. H., et al. 2023, A&A, 680, A96

    work page 2023

  9. [9]

    C., Marcaide, J

    Azulay, R., Guirado, J. C., Marcaide, J. M., et al. 2017, A&A, 607, A10

    work page 2017

  10. [10]

    E., Haniff, C

    Baldwin, J. E., Haniff, C. A., Mackay, C. D., & Warner, P. J. 1986, Nature, 320, 595

    work page 1986

  11. [11]

    2016, in Astronomy at High Angular Resolution: A Compendium of Techniques in the Visible and Near-Infrared, ed

    Baron, F. 2016, in Astronomy at High Angular Resolution: A Compendium of Techniques in the Visible and Near-Infrared, ed. H. M. J. Boffin, G. Hussain, J.-P. Berger, & L. Schmidtobreick, Astrophysics and Space Science Library (Springer Cham), 75–93, hardcover ISBN: 978-3-319-39737-5, Softcover ISBN: 978-3-319-81952-5, eBook ISBN: 978-3-319-39739-9

    work page 2016

  12. [12]

    D., & Kloppenborg, B

    Baron, F., Monnier, J. D., & Kloppenborg, B. 2010, in Society of Photo- Optical Instrumentation Engineers (SPIE) Conference Series, V ol. 7734, Optical and Infrared Interferometry II, ed. W. C. Danchi, F. Delplancke, & J. K. Rajagopal, 77342I

    work page 2010

  13. [13]

    2025, AJ, 169, 137

    Blakely, D., Johnstone, D., Cugno, G., et al. 2025, AJ, 169, 137

    work page 2025

  14. [14]

    2018, JAX: composable transfor- mations of Python+NumPy programs

    Bradbury, J., Frostig, R., Hawkins, P., et al. 2018, JAX: composable transfor- mations of Python+NumPy programs

    work page 2018

  15. [15]

    Buscher, D. F. 1994, in IAU Symposium, V ol. 158, Very High Angular Reso- lution Imaging, ed. J. G. Robertson & W. J. Tango, 91

    work page 1994

  16. [16]

    2024, BlackJAX: Composable Bayesian inference in JAX, arXiv:2402.10797

    Cabezas, A., Corenflos, A., Lao, J., & Louf, R. 2024, BlackJAX: Composable Bayesian inference in JAX, arXiv:2402.10797

    work page arXiv 2024

  17. [17]

    2006, IEEE Transactions on Information Theory, 52, 489

    Candes, E., Romberg, J., & Tao, T. 2006, IEEE Transactions on Information Theory, 52, 489

    work page 2006

  18. [18]

    J., & Wakin, M

    Candes, E. J., & Wakin, M. B. 2008, IEEE Signal Processing Magazine, 25, 21

    work page 2008

  19. [19]

    F., Pinilla, P., et al

    Cazzoletti, P., van Dishoeck, E. F., Pinilla, P., et al. 2018, A&A, 619, A161

    work page 2018

  20. [20]

    Clark, B. G. 1980, A&A, 89, 377

    work page 1980

  21. [21]

    Cornwell, T. J. 2008, IEEE Journal of Selected Topics in Signal Processing, 2, 793

    work page 2008

  22. [22]

    2025, arXiv e-prints, arXiv:2502.00100

    Czekala, I., Jennings, J., Zawadzki, B., et al. 2025, arXiv e-prints, arXiv:2502.00100

    work page arXiv 2025

  23. [23]

    Davies, A. G. 2007, V olcanism on Io, doi:10.1017/CBO9781107279902

    work page doi:10.1017/cbo9781107279902 2007

  24. [24]

    G., Perry, J., Williams, D

    Davies, A. G., Perry, J., Williams, D. A., & Nelson, D. M. 2024a, in AGU Fall Meeting Abstracts, V ol. 2024, P33D–2894

    work page 2024

  25. [25]

    G., Perry, J

    Davies, A. G., Perry, J. E., Williams, D. A., Veeder, G. J., & Nelson, D. M. 2024b, Proc. Astron. Soc. Japan, 5, 121 De Furio, M., Ygouf, M., Greenbaum, A., et al. 2025, arXiv e-prints, arXiv:2510.05308 de Kleer, K., de Pater, I., Molter, E. M., et al. 2019, AJ, 158, 29

    work page arXiv 2025

  26. [26]

    2020, The DeepMind JAX Ecosystem

    DeepMind, Babuschkin, I., Baumli, K., et al. 2020, The DeepMind JAX Ecosystem

    work page 2020

  27. [27]

    2024, arXiv e-prints, arXiv:2406.08704

    Desdoigts, L., Pope, B., Gully-Santiago, M., & Tuthill, P. 2024, arXiv e-prints, arXiv:2406.08704

    work page arXiv 2024

  28. [28]

    Desdoigts, L., Pope, B. J. S., Dennis, J., & Tuthill, P. G. 2023, Journal of Astronomical Telescopes, Instruments, and Systems, 9, 028007

    work page 2023

  29. [29]

    2025, Publications of the Astro- nomical Society of Australia, accepted

    Desdoigts, L., Pope, B., Charles, M., et al. 2025, Publications of the Astro- nomical Society of Australia, accepted

    work page 2025

  30. [30]

    J., Adam, A., et al

    Dia, N., Yantovski-Barth, M. J., Adam, A., et al. 2025, arXiv e-prints, arXiv:2501.02473

    work page arXiv 2025

  31. [31]

    J., Hutchings, J

    Doyon, R., Willott, C. J., Hutchings, J. B., et al. 2023, PASP, 135, 098001 Enßlin, T. A. 2019, Annalen der Physik, 531, 1800127 Event Horizon Telescope Collaboration, Akiyama, K., Alberdi, A., et al. 2019a, ApJ, 875, L1 —. 2019b, ApJ, 875, L4

    work page 2023

  32. [32]

    T., Bouman, K

    Feng, B. T., Bouman, K. L., & Freeman, W. T. 2024, ApJ, 975, 201

    work page 2024

  33. [33]

    Y., Ferrer-Chávez, R., Levis, A., et al

    Feng, B. Y., Ferrer-Chávez, R., Levis, A., et al. 2025, arXiv e-prints, arXiv:2501.01912

    work page arXiv 2025

  34. [34]

    Ford, K. E. S., McKernan, B., Sivaramakrishnan, A., et al. 2014, ApJ, 783, 73 Gámez Rosas, V., Isbell, J. W., Jaffe, W., et al. 2022, Nature, 602, 403

    work page 2014

  35. [35]

    Z., Pueyo, L., Sivaramakrishnan, A., & Lacour, S

    Greenbaum, A. Z., Pueyo, L., Sivaramakrishnan, A., & Lacour, S. 2015, ApJ, 798, 68

    work page 2015

  36. [36]

    Hansen, P. C. 1992, SIAM Review, 34, 561

    work page 1992

  37. [37]

    2020, Optax: composable gradient transformation and optimisation, in JAX!

    Hessel, M., Budden, D., Viola, F., et al. 2020, Optax: composable gradient transformation and optimisation, in JAX!

    work page 2020

  38. [38]

    D., et al

    Hinkley, S., Lacour, S., Marleau, G. D., et al. 2023, A&A, 671, L5

    work page 2023

  39. [39]

    M., Defrère, D., Skemer, A., et al

    Hinz, P. M., Defrère, D., Skemer, A., et al. 2016, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, V ol. 9907, Optical and Infrared Interferometry and Imaging V, ed. F. Malbet, M. J. Creech- Eakman, & P. G. Tuthill, 990704

    work page 2016

  40. [40]

    M., & Choi, A

    Hirata, C. M., & Choi, A. 2020, PASP, 132, 014501

    work page 2020

  41. [41]

    H., Weigelt, G., & Schertl, D

    Hofmann, K. H., Weigelt, G., & Schertl, D. 2014, A&A, 565, A48 Högbom, J. A. 1974, A&AS, 15, 417 Publications of the Astronomical Society of Australia15

    work page 2014

  42. [42]

    Hunter, J. D. 2007, Computing In Science & Engineering, 9, 90 IceCube Collaboration, Abbasi, R., Ackermann, M., et al. 2022, Science, 378, 538

    work page 2007

  43. [43]

    2020, ApJ, 891, L33

    Inoue, Y., Khangulyan, D., & Doi, A. 2020, ApJ, 891, L33

    work page 2020

  44. [44]

    Ireland, M. J. 2013, MNRAS, 433, 1718

    work page 2013

  45. [45]

    W., Ertel, S., Pott, J

    Isbell, J. W., Ertel, S., Pott, J. U., et al. 2025, Nature Astronomy, 9, 417

    work page 2025

  46. [46]

    Jaffe, W., Meisenheimer, K., Röttgering, H. J. A., et al. 2004, Nature, 429, 47

    work page 2004

  47. [47]

    Jennison, R. C. 1958, MNRAS, 118, 276

    work page 1958

  48. [48]

    1991, Laplace’s method in Bayesian analysis, doi:10.1090/conm/115/07

    Kass, R., Tierney, L., & Kadane, J. 1991, Laplace’s method in Bayesian analysis, doi:10.1090/conm/115/07

    work page doi:10.1090/conm/115/07 1991

  49. [49]

    2021, Differentiable Programming workshop at Neural Information Processing Systems 2021

    Kidger, P., & Garcia, C. 2021, Differentiable Programming workshop at Neural Information Processing Systems 2021

    work page 2021

  50. [50]

    King, O. R. T., & Fletcher, L. N. 2023, Journal of Open Source Software, 8, 5728

    work page 2023

  51. [51]

    2023, OTE Science Performance Memo 2 - A Y ear of Wavefront Sensing with JWST in Flight: Cycle 1 Telescope Monitoring & Maintenance Summary, Tech

    Lajoie, C.-P., Lallo, M., Meléndez, M., et al. 2023, OTE Science Performance Memo 2 - A Y ear of Wavefront Sensing with JWST in Flight: Cycle 1 Telescope Monitoring & Maintenance Summary, Tech. Rep. Technical Report JWST-STScI-008497, STScI, doi:10.48550/arXiv.2307.11179

    work page doi:10.48550/arxiv.2307.11179 2023

  52. [52]

    M., Hankins, M

    Lau, R. M., Hankins, M. J., Sanchez-Bermudez, J., et al. 2024, ApJ, 963, 127

    work page 2024

  53. [53]

    B., Axon, D

    Macchetto, F., Capetti, A., Sparks, W. B., Axon, D. J., & Boksenberg, A. 1994, ApJ, 435, L15

    work page 1994

  54. [54]

    G., et al

    Marchis, F., de Pater, I., Davies, A. G., et al. 2002, Icarus, 160, 124

    work page 2002

  55. [55]

    2010, ApJ, 724, 464

    Martinache, F. 2010, ApJ, 724, 464

    work page 2010

  56. [56]

    2020, A&A, 636, A72

    Martinache, F., Ceau, A., Laugier, R., et al. 2020, A&A, 636, A72

    work page 2020

  57. [57]

    C., Mugnier, L

    Meimon, S. C., Mugnier, L. M., & Le Besnerais, G. 2005, Optics Letters, 30, 1809

    work page 2005

  58. [58]

    2012, MNRAS, 420, 526

    Mor, R., & Netzer, H. 2012, MNRAS, 420, 526

    work page 2012

  59. [59]

    2020, Icarus, 341, 113607

    Mura, A., Adriani, A., Tosi, F., et al. 2020, Icarus, 341, 113607

    work page 2020

  60. [60]

    M., Assef, R

    Padovani, P., Alexander, D. M., Assef, R. J., et al. 2017, A&A Rev., 25, 2

    work page 2017

  61. [61]

    2021, MNRAS, 503, 3724

    Pairet, B., Cantalloube, F., & Jacques, L. 2021, MNRAS, 503, 3724

    work page 2021

  62. [62]

    PyTorch: An Imperative Style, High-Performance Deep Learning Library

    Paszke, A., Gross, S., Massa, F., et al. 2019, PyTorch: An Imperative Style, High-Performance Deep Learning Library, arXiv:1912.01703

    work page internal anchor Pith review Pith/arXiv arXiv 2019

  63. [63]

    Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro

    Phan, D., Pradhan, N., & Jankowiak, M. 2019, arXiv preprint arXiv:1912.11554

    work page internal anchor Pith review Pith/arXiv arXiv 2019

  64. [64]

    2013, ApJ, 767, 110

    Pope, B., Martinache, F., & Tuthill, P. 2013, ApJ, 767, 110

    work page 2013

  65. [65]

    Pope, B. J. S. 2016, MNRAS, 463, 3573–3581

    work page 2016

  66. [66]

    Pope, B. J. S., Pueyo, L., Xin, Y., & Tuthill, P. G. 2021, ApJ, 907, 40

    work page 2021

  67. [67]

    P., Amara, A., Meyer, M

    Quanz, S. P., Amara, A., Meyer, M. R., et al. 2015, ApJ, 807, 64

    work page 2015

  68. [68]

    2025, ApJ, 983, L25

    Ray, S., Sallum, S., Hinkley, S., et al. 2025, ApJ, 983, L25

    work page 2025

  69. [69]

    B., Fogarty, K., Debes, J

    Ren, B. B., Fogarty, K., Debes, J. H., et al. 2024, A&A, 683, L5

    work page 2024

  70. [70]

    2011, A&A, 533, A64

    Renard, S., Thiébaut, E., & Malbet, F. 2011, A&A, 533, A64

    work page 2011

  71. [71]

    2017, ApJS, 233, 9

    Sallum, S., & Eisner, J. 2017, ApJS, 233, 9

    work page 2017

  72. [72]

    2022, in Optical and Infrared Interferometry and Imaging VIII, ed

    Sallum, S., Ray, S., & Hinkley, S. 2022, in Optical and Infrared Interferometry and Imaging VIII, ed. A. Mérand, S. Sallum, & J. Sanchez-Bermudez, V ol. 12183, International Society for Optics and Photonics (SPIE), 121832M

    work page 2022

  73. [73]

    2024, ApJ, 963, L2

    Sallum, S., Ray, S., Kammerer, J., et al. 2024, ApJ, 963, L2

    work page 2024

  74. [74]

    2022, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference

    Sanchez-Bermudez, J., Alberdi, A., Schödel, R., & Sivaramakrishnan, A. 2022, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference

    work page 2022

  75. [75]

    2025, MNRAS, 543, 608

    Sanchez-Bermudez, J., de Pater, I., Conrad, A., et al. 2025, MNRAS, 543, 608

    work page 2025

  76. [76]

    2023, PASP, 135, 018001

    Schlawin, E., Beatty, T., Brooks, B., et al. 2023, PASP, 135, 018001

    work page 2023

  77. [77]

    2014, Journal of the Optical Society of America A, 31, 2334

    Schutz, A., Ferrari, A., Mary, D., et al. 2014, Journal of the Optical Society of America A, 31, 2334

    work page 2014

  78. [78]

    Schwarz, U. J. 1978, A&A, 65, 345

    work page 1978

  79. [79]

    Shannon, C. E. 1948, The Bell System Technical Journal, 27, 379

    work page 1948

  80. [80]

    G., et al

    Sivaramakrishnan, A., Lafrenière, D., Tuthill, P. G., et al. 2010, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, V ol. 7731, Space Telescopes and Instrumentation 2010: Optical, Infrared, and Millimeter Wave, ed. J. M. Oschmann, Jr., M. C. Clampin, & H. A. MacEwen, 77313W

    work page 2010

Showing first 80 references.