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arxiv: 2606.23478 · v1 · pith:V4PU5HZ2new · submitted 2026-06-22 · 🌌 astro-ph.IM · astro-ph.SR

ffortissimo: A Freeform Forward-Modeling Pipeline for High-Contrast Images of Circumstellar Disks Based on Automatic Differentiation

Jay K. Kueny , Joseph D. Long , Jared R. Males , Alycia J. Weinberger , Laird M. Close , Joshua Liberman , Sebastiaan Haffert , Eden McEwen
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Maggie Y. Kautz Olivier Guyon Logan Pearce Parker T. Johnson Katie Twitchell Jialin Li Alex Hedglen Avalon Gower Warren Foster Jhen Lumbres Lauren Schatz
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Pith reviewed 2026-06-26 06:54 UTC · model grok-4.3

classification 🌌 astro-ph.IM astro-ph.SR
keywords circumstellar diskshigh-contrast imagingforward modelingautomatic differentiationHR 4796Aadaptive opticsdust scatteringKLIP reduction
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The pith

A pixel-based freeform pipeline using automatic differentiation fits complex dust distributions in high-contrast disk images without parametric assumptions.

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

The paper introduces ffortissimo as a forward-modeling approach that represents dust density on a pixel grid rather than through fixed parametric forms. Built in JAX, the pipeline uses automatic differentiation to optimize these models efficiently against KLIP-reduced images from extreme adaptive optics. Demonstrated on visible-light MagAO-X observations of the HR 4796A disk, the freeform models recover detailed dust structure and scattering behavior even when PSF subtraction leaves artifacts. This addresses a long-standing limitation in which assumed shapes produce large residuals and restrict what can be learned about real dust distributions.

Core claim

ffortissimo is a pixel-based freeform forward modeling pipeline for extended objects in KLIP-reduced high-contrast images. Implemented in JAX to exploit automatic differentiation and GPU acceleration, it performs data-driven optimization of disk models directly on pixels. When applied to MagAO-X visible-light images of the HR 4796A circumstellar disk, the models fit complex dust distributions and recover scattering properties through PSF subtraction artifacts while showing potential to retrieve spatial features beyond the diffraction limit.

What carries the argument

ffortissimo, a pixel-based freeform forward modeling pipeline that optimizes disk models on image pixels via automatic differentiation in JAX to avoid fixed parametric dust-density forms.

If this is right

  • Complex, non-parametric dust distributions can be recovered directly from the data.
  • Dust scattering properties remain measurable even when PSF subtraction distorts the image.
  • Spatial features smaller than the telescope diffraction limit become potentially accessible.
  • Precision photometry requires additional controls on background and speckle modeling.

Where Pith is reading between the lines

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

  • The same pixel-grid approach could be tested on other extended sources observed with extreme adaptive optics.
  • If noise fitting is controlled, the method may support statistical comparisons across multiple disks without shared parametric assumptions.
  • Controlled injection-recovery tests on sub-diffraction scales would directly test the claimed resolution gain.

Load-bearing premise

The optimization process can be kept from fitting noise artifacts such as background, wind-driven halo, and speckles instead of actual disk features.

What would settle it

Inject known disk features and realistic noise artifacts into simulated KLIP-reduced images, then check whether the freeform model recovers the injected features accurately without also fitting the noise patterns.

Figures

Figures reproduced from arXiv: 2606.23478 by Alex Hedglen, Alycia J. Weinberger, Avalon Gower, Eden McEwen, Jared R. Males, Jay K. Kueny, Jhen Lumbres, Jialin Li, Joseph D. Long, Joshua Liberman, Katie Twitchell, Laird M. Close, Lauren Schatz, Logan Pearce, Maggie Y. Kautz, Olivier Guyon, Parker T. Johnson, Sebastiaan Haffert, Warren Foster.

Figure 1
Figure 1. Figure 1: Flowchart illustrating the various processes and data used during the freeform model optimization. This workflow is explained in detail in Section 3. We inset small thumbnail images illustrating data products and the state of the freeform model during the main optimization loop next to the appropriate nodes in the diagram. We use standard ANSI flowchart shapes to denote processing steps, manual operations,… view at source ↗
Figure 2
Figure 2. Figure 2: Suite of images illustrating how we create a reference template in Fourier space to regularize high-spatial frequencies during the freeform model optimization loop. Left: a toy SCL disk model. Middle left: the magnitude of the Fourier transform squared of the mean-subtracted toy disk model. Middle right: the fitted elliptical Gaussian window function using the magnitude of the spatial frequency spectrum ge… view at source ↗
Figure 3
Figure 3. Figure 3: Showcase of the effects of spatial frequency regularization. Top: Example optimized freeform disk model with regularization. Bottom: Optimized model using the same dataset and modeling parameters as the model in the top panel but without regularization. The colorbar represents pixel counts in arbitrary units. sparsity all with a single regularization parameter, dramatically simplifying the work needed to l… view at source ↗
Figure 4
Figure 4. Figure 4: Results of the test we performed to probe model biases with our method for regularizing high-spatial frequency content in the freeform model. First column: the top image shows the reference disk model. We show the reference power spectrum template in the bottom panel. Second column: the top image shows the diskless KLIP-reduced image that we fit a freeform model to using the same setup parameters we would … view at source ↗
Figure 5
Figure 5. Figure 5: Showcase of two experiments to validate the use of simple reference disk models for regularizing the freeform model. The top 6 panels show results when using a reference disk model with a bright spot in a distinctly different location than the expected location when optimizing the i ′ -band dataset from UT 2023-03-10. The bottom 6 panels show that using a disk model that is nearly isotropic in brightness s… view at source ↗
Figure 6
Figure 6. Figure 6: Comparison of residuals images. Top: Residuals between the KLIP-ADI disk image at r ′ -band made with a traditional SCL disk model (as described in Ren et al. 2019). Bottom: Residuals using the same KLIP-ADI image but using an optimized freeform disk model. Though the oversubtraction artifacts and residual disk halo are not present in the freeform disk model residuals image, the freeform model learns some … view at source ↗
Figure 7
Figure 7. Figure 7: Comparison of freeform modeling results between KLIP-ADI and KLIP-RDI datasets, illustrating inference of the disk through self-subtraction artifacts. Top row: results for the KLIP-RDI image. The left panel shows the KLIP-reduced image of the HR 4796A disk, the middle panel shows the optimized freeform model after 10k iterations, and the right panel shows the residuals between the KLIP-reduced data and the… view at source ↗
Figure 8
Figure 8. Figure 8: Inject-and-recover test results using a synthetic disk with a known SPF showing the degree to which ffortissimo can infer an SPF given different noise profiles. We used two different “back-rotated” KLIP-ADI datasets (see Section 3.6) from separate nights. The top row shows the results using the i ′ -band KLIP-ADI data and the bottom row shows results using the r ′ -band KLIP-ADI dataset. For each row, in l… view at source ↗
Figure 9
Figure 9. Figure 9: Results of inferring the azimuthal brightness distribution of the disk (i.e., the SPF) with our freeform models. Shaded regions denote 1 sigma uncertainties estimated from the spatial noise map mentioned in Section 3.4. Top: Comparison of the z ′ -band SPF measured using the freeform models for the cases of the KLIP-RDI image (red line) and the KLIP-ADI image (dotted blue line). We include the SPF extracte… view at source ↗
Figure 10
Figure 10. Figure 10: A demonstration of retrieval of object features by ffortissimo that are smaller than the diffraction limit of the telescope. We injected thin nested ringlets in a “counter-rotated” z ′ -band dataset (λ/D = 2.4 pixels) such that the HR 4796A disk is averaged out. Top left: injected nested ringlets with width and spacing of ∼ 1 pixel (0. ′′012). Top right: KLIP-ADI reduced image of the injected ringlets. Bo… view at source ↗
Figure 11
Figure 11. Figure 11: Left: the average converged model fit across all N = 100 runs with different random initial pixel values. Middle: the standard deviation of the final pixel values across all N runs. Right: a “signal-to-noise ratio” quantifying how well-constrained a particular pixel’s value is. When this value is low, the final value of that grid cell in the model is more dependent on its random initial value. The “noise”… view at source ↗
read the original abstract

Modeling circumstellar disks in the traditional sense carries the assumption that the dust density distribution can be accurately described with a fixed parametric form. Furthermore, commonly-used algorithms for subtracting the stellar point-spread function (PSF) distort the true morphology of the faint underlying disk structure, especially dusty features that are located at small angular separations. These phenomena often lead to significant residuals with parametric disk models and make it difficult to measure the full realizable range of the scattering function of the dust. We address these challenges with ffortissimo, a novel, pixel-based freeform forward modeling pipeline designed to characterize extended objects in KLIP-reduced images. We built this pipeline within the framework of JAX, which is a machine learning library in Python that enables efficient optimization through automatic differentiation ("autodiff") and GPU-accelerated array computations. Using visible light images of the disk around HR 4796A taken by the "extreme" Magellan Adaptive Optics instrument (MagAO-X), we show that our data-driven freeform models excel at fitting a complex dust distribution and can infer the dust scattering properties even through PSF subtraction artifacts. Additionally, we demonstrate the potential for retrieving spatial dust features beyond the diffraction limit of the telescope. We note that there are remaining challenges to address before precision photometry using these freeform models is advised. These include better background, wind-driven halo, and speckle characterization as preventing the freeform models from learning these noise artifacts is currently difficult.

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

1 major / 0 minor

Summary. The manuscript introduces ffortissimo, a pixel-based freeform forward-modeling pipeline implemented in JAX for analyzing high-contrast images of circumstellar disks after KLIP PSF subtraction. Using MagAO-X observations of the HR 4796A disk, it claims that these data-driven models can fit complex dust distributions, infer dust scattering properties despite artifacts, and potentially recover spatial features beyond the diffraction limit, while acknowledging challenges in preventing the models from fitting noise artifacts like background, wind-driven halo, and speckles.

Significance. If the freeform models can be shown to reliably separate true disk signals from PSF subtraction artifacts, this pipeline would represent a significant advance in non-parametric modeling of extended sources in high-contrast imaging, enabling better constraints on dust properties without assuming specific density distributions. The use of automatic differentiation in JAX for efficient optimization is a notable technical strength.

major comments (1)
  1. Abstract: The central claim that the freeform models 'excel at fitting a complex dust distribution and can infer the dust scattering properties even through PSF subtraction artifacts' is in tension with the statement that 'preventing the freeform models from learning these noise artifacts is currently difficult'; without explicit demonstration of regularization, noise-injection tests, or residual analysis that isolates disk signal, the support for the reported inferences on scattering properties and sub-diffraction features remains unclear and load-bearing for the paper's conclusions.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their careful and constructive review. The major comment identifies a genuine tension in the abstract that merits revision. We respond point-by-point below.

read point-by-point responses

  1. Referee: Abstract: The central claim that the freeform models 'excel at fitting a complex dust distribution and can infer the dust scattering properties even through PSF subtraction artifacts' is in tension with the statement that 'preventing the freeform models from learning these noise artifacts is currently difficult'; without explicit demonstration of regularization, noise-injection tests, or residual analysis that isolates disk signal, the support for the reported inferences on scattering properties and sub-diffraction features remains unclear and load-bearing for the paper's conclusions.

    Authors: We agree that the current abstract wording creates an unresolved tension and that the manuscript would benefit from clearer qualification of the claims. The demonstrations on HR 4796A data illustrate fitting performance and scattering-property recovery, yet the text already flags the difficulty of separating disk signal from artifacts. To resolve this, we will revise the abstract to replace 'excel at fitting' and 'can infer' with more measured phrasing (e.g., 'demonstrate the ability to fit' and 'show potential to recover'), add an explicit caveat sentence referencing the artifact challenge, and expand the results/discussion sections with additional residual maps and a brief description of the regularization approaches already explored. These changes will make the support for the inferences more transparent without overstating the current capabilities. revision: yes

Circularity Check

0 steps flagged

No circularity: data-driven forward-modeling pipeline is self-contained

full rationale

The paper describes a JAX-based software pipeline for pixel-level freeform forward modeling of circumstellar disks applied to KLIP-reduced MagAO-X observations of HR 4796A. All load-bearing steps consist of optimization against external observational data rather than any derivation that reduces to fitted parameters or self-citations by construction. No equations or claims equate a prediction to its own input, import uniqueness from prior author work, or rename known results as novel derivations. The work is methodological and externally benchmarked against real telescope data, yielding a normal non-finding of circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no specific details on free parameters, axioms, or invented entities. The model is described as data-driven freeform but the number and nature of fitted pixel values or optimization constraints are not specified.

pith-pipeline@v0.9.1-grok · 5884 in / 1356 out tokens · 33338 ms · 2026-06-26T06:54:20.637714+00:00 · methodology

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