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arxiv: 2604.18812 · v2 · pith:SM62JGENnew · submitted 2026-04-20 · 🌌 astro-ph.IM · astro-ph.GA

SAGUI: SED-based Segmentation of Multi-band Galaxy Images -- Application to JADES in GOODS-South

Rafael S. de Souza , Andressa Wille , Shravya Shenoy , Aarya A. Patil , Alberto Krone-Martins , Ana L. Chies-Santos , Celine Boehm , Reinaldo R. Rosa
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Thallis Pessi Emille E. O. Ishida Kristen C. Dage Lilianne Nakazono Phelipe Darc Rupesh Durgesh (for the COIN collaboration)
This is my paper

Pith reviewed 2026-05-10 03:35 UTC · model grok-4.3

classification 🌌 astro-ph.IM astro-ph.GA
keywords galaxy segmentationmulti-band imaginglow surface brightnessSED analysisstarlet decompositioncopula transformJADES surveygalaxy morphology
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The pith

SAGUI segments multi-band galaxy images by combining starlet decomposition with spectral-similarity grouping and copula recovery of faint features.

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

The paper introduces SAGUI as a framework for analyzing multi-band images of galaxies that treats spatial structures and spectral information together at the pixel level. It begins with a starlet-based decomposition to identify features across multiple scales while suppressing noise. Pixels are then partitioned into groups based on spectral similarity to ensure consistency in their properties across bands. A copula transform is applied to detect and restore faint, diffuse low-surface-brightness components that standard methods often miss. The method is applied to eleven galaxies of varied morphologies from the JADES survey in GOODS-South, showing it can delineate clumps, bars, and extended structures. A sympathetic reader would value this because it offers a unified way to extract detailed spatial and color information from deep imaging data for studying galaxy properties.

Core claim

SAGUI extends the spectro-spatial paradigm to imaging data through a two-stage strategy: starlet-based multi-scale decomposition first identifies and masks spatial structures while suppressing noise, after which spectral-similarity analysis partitions the image into coherent pixel groups that preserve spectral consistency. The framework adds a dedicated copula-transform treatment to identify and recover faint diffuse low-surface-brightness components, and the approach is demonstrated on morphologically diverse galaxies from the JADES GOODS-South field.

What carries the argument

The central mechanism is the two-stage segmentation that pairs starlet-based multi-scale decomposition for spatial structure detection and noise suppression with spectral-similarity partitioning for pixel grouping, augmented by a copula transform to recover low-surface-brightness features.

If this is right

  • Enables characterization of complex galaxy structures including clumps, bars, interacting systems, and low-surface-brightness features.
  • Delivers a coherent pixel-level treatment of spatial and spectral information across multiple bands.
  • Supports synergies with integral-field spectroscopy for spatially resolved galaxy studies.
  • Facilitates analysis of faint components in deep surveys such as JADES.

Where Pith is reading between the lines

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

  • The segmentation output could be combined with existing photometry pipelines to improve measurements of galaxy stellar populations in large surveys.
  • The approach might help isolate diffuse features that influence models of galaxy assembly and evolution.
  • Extension to additional wavelength regimes could reveal how spectral consistency holds or breaks across different instruments.

Load-bearing premise

The method assumes that starlet decomposition plus spectral-similarity partitioning will form pixel groups that keep spectral consistency across bands, and that the copula transform will recover true faint low-surface-brightness structures without creating artifacts or false features.

What would settle it

Apply SAGUI to simulated multi-band galaxy images that contain known injected low-surface-brightness components and check whether the output segmentation recovers those exact components without adding spurious structures or missing real ones; systematic mismatches would falsify the claim.

Figures

Figures reproduced from arXiv: 2604.18812 by Aarya A. Patil, Alberto Krone-Martins, Ana L. Chies-Santos, Andressa Wille, Celine Boehm, Emille E. O. Ishida, Kristen C. Dage, Lilianne Nakazono, Phelipe Darc, Rafael S. de Souza, Reinaldo R. Rosa, Rupesh Durgesh (for the COIN collaboration), Shravya Shenoy, Thallis Pessi.

Figure 1
Figure 1. Figure 1: JWST/NIRCam color composites of the galaxies analyzed in this study. Their identifiers and redshifts are indicated in each panel. The images were constructed using the filters F277W (red), F182M (green) and F115W (blue). brightness profiles fade gradually into the background. A variety of methods have been proposed for this task, ranging from noise-based detection techniques such as noisechisel (Akhlaghi &… view at source ↗
Figure 2
Figure 2. Figure 2: sagui workflow illustration. Starting from a multi-band imaging cube, a white-light image is collapsed and used to define a starlet-based foreground support. Pixel SEDs within this support are then compared in photometric space, and hierarchical clustering is applied to the corresponding dissimilarity matrix. The final cluster assignments are projected back onto the image plane, yielding a spatial segmenta… view at source ↗
Figure 3
Figure 3. Figure 3: Starlet decomposition for representative galaxies (Sagui-1–3, Sagui-4, and Sagui-10). In each case, the original image is exactly recovered by summing the detail coefficients across all five scales and the coarse component. The finest scale (𝑗 = 1) is dominated by pixel-scale fluctuations, while intermediate scales trace coherent galactic structure. Larger scales isolate progressively smoother and more ext… view at source ↗
Figure 4
Figure 4. Figure 4: Spatial distribution of the 20 segments detected by sagui. Different colours denote distinct segments. Voronoi piXedfit sagui [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Comparison of segmentation behaviour for Sagui-10 using 10 components. Left: pure Voronoi binning without a foreground mask. Middle: a piXedfit-like adaptive pixel-binning baseline, restricted to a piXedfit-like foreground mask. Right: sagui, which combines a starlet-derived foreground mask with clustering in SED space. adaptive binning schemes are controlled by a S/N criterion rather than by fixing the ex… view at source ↗
Figure 6
Figure 6. Figure 6: Benchmark comparison for Sagui-8. Rows show sagui, pure Voronoi binning, and the pixedfit-like baseline. Columns show nominal target group numbers 𝑁 = 10, 15, 20, 30. tion maps, and the right panels the corresponding bundles of normal￾ized JWST SEDs for the pixels in each region. Because the mask is fixed, the differences reflect only the clustering step. The sagui seg￾mentation follows the galaxy morpholo… view at source ↗
Figure 7
Figure 7. Figure 7: Illustrative comparison for Sagui-10 using a shared starlet support, with sagui segmentation in the top panel and a Voronoi tessellation in the bottom panel, both using six regions. The left panels show the segmentation maps, while the right panels display bundles of normalized JWST SEDs for the pixels in each segment. The shaded coloured bands indicate the approximate transmission windows of the JWST filt… view at source ↗
Figure 8
Figure 8. Figure 8: Validation of the segmentation quality as a function of the number of groups for Sagui-7, Sagui-8, Sagui-9, and Sagui-10. The top row shows the spectral dissimilarity, while the bottom row shows the mean cluster S/N. Across all four galaxies, sagui yields lower spectral dissimilarity than Voronoi binning, indicating more spectrally coherent regions, while maintaining comparable or higher mean cluster S/N o… view at source ↗
Figure 9
Figure 9. Figure 9: Spatially resolved stellar population maps derived from SED fit￾ting for the Sagui-4 system. Each panel displays a two-dimensional map of a physical property inferred from the SED-fitting, including stellar metallic￾ity log10 (𝑍/𝑍⊙ ), star-formation-rate surface density ΣSFR [𝑀⊙ yr−1 kpc−2 ], mass-weighted stellar age ⟨𝑡⟩M, and dust attenuation 𝐴𝑉 . star formation behaviour of this structure may indicate t… view at source ↗
Figure 11
Figure 11. Figure 11: Spatially resolved stellar population maps derived from SED fit￾ting for the Sagui-7 system. Each panel displays a two-dimensional map of a physical property inferred from the SED-fitting, including stellar metallic￾ity log10 (𝑍/𝑍⊙ ), star-formation-rate surface density ΣSFR [𝑀⊙ yr−1 kpc−2 ], mass-weighted stellar age ⟨𝑡⟩M, and dust attenuation 𝐴𝑉 . log10(Z/Z⊙) ΣSFR [M⊙ yr−1 kpc−2 ] ⟨t⟩M [Gyr] AV [mag] [… view at source ↗
Figure 12
Figure 12. Figure 12: Spatially resolved stellar population maps derived from SED fit￾ting for the Sagui-8 system. Each panel displays a two-dimensional map of a physical property inferred from the SED-fitting, including stellar metallic￾ity log10 (𝑍/𝑍⊙ ), star-formation-rate surface density ΣSFR [𝑀⊙ yr−1 kpc−2 ], mass-weighted stellar age ⟨𝑡⟩M, and dust attenuation 𝐴𝑉 . log10(Z/Z⊙) ΣSFR [M⊙ yr−1 kpc−2 ] ⟨t⟩M [Gyr] AV [mag] [… view at source ↗
Figure 14
Figure 14. Figure 14: Spatially resolved stellar population maps derived from SED fit￾ting for the Sagui-10 system. Each panel displays a two-dimensional map of a physical property inferred from the SED-fitting, including stellar metallic￾ity log10 (𝑍/𝑍⊙ ), star-formation-rate surface density ΣSFR [𝑀⊙ yr−1 kpc−2 ], mass-weighted stellar age ⟨𝑡⟩M, and dust attenuation 𝐴𝑉 [PITH_FULL_IMAGE:figures/full_fig_p013_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Interacting galaxies at 𝑧 ≃ 1.1. These galaxies have structures that are examples of low-surface-brightness features: tidal tails and a bridge between them. The image was constructed using the JWST/NIRCam filters F277W (red), F182M (green) and F115W (blue). It has ∼250 pixels per side, corresponding to ∼0.167 arcmin. 4.3.2 Copula transform To disentangle galaxy light from noisy backgrounds in multi-band i… view at source ↗
Figure 16
Figure 16. Figure 16: Starlet decomposition for representative galaxies (Sagui-11). In each case, the original image is exactly recovered by summing the detail coefficients across all five scales and the coarse component. The finest scale (𝑗 = 1) is dominated by pixel-scale fluctuations, while intermediate scales trace coherent galactic structure. Larger scales isolate progressively smoother and more extended components [PITH… view at source ↗
Figure 17
Figure 17. Figure 17: Spatial distribution of the 20 segments detected by sagui. Dif￾ferent colours denote distinct segments. Left panel: segmentation performed directly on the data, as in [PITH_FULL_IMAGE:figures/full_fig_p015_17.png] view at source ↗
Figure 18
Figure 18. Figure 18 [PITH_FULL_IMAGE:figures/full_fig_p015_18.png] view at source ↗
read the original abstract

We present sagui, a modular framework for the analysis of multi-band imaging data in spatially resolved galaxies, with synergies to integral-field spectroscopy (IFS). Building on the spectro-spatial paradigm introduced by capivara for IFS data, sagui extends this approach to imaging datasets, enabling a coherent, pixel-level treatment of spatial and spectral information across multiple bands. The method follows a two-stage strategy: a starlet-based decomposition is first used to identify and mask spatial structures across multiple scales while suppressing noise, and a spectral-similarity analysis then partitions the image into coherent pixel groups that preserve spectral consistency. In addition to compact and high-contrast structures, the framework incorporates a dedicated statistical treatment, based on a copula transform, to identify and recover faint, diffuse low-surface-brightness components. We demonstrate the method across a diverse range of galaxy morphologies, highlighting its ability to characterize complex spatial structures, including clumps, bars, interacting systems, and low-surface-brightness features. As a case study, we apply it to eleven morphologically diverse galaxies from the James Webb Space Telescope Advanced Deep Extragalactic Survey in the GOODS--South field. sagui is released under an MIT license and is available at https://rafaelsdesouza.github.io/sagui/.

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 / 1 minor

Summary. The paper introduces SAGUI, a modular framework extending the CAPIVARA spectro-spatial approach from IFS to multi-band imaging data. It employs a two-stage pipeline—starlet-based multi-scale decomposition to identify spatial structures and suppress noise, followed by spectral-similarity partitioning to group pixels while preserving spectral consistency—plus a copula transform to recover faint diffuse low-surface-brightness components. The method is demonstrated qualitatively on 11 morphologically diverse galaxies from the JADES survey in GOODS-South, with open-source code released under MIT license.

Significance. If the LSB recovery and segmentation claims hold under quantitative scrutiny, SAGUI would provide a useful open tool for coherent pixel-level analysis of complex galaxy structures (clumps, bars, interactions, diffuse features) in JWST multi-band imaging while maintaining spectral fidelity, with potential synergies to IFS studies. The public code release is a clear strength supporting reproducibility.

major comments (2)
  1. [Abstract and case-study application] Abstract and demonstration on JADES galaxies: the central claim that the copula transform accurately identifies and recovers faint LSB components without introducing artifacts or false structures rests on qualitative examples only; no completeness, purity, flux-recovery fractions, or controlled tests on simulated images with injected LSB signals at known surface-brightness levels and noise realizations are reported, leaving the performance unverified.
  2. [Methods] Methods description of the two-stage strategy: no quantitative error analysis, baseline comparisons (e.g., against standard segmentation tools), or details on parameter choices for starlet scales and spectral-similarity thresholds are supplied, which is load-bearing for assessing whether the partitioning truly preserves spectral consistency across bands.
minor comments (1)
  1. [Abstract] The abstract states the code is available at a GitHub link but does not include a direct citation or DOI for the CAPIVARA framework it builds upon.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the careful and constructive review of our manuscript. The comments identify key areas where additional rigor would strengthen the presentation of SAGUI. We address each major comment point by point below, indicating the revisions we will implement.

read point-by-point responses

  1. Referee: [Abstract and case-study application] Abstract and demonstration on JADES galaxies: the central claim that the copula transform accurately identifies and recovers faint LSB components without introducing artifacts or false structures rests on qualitative examples only; no completeness, purity, flux-recovery fractions, or controlled tests on simulated images with injected LSB signals at known surface-brightness levels and noise realizations are reported, leaving the performance unverified.

    Authors: We agree that the current demonstration is based on qualitative assessment of real JADES data across 11 morphologically diverse galaxies. While this choice emphasizes applicability to complex observational datasets, we acknowledge that quantitative validation would better support the LSB recovery claims. In the revised manuscript we will add a dedicated section presenting controlled tests on simulated images with injected LSB signals, including completeness, purity, and flux-recovery metrics across multiple noise realizations. revision: yes

  2. Referee: [Methods] Methods description of the two-stage strategy: no quantitative error analysis, baseline comparisons (e.g., against standard segmentation tools), or details on parameter choices for starlet scales and spectral-similarity thresholds are supplied, which is load-bearing for assessing whether the partitioning truly preserves spectral consistency across bands.

    Authors: We accept that the methods section would benefit from greater quantitative support. The original text describes the starlet decomposition and spectral-similarity partitioning but does not include formal error analysis or direct comparisons. We will revise the methods to incorporate quantitative error analysis, baseline comparisons against standard tools such as SExtractor, and explicit details on the selection of starlet scales and spectral-similarity thresholds together with sensitivity tests confirming preservation of spectral consistency. revision: yes

Circularity Check

0 steps flagged

SAGUI presents independent methodological extensions without derivation circularity

full rationale

The manuscript introduces SAGUI as a new two-stage pipeline (starlet decomposition followed by spectral-similarity partitioning, augmented by a copula transform for LSB recovery) that extends the prior CAPIVARA framework to imaging data. No equations or steps in the provided description reduce the core outputs (pixel groups, recovered LSB components) to quantities fitted from the same JADES dataset by construction. The application to eleven galaxies is a demonstration rather than a self-referential prediction. The self-citation to CAPIVARA is explicit but does not serve as the sole justification for the new imaging-specific components or claims; those rest on the described algorithmic choices and qualitative results. This is a standard case of building on prior work without tautological reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Based solely on the abstract; no explicit free parameters, axioms, or invented entities are described. The method likely involves implicit choices such as decomposition scales, similarity thresholds, and copula parameters, but these are not detailed.

pith-pipeline@v0.9.0 · 5608 in / 1291 out tokens · 33838 ms · 2026-05-10T03:35:55.917394+00:00 · methodology

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