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, Thallis Pessi, Emille E. O. Ishida, Kristen C. Dage, Lilianne Nakazono, Phelipe Darc, Rupesh Durgesh (for the COIN collaboration)

TL;DR
Sagui is a modular framework that extends spectro-spatial analysis to multi-band galaxy imaging, enabling detailed pixel-level characterization of complex structures and faint features in diverse galaxy morphologies.
Contribution
It introduces a novel two-stage pixel-level analysis method combining starlet decomposition and spectral similarity, applicable to multi-band imaging data, including faint low-surface-brightness components.
Findings
Successfully characterizes complex galaxy structures like clumps, bars, and interactions.
Effectively recovers faint, diffuse low-surface-brightness features.
Demonstrates applicability to diverse galaxy morphologies in JWST data.
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…
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