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

SAGUI: SED-based Segmentation of Multi-band Galaxy Images — Application to JADES in GOODS-South
Rafael S. de Souza (for the COIN collaboration), Andressa Wille (for the COIN collaboration), Shravya Shenoy (for the COIN collaboration), Aarya A. Patil (for the COIN collaboration), Alberto Krone-Martins (for the COIN collaboration), Ana L. Chies-Santos (for the COIN collaboration), Celine Boehm (for the COIN collaboration), Reinaldo R. Rosa (for the COIN collaboration), Thallis Pessi (for the COIN collaboration), Emille E. O. Ishida (for the COIN collaboration), Kristen C. Dage (for the COIN collaboration), Lilianne Nakazono (for the COIN collaboration), Phelipe Darc (for the COIN collaboration), Rupesh Durgesh (for the COIN collaboration)
arXiv:2604.18812v1 Announce Type: new
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/.arXiv:2604.18812v1 Announce Type: new
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/.