Year: 2025
Type: preprint
Abstract: The COSMOS-Web survey, with its unparalleled combination of multiband data, notably, near-infrared imaging from JWST's NIRCam (F115W, F150W, F277W, and F444W), provides a transformative dataset down to $\sim28$ mag (F444W) for studying galaxy evolution. In this work, we employ Self-Organizing Maps (SOMs), an unsupervised machine learning method, to estimate key physical parameters of galaxies -- redshift, stellar mass, star formation rate (SFR), specific SFR (sSFR), and age -- directly from photometric data out to $z=3.5$. SOMs efficiently project high-dimensional galaxy color information onto 2D maps, showing how physical properties vary among galaxies with similar spectral energy distributions. We first validate our approach using mock galaxy catalogs from the HORIZON-AGN simulation, where the SOM accurately recovers the true parameters, demonstrating its robustness. Applying the method to COSMOS-Web observations, we find that the SOM delivers robust estimates despite the increased complexity of real galaxy populations. Performance metrics ($σ_{\mathrm{NMAD}}$ typically between $0.1$--$0.3$, and Pearson correlation between $0.7$ and $0.9$) confirm the precision of the method, with $\sim$ $70\%$ of predictions within 1$σ$ dex of reference values. Although redshift estimation in COSMOS-Web remains challenging (median $σ_{\mathrm{NMAD}} = 0.04$), the overall success of the highlights its potential as a powerful and interpretable tool for galaxy parameter estimation. A key advance of this work is the use of JWST/NIRCam photometry, particularly the F444W band, which enhances SOM training and allows more accurate estimation of stellar mass, SFR, and age compared to previous studies using IRAC/Spitzer filters.
Source: arXiv (Cornell University)
Authors: Abedini, Fatemeh, Gozaliasl, Ghassem, Zonoozi, Akram Hasani, Kalantari, Atousa, Korpi-Lagg, Maarit, Ilbert, Olivier, Akins, Hollis, Allen, Natalie, Arango-Toro, Rafael, Casey, Caitlin, Drakos, Nicole, Faisst, Andreas, Flayhart, Carter, Franco, Maximilien, Haghi, Hosein, Haghjoo, Aryana, Harish, Santosh, Hatamnia, Hossein, Kartaltepe, Jeyhan, Khostovan, Ali, Koekemoer, Anton, Kokorev, Vasily, Larson, Rebecca, Leroy, Gavin, Liu, Daizhong, McCracken, Henry, McKinney, Jed, McMahon, Nicolas, Mercier, Wilfried, Mobasher, Bahram, Newman, Sophie, Paquereau, Louise, Rhodes, Jason, Robertson, Brant, Sanjaripour, Sogol, Shuntov, Marko, Taamoli, Sina, Toft, Sune, Valentino, Francesco, Vardoulaki, Eleni, Weaver, John
Institutions: Institute for Advanced Studies in Basic Sciences, University of Helsinki, Aalto University, University of Bonn, Centre National de la Recherche Scientifique, Aix-Marseille Université, Laboratoire d’Astrophysique de Marseille, The University of Texas at Austin, University of Copenhagen, University of Hawaii at Hilo, California Institute of Technology, Infrared Processing and Analysis Center, Rochester Institute of Technology, Université Paris Cité, Commissariat à l'Énergie Atomique et aux Énergies Alternatives, Université Paris-Saclay, University of California, Riverside, University of Kentucky, Space Telescope Science Institute, Durham University, Chinese Academy of Sciences, Purple Mountain Observatory, Institut d'Astrophysique de Paris, University of Portsmouth, Jet Propulsion Laboratory, University of California, Santa Cruz, DTU Space, Technical University of Denmark, Astrophysique, Instrumentation et Modélisation, Thüringer Landessternwarte Tautenburg, University of Massachusetts Amherst
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4416075166
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