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 ~28 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 (σNMAD typically between 0.1–0.3, and Pearson correlation between 0.7 and 0.9) confirm the precision of the method, with ~ 70% of predictions within 1σ dex of reference values. Although redshift estimation in COSMOS-Web remains challenging (median σNMAD = 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.
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