Résultats du processus de synthèse de l'article arXiv : 2404.02962v1

Résumé : Galaxies are complex objects, yet the number of independent parameters to describe them remains unknown. We present here a non-parametric method to estimate the intrinsic dimensionality of large datasets. We apply it to wide-band photometric data drawn from the COSMOS2020 catalogue and a comparable mock catalogue from the Horizon-AGN simulation. Our galaxy catalogues are limited in signal-to-noise ratio in all optical and NIR bands. Our results reveal that most of the variance in the wide-band photometry of this galaxy sample can be described with at most $4.3\pm0.5$ independent parameters for star-forming galaxies and $2.9\pm0.2$ for passive ones, both in the observed and simulated catalogues. We identify one of these parameters to be noise-driven, and recover that stellar mass and redshift are two key independent parameters driving the magnitudes. Our findings support the idea that wide-band photometry does not provide more than one additional independent parameter for star-forming galaxies. Although our sample is not mass-limited and may miss some passive galaxies due to our cut in SNR, our work suggests that dimensionality reduction techniques may be effectively used to explore and analyse wide-band photometric data, provided the used latent space is at least four-dimensional.