  We present a novel method to estimate galaxy physical properties from
spectral energy distributions (SEDs), alternate to template fitting techniques
and based on self-organizing maps (SOM) to learn the high-dimensional manifold
of a photometric galaxy catalog. The method has been previously tested with
hydrodynamical simulations in Davidzon et al. (2019) while here is applied to
real data for the first time. It is crucial for its implementation to build the
SOM with a high quality, panchromatic data set, which we elect to be the
"COSMOS2020" galaxy catalog. After the training and calibration steps with
COSMOS2020, other galaxies can be processed through SOM to obtain an estimate
of their stellar mass and star formation rate (SFR). Both quantities result to
be in good agreement with independent measurements derived from more extended
photometric baseline, and also their combination (i.e., the SFR vs. stellar
mass diagram) shows a main sequence of star forming galaxies consistent with
previous studies. We discuss the advantages of this method compared to
traditional SED fitting, highlighting the impact of having, instead of the
usual synthetic templates, a collection of empirical SEDs built by the SOM in a
"data-driven" way. Such an approach also allows, even for extremely large data
sets, an efficient visual inspection to identify photometric errors or peculiar
galaxy types. Considering in addition the computational speed of this new
estimator, we argue that it will play a valuable role in the analysis of
oncoming large-area surveys like Euclid or the Legacy Survey of Space and Time
at the Vera Cooper Rubin Telescope.
