  We are presenting a novel, Deep Learning based approach to estimate the
normalized broadband spectral energy distribution (SED) of different stellar
populations in synthetic galaxies. In contrast to the non-parametric multiband
source separation algorithm, SCARLET - where the SED and morphology are
simultaneously fitted - in our study we provide a morphology-independent,
statistical determination of the SEDs, where we only use the color distribution
of the galaxy. We developed a neural network (sedNN) that accurately predicts
the SEDs of the old, red and young, blue stellar populations of realistic
synthetic galaxies from the color distribution of the galaxy-related pixels in
simulated broadband images. We trained and tested the network on a subset of
the recently published CosmoDC2 simulated galaxy catalog containing about 3,600
galaxies. The model performance was compared to the results of SCARLET, where
we found that sedNN can predict the SEDs with 4-5% accuracy on average, which
is about two times better than applying SCARLET. We also investigated the
effect of this improvement on the flux determination accuracy of the bulge and
disk. We found that using more accurate SEDs decreases the error in the flux
determination of the components by approximately 30%.

Instrumentation and Methods for Astrophysics;Astrophysics of Galaxies