Decomposition of stellar populations in CosmoDC2 galaxies using SCARLET and Deep Learning

TL;DR

This paper introduces a deep learning method to estimate stellar population SEDs in galaxies from color data, outperforming traditional algorithms and improving flux measurement accuracy.

Contribution

The study presents sedNN, a neural network that predicts galaxy stellar population SEDs from color distributions, offering a morphology-independent and more accurate alternative to SCARLET.

Findings

sedNN predicts SEDs with 4-5% accuracy, twice as good as SCARLET.

Using sedNN reduces flux error in galaxy components by about 30%.

The approach is validated on the CosmoDC2 simulated galaxy catalog.

Abstract

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%.