Galaxy Light profile neural Networks (GaLNets). II. Bulge-Disc decomposition in optical space-based observations

TL;DR

This paper extends the GaLNet neural network to predict galaxy bulge and disk parameters from simulated space-based optical images, enabling detailed morphological analysis at higher redshifts with upcoming surveys.

Contribution

The study develops and tests a new GaLNet model for 2-Sersic bulge-disk decomposition using simulated CSST data, achieving high accuracy in parameter estimation up to redshift 1.

Findings

GaLNet accurately predicts bulge-disk parameters for galaxies down to r=23.5 mag at z~1.

The model provides reliable bulge-to-total ratio estimates.

CSST data will enable detailed galaxy structure studies at higher redshifts.

Abstract

Bulge-disk (B-D) decomposition is an effective diagnostic to characterize the galaxy morphology and understand its evolution across time. So far, high-quality data have allowed detailed B-D decomposition to redshift below 0.5, with limited excursions over small volumes at higher redshifts. Next-generation large sky space surveys in optical, e.g. from the China Space Station Telescope (CSST), and near-infrared, e.g. from the space EUCLID mission, will produce a gigantic leap in these studies as they will provide deep, high-quality photometric images over more than 15000 deg2 of the sky, including billions of galaxies. Here, we extend the use of the Galaxy Light profile neural Network (GaLNet) to predict 2-S\'ersic model parameters, specifically from CSST data. We simulate point-spread function (PSF) convolved galaxies, with realistic B-D parameter distributions, on CSST mock observations to train the new GaLNet and predict the structural parameters (e.g. magnitude, effective radius, Sersic index, axis ratio, etc.) of both bulge and disk components. We find that the GaLNet can achieve very good accuracy for most of the B-D parameters down to an $r$-band magnitude of 23.5 and redshift $\sim$1. The best accuracy is obtained for magnitudes, implying accurate bulge-to-total (B/T) estimates. To further forecast the CSST performances, we also discuss the results of the 1-S\'ersic GaLNet and show that CSST half-depth data will allow us to derive accurate 1-component models up to $r\sim$24 and redshift z$\sim$1.7.