Robust machine learning model of inferring the ex situ stellar fraction of galaxies from photometric data

¹ Shanghai Astronomical Observatory, Chinese Academy of Sciences, 80 Nandan Road, Shanghai 200030, China
² School of Astronomy and Space Sciences, University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, People’s Republic of China
³ Department of Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China
⁴ Max Planck Institute for Astronomy, Königstuhl 17, 69117 Heidelberg, Germany
⁵ Instituto de Astrofísica de Canarias, Calle Via Láctea s/n, 38200 La Laguna, Tenerife, Spain
⁶ Depto. Astrofísica, Universidad de La Laguna, Calle Astrofísico Francisco Sánchez s/n, 38206, La Laguna, Tenerife, Spain

^⋆ Corresponding authors; cairunsheng@shao.ac.cn; lzhu@shao.ac.cn

Received: 28 October 2024
Accepted: 24 January 2025

Abstract

We searched for the parameters defined from photometric images to quantify the ex situ stellar mass fraction of galaxies. We created mock images using galaxies in the cosmological hydrodynamical simulations TNG100, EAGLE, and TNG50 at redshift z = 0. We defined a series of parameters describing their structures, including the absolute magnitude in r and g bands (M_r, M_g), the half-light and 90% light radius (r₅₀, r₉₀), the concentration (C), the luminosity fractions of inner and outer halos (f_innerhalo, f_outerhalo), and the inner and outer surface brightness gradients (∇ρ_inner,∇ρ_outer) and g − r colour gradients (∇(g − r)_inner,∇(g − r)_outer). In particular, the inner and outer halo of a galaxy are defined by sectors ranging from 45 to 135 degrees from the disk major axis, and with radii ranging from 3.5 to 10 kpc and 10 to 30 kpc, respectively, to avoid the contamination of disk and bulge. The surface brightness and colour gradients are defined by the same sectors along the minor axis and with similar radii ranges. We used the random forest method to create a model that predicts f_exsitu from morphological parameters. The model predicts f_exsitu well with a scatter smaller than 0.1 compared to the ground truth in all mass ranges. The models trained from TNG100 and EAGLE work similarly well and are cross-validated; they also work well in making predictions for TNG50 galaxies. The analysis using random forest reveals that ∇ρ_outer, ∇(g − r)_outer, f_outerhalo, and f_innerhalo are the most influential parameters in predicting f_exsitu, underscoring their significance in uncovering the merging history of galaxies. We further analysed how the quality of images will affect the results by using SDSS-like and HSC-like mock images for galaxies at different distances. Our results can be used to infer the ex situ stellar mass fractions for a large sample of galaxies from photometric surveys.