Toward Complete Merger Identification at Cosmic Noon with Deep Learning

TL;DR

This paper demonstrates that deep learning models can effectively identify galaxy mergers, including minor and low-mass mergers at high redshifts, revealing insights into observational biases and model limitations.

Contribution

It introduces a ResNet18-based classifier trained on simulated HST images, capable of detecting subtle mergers at challenging redshifts, a novel achievement in astronomical image analysis.

Findings

Achieves 73% accuracy, purity, and completeness in merger classification.

Identifies that some mergers are only detectable from specific observation angles.

Reveals a latent space gradient related to stellar mass and star formation rate.

Abstract

As we enter the era of large imaging surveys such as $\textit{Roman}$, Rubin, and $\textit{Euclid}$, a deeper understanding of potential biases and selection effects in optical astronomical catalogs created with the use of ML-based methods is paramount. This work focuses on a deeper understanding of the performance and limitations of deep learning-based classifiers as tools for galaxy merger identification. We train a ResNet18 model on mock Hubble Space Telescope CANDELS images from the IllustrisTNG50 simulation. Our focus is on a more challenging classification of galaxy mergers and nonmergers at higher redshifts $1<z<1.5$, including minor mergers and lower mass galaxies down to the stellar mass of $10^8 M_\odot$. We demonstrate, for the first time, that a deep learning model, such as the one developed in this work, can successfully identify even minor and low mass mergers even at these redshifts. Our model achieves overall accuracy, purity, and completeness of 73%. We show that some galaxy mergers can only be identified from certain observation angles, leading to a potential upper limit in overall accuracy. Using Grad-CAMs and UMAPs, we more deeply examine the performance and observe a visible gradient in the latent space with stellar mass and specific star formation rate, but no visible gradient with merger mass ratio or merger stage.