Identifying Galaxy Mergers in Simulated CEERS NIRCam Images using Random Forests

TL;DR

This study uses random forests on simulated JWST images to classify galaxy mergers, achieving about 60% accuracy across redshifts, and compares results with theoretical predictions.

Contribution

It introduces a novel application of random forests to classify galaxy mergers in simulated JWST images, incorporating realistic observational effects.

Findings

Random forests classify ~60% of mergers across redshifts.

Rest-frame asymmetry is more important at low redshift.

Merger fractions are underestimated by a factor of ~0.5.

Abstract

Identifying merging galaxies is an important - but difficult - step in galaxy evolution studies. We present random forest classifications of galaxy mergers from simulated JWST images based on various standard morphological parameters. We describe (a) constructing the simulated images from IllustrisTNG and the Santa Cruz SAM, and modifying them to mimic future CEERS observations as well as nearly noiseless observations, (b) measuring morphological parameters from these images, and (c) constructing and training the random forests using the merger history information for the simulated galaxies available from IllustrisTNG. The random forests correctly classify $\sim60\%$ of non-merging and merging galaxies across $0.5 < z < 4.0$. Rest-frame asymmetry parameters appear more important for lower redshift merger classifications, while rest-frame bulge and clump parameters appear more important for higher redshift classifications. Adjusting the classification probability threshold does not improve the performance of the forests. Finally, the shape and slope of the resulting merger fraction and merger rate derived from the random forest classifications match with theoretical Illustris predictions, but are underestimated by a factor of $\sim 0.5$.