Identifying Galaxy Mergers in Simulated CEERS NIRCam Images using Random Forests

Understand

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 $0.5 < z < 4.0$.