The Astrophysical Journal (Jan 2023)

Identifying Galaxy Mergers in Simulated CEERS NIRCam Images Using Random Forests

  • Caitlin Rose,
  • Jeyhan S. Kartaltepe,
  • Gregory F. Snyder,
  • Vicente Rodriguez-Gomez,
  • L. Y. Aaron Yung,
  • Pablo Arrabal Haro,
  • Micaela B. Bagley,
  • Antonello Calabró,
  • Nikko J. Cleri,
  • M. C. Cooper,
  • Luca Costantin,
  • Darren Croton,
  • Mark Dickinson,
  • Steven L. Finkelstein,
  • Boris Häußler,
  • Benne W. Holwerda,
  • Anton M. Koekemoer,
  • Peter Kurczynski,
  • Ray A. Lucas,
  • Kameswara Bharadwaj Mantha,
  • Casey Papovich,
  • Pablo G. Pérez-González,
  • Nor Pirzkal,
  • Rachel S. Somerville,
  • Amber N. Straughn,
  • Sandro Tacchella

DOI
https://doi.org/10.3847/1538-4357/ac9f10
Journal volume & issue
Vol. 942, no. 1
p. 54

Abstract

Read online

Identifying merging galaxies is an important—but difficult—step in galaxy evolution studies. We present random forest (RF) 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 and nearly noiseless observations, (b) measuring morphological parameters from these images, and (c) constructing and training the RFs using the merger history information for the simulated galaxies available from IllustrisTNG. The RFs correctly classify ∼60% 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 RF classifications match with theoretical Illustris predictions but are underestimated by a factor of ∼0.5.

Keywords