TY - JOUR
T1 - Identifying Galaxy Mergers in Simulated CEERS NIRCam Images Using Random Forests
AU - Rose, Caitlin
AU - Kartaltepe, Jeyhan S.
AU - Snyder, Gregory F.
AU - Rodriguez-Gomez, Vicente
AU - Aaron Yung, L. Y.
AU - Haro, Pablo Arrabal
AU - Bagley, Micaela B.
AU - Calabró, Antonello
AU - Cleri, Nikko J.
AU - Cooper, M. C.
AU - Costantin, Luca
AU - Croton, Darren
AU - Dickinson, Mark
AU - Finkelstein, Steven L.
AU - Häußler, Boris
AU - Holwerda, Benne W.
AU - Koekemoer, Anton M.
AU - Kurczynski, Peter
AU - Lucas, Ray A.
AU - Mantha, Kameswara Bharadwaj
AU - Papovich, Casey
AU - Pérez-González, Pablo G.
AU - Pirzkal, Nor
AU - Somerville, Rachel S.
AU - Straughn, Amber N.
AU - Tacchella, Sandro
N1 - Publisher Copyright:
© 2023. The Author(s). Published by the American Astronomical Society.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - 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.
AB - 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.
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U2 - 10.3847/1538-4357/ac9f10
DO - 10.3847/1538-4357/ac9f10
M3 - Article
AN - SCOPUS:85146510500
SN - 0004-637X
VL - 942
JO - Astrophysical Journal
JF - Astrophysical Journal
IS - 1
M1 - 54
ER -