Identifying Galaxy Mergers in Simulated CEERS NIRCam Images using Random Forests

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 IllustrisT...

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Veröffentlicht in:	arXiv.org 2022-08
Hauptverfasser:	Rose, Caitlin, Kartaltepe, Jeyhan S, Snyder, Gregory F, Rodriguez-Gomez, Vicente, Yung, L Y Aaron, Pablo Arrabal Haro, Bagley, Micaela B, Calabrò, Antonello, Cleri, Nikko J, Cooper, M C, Costantin, Luca, Croton, Darren, Dickinson, Mark, Finkelstein, Steven L, Häußler, Boris, Holwerda, Benne W, Koekemoer, Anton M, Kurczynski, Peter, Lucas, Ray A, Kameswara Bharadwaj Mantha, Papovich, Casey, Pérez-González, Pablo G, Pirzkal, Nor, Somerville, Rachel S, Straughn, Amber N, Tacchella, Sandro
Format:	Artikel
Sprache:	eng
Schlagworte:	Galactic evolution Galaxy mergers & collisions Machine learning Morphology Parameters Physics - Astrophysics of Galaxies Red shift Simulation Stars & galaxies
Online-Zugang:	Volltext
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