1. CEERS Key Paper. IX. Identifying Galaxy Mergers in CEERS NIRCam Images Using Random Forests and Convolutional Neural Networks
- Author
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Rose, Caitlin, Kartaltepe, Jeyhan S., Snyder, Gregory F., Huertas-Company, Marc, Yung, L. Y. Aaron, Haro, Pablo Arrabal, Bagley, Micaela B., Bisigello, Laura, Calabrò, Antonello, Cleri, Nikko J., Dickinson, Mark, Ferguson, Henry C., Finkelstein, Steven L., Fontana, Adriano, Grazian, Andrea, Grogin, Norman A., Holwerda, Benne W., Iyer, Kartheik G., Kewley, Lisa J., Kirkpatrick, Allison, Kocevski, Dale D., Koekemoer, Anton M., Lotz, Jennifer M., Lucas, Ray A., Napolitan, Lorenzo, Papovich, Casey, Pentericci, Laura, Pérez-González, Pablo G., Pirzkal, Nor, Ravindranath, Swara, Somerville, Rachel S., Straughn, Amber N., Trump, Jonathan R., Wilkins, Stephen M., and Yang, Guang
- Subjects
Astrophysics - Astrophysics of Galaxies - Abstract
A crucial yet challenging task in galaxy evolution studies is the identification of distant merging galaxies, a task which suffers from a variety of issues ranging from telescope sensitivities and limitations to the inherently chaotic morphologies of young galaxies. In this paper, we use random forests and convolutional neural networks to identify high-redshift JWST CEERS galaxy mergers. We train these algorithms on simulated $3
- Published
- 2024