CEERS Key Paper. IX. Identifying Galaxy Mergers in CEERS NIRCam Images Using Random Forests and Convolutional Neural Networks

Abstract A crucial yet challenging task in galaxy evolution studies is the identification of distant merging galaxies, a task that 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 Cosmic Evolution Early Release Science Survey (CEERS) galaxy mergers. We train these algorithms on simulated 3 < z < 5 CEERS galaxies created from the IllustrisTNG subhalo morphologies and the Santa Cruz SAM light cone. We apply our models to observed CEERS galaxies at 3 < z < 5. We find that our models correctly classify ∼60%–70% of simulated merging and nonmerging galaxies; better performance on the merger class comes at the expense of misclassifying more nonmergers. We could achieve more accurate classifications, as well as test for a dependency on physical parameters such as gas fraction, mass ratio, and relative orbits, by curating larger training sets. When applied to real CEERS galaxies using visual classifications as ground truth, the random forests correctly classified 40%–60% of mergers and nonmergers at 3 < z < 4 but tended to classify most objects as nonmergers at 4 < z < 5 (misclassifying ∼70% of visually classified mergers). On the other hand, the CNNs tended to classify most objects as mergers across all redshifts (misclassifying 80%–90% of visually classified nonmergers). We investigate what features the models find most useful, as well as the characteristics of false positives and false negatives, and also calculate merger rates derived from the identifications made by the models.

Authors

Publication Details

Journal
The Astrophysical Journal Letters
Published
2024-11-01
DOI
https://doi.org/10.3847/2041-8213/ad8dd4
Citations
5
Primary Topic
Galaxies: Formation, Evolution, Phenomena
Type
article
Field-Weighted Citation Impact
1.66

Funders

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article

CEERS Key Paper. IX. Identifying Galaxy Mergers in CEERS NIRCam Images Using Random Forests and Convolutional Neural Networks

Laura Bisigello, Ray A. Lucas, Swara Ravindranath, Stephen M. Wilkins et al.
5 citations
The Astrophysical Journal Letters
Galaxies: Formation, Evolution, Phenomena
1.66
article

CEERS Key Paper. IX. Identifying Galaxy Mergers in CEERS NIRCam Images Using Random Forests and Convolutional Neural Networks

Laura Bisigello, Ray A. Lucas, Swara Ravindranath, Stephen M. Wilkins, Allison Kirkpatrick, A. Grazian, L. Pentericci, Norman A. Grogin, Lisa J. Kewley, Jennifer M. Lotz, Rachel S. Somerville, Pablo Arrabal Haro, Casey Papovich, Henry C. Ferguson, Anton M. Koekemoer, Pablo G. Pérez‐González, Caitlin Rose, Antonello Calabrò, Gregory F. Snyder, L. Y. Aaron Yung, Benne W. Holwerda, Kartheik G. Iyer, Guang Yang, Nor Pirzkal, Nikko J. Cleri, Jonathan R. Trump, Steven L. Finkelstein, Mark Dickinson, Micaela B. Bagley, Dale D. Kocevski, Jeyhan S. Kartaltepe, A. Fontana, Amber N. Straughn, Lorenzo Napolitano
article en
5 citations

Abstract

Abstract A crucial yet challenging task in galaxy evolution studies is the identification of distant merging galaxies, a task that 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 Cosmic Evolution Early Release Science Survey (CEERS) galaxy mergers. We train these algorithms on simulated 3 < z < 5 CEERS galaxies created from the IllustrisTNG subhalo morphologies and the Santa Cruz SAM light cone. We apply our models to observed CEERS galaxies at 3 < z < 5. We find that our models correctly classify ∼60%–70% of simulated merging and nonmerging galaxies; better performance on the merger class comes at the expense of misclassifying more nonmergers. We could achieve more accurate classifications, as well as test for a dependency on physical parameters such as gas fraction, mass ratio, and relative orbits, by curating larger training sets. When applied to real CEERS galaxies using visual classifications as ground truth, the random forests correctly classified 40%–60% of mergers and nonmergers at 3 < z < 4 but tended to classify most objects as nonmergers at 4 < z < 5 (misclassifying ∼70% of visually classified mergers). On the other hand, the CNNs tended to classify most objects as mergers across all redshifts (misclassifying 80%–90% of visually classified nonmergers). We investigate what features the models find most useful, as well as the characteristics of false positives and false negatives, and also calculate merger rates derived from the identifications made by the models.

The Astrophysical Journal LettersVol. 976(1)
Space Telescope Science Institute, Science and Technology Facilities Council
Life in Land
Openalex Percentile: Top 16%
Galaxies: Formation, Evolution, Phenomena
1.66
DOIOpen Access PDFOpenAlex
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