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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| creator | 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 |
| description | 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 IllustrisTNG and the Santa Cruz SAM, and modifying them to mimic future CEERS observations as well as nearly noiseless observations, (b) measuring morphological parameters from these images, and (c) constructing and training the random forests using the merger history information for the simulated galaxies available from IllustrisTNG. The random forests correctly classify \(\sim60\%\) 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 random forest classifications match with theoretical Illustris predictions, but are underestimated by a factor of \(\sim 0.5\). |
| doi_str_mv | 10.48550/arxiv.2208.11164 |
| format | Article |
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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 IllustrisTNG and the Santa Cruz SAM, and modifying them to mimic future CEERS observations as well as nearly noiseless observations, (b) measuring morphological parameters from these images, and (c) constructing and training the random forests using the merger history information for the simulated galaxies available from IllustrisTNG. The random forests correctly classify \(\sim60\%\) 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 random forest classifications match with theoretical Illustris predictions, but are underestimated by a factor of \(\sim 0.5\).</description><identifier>EISSN: 2331-8422</identifier><identifier>DOI: 10.48550/arxiv.2208.11164</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Galactic evolution ; Galaxy mergers & collisions ; Machine learning ; Morphology ; Parameters ; Physics - Astrophysics of Galaxies ; Red shift ; Simulation ; Stars & galaxies</subject><ispartof>arXiv.org, 2022-08</ispartof><rights>2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>http://creativecommons.org/licenses/by/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,784,885,27925</link.rule.ids><backlink>$$Uhttps://doi.org/10.3847/1538-4357/ac9f10$$DView published paper (Access to full text may be restricted)$$Hfree_for_read</backlink><backlink>$$Uhttps://doi.org/10.48550/arXiv.2208.11164$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Rose, Caitlin</creatorcontrib><creatorcontrib>Kartaltepe, Jeyhan S</creatorcontrib><creatorcontrib>Snyder, Gregory F</creatorcontrib><creatorcontrib>Rodriguez-Gomez, Vicente</creatorcontrib><creatorcontrib>Yung, L Y Aaron</creatorcontrib><creatorcontrib>Pablo Arrabal Haro</creatorcontrib><creatorcontrib>Bagley, Micaela B</creatorcontrib><creatorcontrib>Calabrò, Antonello</creatorcontrib><creatorcontrib>Cleri, Nikko J</creatorcontrib><creatorcontrib>Cooper, M C</creatorcontrib><creatorcontrib>Costantin, Luca</creatorcontrib><creatorcontrib>Croton, Darren</creatorcontrib><creatorcontrib>Dickinson, Mark</creatorcontrib><creatorcontrib>Finkelstein, Steven L</creatorcontrib><creatorcontrib>Häußler, Boris</creatorcontrib><creatorcontrib>Holwerda, Benne W</creatorcontrib><creatorcontrib>Koekemoer, Anton M</creatorcontrib><creatorcontrib>Kurczynski, Peter</creatorcontrib><creatorcontrib>Lucas, Ray A</creatorcontrib><creatorcontrib>Kameswara Bharadwaj Mantha</creatorcontrib><creatorcontrib>Papovich, Casey</creatorcontrib><creatorcontrib>Pérez-González, Pablo G</creatorcontrib><creatorcontrib>Pirzkal, Nor</creatorcontrib><creatorcontrib>Somerville, Rachel S</creatorcontrib><creatorcontrib>Straughn, Amber N</creatorcontrib><creatorcontrib>Tacchella, Sandro</creatorcontrib><title>Identifying Galaxy Mergers in Simulated CEERS NIRCam Images using Random Forests</title><title>arXiv.org</title><description>Identifying merging galaxies is an important - but difficult - step in galaxy evolution studies. 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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 IllustrisTNG and the Santa Cruz SAM, and modifying them to mimic future CEERS observations as well as nearly noiseless observations, (b) measuring morphological parameters from these images, and (c) constructing and training the random forests using the merger history information for the simulated galaxies available from IllustrisTNG. The random forests correctly classify \(\sim60\%\) 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. 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| subjects | Galactic evolution Galaxy mergers & collisions Machine learning Morphology Parameters Physics - Astrophysics of Galaxies Red shift Simulation Stars & galaxies |
| title | Identifying Galaxy Mergers in Simulated CEERS NIRCam Images using Random Forests |
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