Identifying Galaxy Mergers in Simulated CEERS NIRCam Images Using Random Forests

Caitlin Rose; Jeyhan S. Kartaltepe; Gregory F. Snyder; Vicente Rodriguez-Gomez; L. Y. Aaron Yung; Pablo Arrabal Haro; Micaela B. Bagley; Antonello Calabró; Nikko J. Cleri; M. C. Cooper; Luca Costantin; Darren Croton; Mark Dickinson; Steven L. Finkelstein; Boris Häußler; Benne W. Holwerda; Anton M. Koekemoer; Peter Kurczynski; Ray A. Lucas; Kameswara Bharadwaj Mantha; Casey Papovich; Pablo G. Pérez-González; Nor Pirzkal; Rachel S. Somerville; Amber N. Straughn; Sandro Tacchella

doi:10.3847/1538-4357/ac9f10

Identifying Galaxy Mergers in Simulated CEERS NIRCam Images Using Random Forests

Caitlin Rose, Jeyhan S. Kartaltepe, Gregory F. Snyder, Vicente Rodriguez-Gomez, L. Y. Aaron Yung, Pablo Arrabal Haro, Micaela B. Bagley, Antonello Calabró, Nikko J. Cleri, M. C. Cooper, Luca Costantin, Darren Croton, Mark Dickinson, Steven L. Finkelstein, Boris Häußler, Benne W. Holwerda, Anton M. Koekemoer, Peter Kurczynski, Ray A. Lucas, Kameswara Bharadwaj ManthaCasey Papovich, Pablo G. Pérez-González, Nor Pirzkal, Rachel S. Somerville, Amber N. Straughn, Sandro Tacchella

Physics and Astronomy (Twin Cities)

Research output: Contribution to journal › Article › peer-review

12 Scopus citations

Abstract

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.

Original language	English (US)
Article number	54
Journal	Astrophysical Journal
Volume	942
Issue number	1
DOIs	https://doi.org/10.3847/1538-4357/ac9f10
State	Published - Jan 1 2023

Bibliographical note

Funding Information:
Support for this work was provided by NASA through grants JWST-ERS-01345.015-A and HST-AR-15802.001-A awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555. This research is based in part on observations made with the NASA/ESA Hubble Space Telescope obtained from the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 526555. LC acknowledges financial support from Comunidad de Madrid under Atracción de Talento grant 2018-T2/TIC-11612 and Spanish Ministerio de Ciencia e Innovación MCIN/AEI/10.13039/501100011033 through grant PGC2018-093499-B-I00.

Publisher Copyright:
© 2023. The Author(s). Published by the American Astronomical Society.

Publisher link

10.3847/1538-4357/ac9f10

Find It

Find It at U of M Libraries

Cite this

Rose, C., Kartaltepe, J. S., Snyder, G. F., Rodriguez-Gomez, V., Aaron Yung, L. Y., Haro, P. A., Bagley, M. B., Calabró, A., Cleri, N. J., Cooper, M. C., Costantin, L., Croton, D., Dickinson, M., Finkelstein, S. L., Häußler, B., Holwerda, B. W., Koekemoer, A. M., Kurczynski, P., Lucas, R. A., ... Tacchella, S. (2023). Identifying Galaxy Mergers in Simulated CEERS NIRCam Images Using Random Forests. Astrophysical Journal, 942(1), Article 54. https://doi.org/10.3847/1538-4357/ac9f10

Rose, C, Kartaltepe, JS, Snyder, GF, Rodriguez-Gomez, V, Aaron Yung, LY, Haro, PA, Bagley, MB, Calabró, A, Cleri, NJ, Cooper, MC, Costantin, L, Croton, D, Dickinson, M, Finkelstein, SL, Häußler, B, Holwerda, BW, Koekemoer, AM, Kurczynski, P, Lucas, RA, Mantha, KB, Papovich, C, Pérez-González, PG, Pirzkal, N, Somerville, RS, Straughn, AN & Tacchella, S 2023, 'Identifying Galaxy Mergers in Simulated CEERS NIRCam Images Using Random Forests', Astrophysical Journal, vol. 942, no. 1, 54. https://doi.org/10.3847/1538-4357/ac9f10

@article{b1661c761c6a40a08476086e98c2bffd,

title = "Identifying Galaxy Mergers in Simulated CEERS NIRCam Images Using Random Forests",

abstract = "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.",

author = "Caitlin Rose and Kartaltepe, {Jeyhan S.} and Snyder, {Gregory F.} and Vicente Rodriguez-Gomez and {Aaron Yung}, {L. Y.} and Haro, {Pablo Arrabal} and Bagley, {Micaela B.} and Antonello Calabr{\'o} and Cleri, {Nikko J.} and Cooper, {M. C.} and Luca Costantin and Darren Croton and Mark Dickinson and Finkelstein, {Steven L.} and Boris H{\"a}u{\ss}ler and Holwerda, {Benne W.} and Koekemoer, {Anton M.} and Peter Kurczynski and Lucas, {Ray A.} and Mantha, {Kameswara Bharadwaj} and Casey Papovich and P{\'e}rez-Gonz{\'a}lez, {Pablo G.} and Nor Pirzkal and Somerville, {Rachel S.} and Straughn, {Amber N.} and Sandro Tacchella",

note = "Funding Information: Support for this work was provided by NASA through grants JWST-ERS-01345.015-A and HST-AR-15802.001-A awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555. This research is based in part on observations made with the NASA/ESA Hubble Space Telescope obtained from the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 526555. LC acknowledges financial support from Comunidad de Madrid under Atracci{\'o}n de Talento grant 2018-T2/TIC-11612 and Spanish Ministerio de Ciencia e Innovaci{\'o}n MCIN/AEI/10.13039/501100011033 through grant PGC2018-093499-B-I00. Publisher Copyright: {\textcopyright} 2023. The Author(s). Published by the American Astronomical Society.",

year = "2023",

month = jan,

day = "1",

doi = "10.3847/1538-4357/ac9f10",

language = "English (US)",

volume = "942",

journal = "Astrophysical Journal",

issn = "0004-637X",

publisher = "IOP Publishing Ltd.",

number = "1",

}

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 - Funding Information: Support for this work was provided by NASA through grants JWST-ERS-01345.015-A and HST-AR-15802.001-A awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555. This research is based in part on observations made with the NASA/ESA Hubble Space Telescope obtained from the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 526555. LC acknowledges financial support from Comunidad de Madrid under Atracción de Talento grant 2018-T2/TIC-11612 and Spanish Ministerio de Ciencia e Innovación MCIN/AEI/10.13039/501100011033 through grant PGC2018-093499-B-I00. 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.

UR - http://www.scopus.com/inward/record.url?scp=85146510500&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85146510500&partnerID=8YFLogxK

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 -

Identifying Galaxy Mergers in Simulated CEERS NIRCam Images Using Random Forests

Abstract

Bibliographical note

Publisher link

Find It

Other files and links

Fingerprint

Cite this