Standard
A machine learning software to estimate morphological parameters of distant galaxies. / Umayahara, Takuya; Shibuya, Takatoshi; Miura, Noriaki; Chang, Yu Yen; Fujimoto, Seiji; Harikane, Yuichi; Higuchi, Ryo; Inoue, Shigeki; Kojima, Takashi; Tadaki, Ken Ichi; Toba, Yoshiki.
Software and Cyberinfrastructure for Astronomy VI. ed. / Juan C. Guzman; Jorge Ibsen. SPIE - International Society for Optical Engineering, 2020. 1145223 (Proceedings of SPIE - The International Society for Optical Engineering, Vol. 11452).
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
Umayahara, T, Shibuya, T, Miura, N, Chang, YY, Fujimoto, S, Harikane, Y, Higuchi, R, Inoue, S, Kojima, T, Tadaki, KI & Toba, Y 2020,
A machine learning software to estimate morphological parameters of distant galaxies. in JC Guzman & J Ibsen (eds),
Software and Cyberinfrastructure for Astronomy VI., 1145223, SPIE - International Society for Optical Engineering, Proceedings of SPIE - The International Society for Optical Engineering, vol. 11452, Software and Cyberinfrastructure for Astronomy VI 2020, Virtual, Online, United States,
14/12/2020.
https://doi.org/10.1117/12.2561264APA
Umayahara, T., Shibuya, T., Miura, N., Chang, Y. Y., Fujimoto, S., Harikane, Y., Higuchi, R., Inoue, S., Kojima, T., Tadaki, K. I., & Toba, Y. (2020).
A machine learning software to estimate morphological parameters of distant galaxies. In J. C. Guzman, & J. Ibsen (Eds.),
Software and Cyberinfrastructure for Astronomy VI [1145223] SPIE - International Society for Optical Engineering. Proceedings of SPIE - The International Society for Optical Engineering Vol. 11452
https://doi.org/10.1117/12.2561264Vancouver
Umayahara T, Shibuya T, Miura N, Chang YY, Fujimoto S, Harikane Y et al.
A machine learning software to estimate morphological parameters of distant galaxies. In Guzman JC, Ibsen J, editors, Software and Cyberinfrastructure for Astronomy VI. SPIE - International Society for Optical Engineering. 2020. 1145223. (Proceedings of SPIE - The International Society for Optical Engineering, Vol. 11452).
https://doi.org/10.1117/12.2561264Author
Umayahara, Takuya ; Shibuya, Takatoshi ; Miura, Noriaki ; Chang, Yu Yen ; Fujimoto, Seiji ; Harikane, Yuichi ; Higuchi, Ryo ; Inoue, Shigeki ; Kojima, Takashi ; Tadaki, Ken Ichi ; Toba, Yoshiki. / A machine learning software to estimate morphological parameters of distant galaxies. Software and Cyberinfrastructure for Astronomy VI. editor / Juan C. Guzman ; Jorge Ibsen. SPIE - International Society for Optical Engineering, 2020. (Proceedings of SPIE - The International Society for Optical Engineering, Vol. 11452).
Bibtex
@inproceedings{1fdf353e1e7048688309c01130cffd37,
title = "A machine learning software to estimate morphological parameters of distant galaxies",
abstract = "We develop a machine learning (ML) software to estimate morphological parameters (e.g., the half-light radius re) of high redshift galaxies in the Subaru/Hyper Suprime-Cam data. To make the ML software capture simultaneously galaxy morphological features and point spread function (PSF) broadening effects, we implement a two-stream convolutional neural network (CNN) for inputs of galaxy and PSF images. Thanks to large training samples of galaxy and PSF images, the two-stream CNN estimates re more accurately than a single-stream CNN with only galaxy images. Our ML software would be a useful tool to investigate galaxy morphological properties with PSF-unstable images obtained in future large-area ground-based surveys. ",
keywords = "Galaxy morphology, Machine learning",
author = "Takuya Umayahara and Takatoshi Shibuya and Noriaki Miura and Chang, {Yu Yen} and Seiji Fujimoto and Yuichi Harikane and Ryo Higuchi and Shigeki Inoue and Takashi Kojima and Tadaki, {Ken Ichi} and Yoshiki Toba",
note = "Publisher Copyright: {\textcopyright} 2020 SPIE.; Software and Cyberinfrastructure for Astronomy VI 2020 ; Conference date: 14-12-2020 Through 18-12-2020",
year = "2020",
doi = "10.1117/12.2561264",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE - International Society for Optical Engineering",
editor = "Guzman, {Juan C.} and Jorge Ibsen",
booktitle = "Software and Cyberinfrastructure for Astronomy VI",
}
RIS
TY - GEN
T1 - A machine learning software to estimate morphological parameters of distant galaxies
AU - Umayahara, Takuya
AU - Shibuya, Takatoshi
AU - Miura, Noriaki
AU - Chang, Yu Yen
AU - Fujimoto, Seiji
AU - Harikane, Yuichi
AU - Higuchi, Ryo
AU - Inoue, Shigeki
AU - Kojima, Takashi
AU - Tadaki, Ken Ichi
AU - Toba, Yoshiki
N1 - Publisher Copyright:
© 2020 SPIE.
PY - 2020
Y1 - 2020
N2 - We develop a machine learning (ML) software to estimate morphological parameters (e.g., the half-light radius re) of high redshift galaxies in the Subaru/Hyper Suprime-Cam data. To make the ML software capture simultaneously galaxy morphological features and point spread function (PSF) broadening effects, we implement a two-stream convolutional neural network (CNN) for inputs of galaxy and PSF images. Thanks to large training samples of galaxy and PSF images, the two-stream CNN estimates re more accurately than a single-stream CNN with only galaxy images. Our ML software would be a useful tool to investigate galaxy morphological properties with PSF-unstable images obtained in future large-area ground-based surveys.
AB - We develop a machine learning (ML) software to estimate morphological parameters (e.g., the half-light radius re) of high redshift galaxies in the Subaru/Hyper Suprime-Cam data. To make the ML software capture simultaneously galaxy morphological features and point spread function (PSF) broadening effects, we implement a two-stream convolutional neural network (CNN) for inputs of galaxy and PSF images. Thanks to large training samples of galaxy and PSF images, the two-stream CNN estimates re more accurately than a single-stream CNN with only galaxy images. Our ML software would be a useful tool to investigate galaxy morphological properties with PSF-unstable images obtained in future large-area ground-based surveys.
KW - Galaxy morphology
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85099396938&partnerID=8YFLogxK
U2 - 10.1117/12.2561264
DO - 10.1117/12.2561264
M3 - Article in proceedings
AN - SCOPUS:85099396938
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Software and Cyberinfrastructure for Astronomy VI
A2 - Guzman, Juan C.
A2 - Ibsen, Jorge
PB - SPIE - International Society for Optical Engineering
T2 - Software and Cyberinfrastructure for Astronomy VI 2020
Y2 - 14 December 2020 through 18 December 2020
ER -
