A machine learning software to estimate morphological parameters of distant galaxies

Takuya Umayahara; Takatoshi Shibuya; Noriaki Miura; Yu-Yen Chang; Seiji Fujimoto; Yuichi Harikane; Ryo Higuchi; Shigeki Inoue; Takashi Kojima; Ken-ichi Tadaki; Yoshiki Toba

doi:10.1117/12.2561264

13 December 2020 A machine learning software to estimate morphological parameters of distant galaxies

Takuya Umayahara, Takatoshi Shibuya, Noriaki Miura, Yu-Yen Chang, Seiji Fujimoto, Yuichi Harikane, Ryo Higuchi, Shigeki Inoue, Takashi Kojima, Ken-ichi Tadaki, Yoshiki Toba

Author Affiliations +

Proceedings Volume 11452, Software and Cyberinfrastructure for Astronomy VI; 1145223 (2020) https://doi.org/10.1117/12.2561264
Event: SPIE Astronomical Telescopes + Instrumentation, 2020, Online Only

Conference Poster

Abstract

We develop a machine learning (ML) software to estimate morphological parameters (e.g., the half-light radius r_e) 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.

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Takuya Umayahara, Takatoshi Shibuya, Noriaki Miura, Yu-Yen Chang, Seiji Fujimoto, Yuichi Harikane, Ryo Higuchi, Shigeki Inoue, Takashi Kojima, Ken-ichi Tadaki, and Yoshiki Toba "A machine learning software to estimate morphological parameters of distant galaxies", Proc. SPIE 11452, Software and Cyberinfrastructure for Astronomy VI, 1145223 (13 December 2020); https://doi.org/10.1117/12.2561264

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