A machine learning software to estimate morphological parameters of distant galaxies
A machine learning software to estimate morphological parameters of distant galaxies
复制标题
用于估计遥远星系形态参数的机器学习软件
DOI:
10.1117/12.2561264
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发表时间:
2020
期刊:
影响因子:
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通讯作者:
Toba Yoshiki
中科院分区:
文献类型:
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作者:
Umayahara Takuya;Shibuya Takatoshi;Miura Noriaki;Chang Yu-Yen;Fujimoto Seiji;Harikane Yuichi;Higuchi Ryo;Inoue Shigeki;Kojima Takashi;Tadaki Ken-ichi;Toba Yoshiki
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.
DOI:
10.3847/0067-0049/224/2/24
发表时间:
2016-04
期刊:
The Astrophysical Journal Supplement Series
影响因子:
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作者:
C. Laigle;H. McCracken;O. Ilbert;B. Hsieh;I. Davidzon;P. Capak;G. Hasinger;J. Silverman;C. Pi
通讯作者:
C. Laigle;H. McCracken;O. Ilbert;B. Hsieh;I. Davidzon;P. Capak;G. Hasinger;J. Silverman;C. Pi
DOI:
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发表时间:
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期刊:
影响因子:
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作者:
通讯作者:
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DOI:
10.3847/2041-8213/ab4ff3
发表时间:
2019-09
期刊:
The Astrophysical Journal Letters
影响因子:
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作者:
Masayuki Tanaka;F. Valentino;S. Toft;M. Onodera;R. Shimakawa;D. Ceverino;A. Faisst;A. Gallazzi;C. Gómez-Guijarro;M. Kubo;G. Magdis;C. Steinhardt;M. Stockmann;K. Yabe;J. Zabl
通讯作者:
Masayuki Tanaka;F. Valentino;S. Toft;M. Onodera;R. Shimakawa;D. Ceverino;A. Faisst;A. Gallazzi;C. Gómez-Guijarro;M. Kubo;G. Magdis;C. Steinhardt;M. Stockmann;K. Yabe;J. Zabl