A machine learning software to estimate morphological parameters of distant galaxies
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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.
Originalsprog | Engelsk |
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Titel | Software and Cyberinfrastructure for Astronomy VI |
Redaktører | Juan C. Guzman, Jorge Ibsen |
Forlag | SPIE - International Society for Optical Engineering |
Publikationsdato | 2020 |
Artikelnummer | 1145223 |
ISBN (Elektronisk) | 9781510636910 |
DOI | |
Status | Udgivet - 2020 |
Begivenhed | Software and Cyberinfrastructure for Astronomy VI 2020 - Virtual, Online, USA Varighed: 14 dec. 2020 → 18 dec. 2020 |
Konference
Konference | Software and Cyberinfrastructure for Astronomy VI 2020 |
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Land | USA |
By | Virtual, Online |
Periode | 14/12/2020 → 18/12/2020 |
Sponsor | The Society of Photo-Optical Instrumentation Engineers (SPIE) |
Navn | Proceedings of SPIE - The International Society for Optical Engineering |
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Vol/bind | 11452 |
ISSN | 0277-786X |
Bibliografisk note
Publisher Copyright:
© 2020 SPIE.
ID: 271555760