A machine learning software to estimate morphological parameters of distant galaxies
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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.
Original language | English |
---|---|
Title of host publication | Software and Cyberinfrastructure for Astronomy VI |
Editors | Juan C. Guzman, Jorge Ibsen |
Publisher | SPIE - International Society for Optical Engineering |
Publication date | 2020 |
Article number | 1145223 |
ISBN (Electronic) | 9781510636910 |
DOIs | |
Publication status | Published - 2020 |
Event | Software and Cyberinfrastructure for Astronomy VI 2020 - Virtual, Online, United States Duration: 14 Dec 2020 → 18 Dec 2020 |
Conference
Conference | Software and Cyberinfrastructure for Astronomy VI 2020 |
---|---|
Land | United States |
By | Virtual, Online |
Periode | 14/12/2020 → 18/12/2020 |
Sponsor | The Society of Photo-Optical Instrumentation Engineers (SPIE) |
Series | Proceedings of SPIE - The International Society for Optical Engineering |
---|---|
Volume | 11452 |
ISSN | 0277-786X |
Bibliographical note
Publisher Copyright:
© 2020 SPIE.
- Galaxy morphology, Machine learning
Research areas
ID: 271555760