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AGNet: weighing black holes with deep learning
- Source :
- Monthly Notices of the Royal Astronomical Society. 518:4921-4929
- Publication Year :
- 2022
- Publisher :
- Oxford University Press (OUP), 2022.
-
Abstract
- Supermassive black holes (SMBHs) are ubiquitously found at the centers of most massive galaxies. Measuring SMBH mass is important for understanding the origin and evolution of SMBHs. However, traditional methods require spectroscopic data which is expensive to gather. We present an algorithm that weighs SMBHs using quasar light time series, circumventing the need for expensive spectra. We train, validate, and test neural networks that directly learn from the Sloan Digital Sky Survey (SDSS) Stripe 82 light curves for a sample of $38,939$ spectroscopically confirmed quasars to map out the nonlinear encoding between SMBH mass and multi-color optical light curves. We find a 1$\sigma$ scatter of 0.37 dex between the predicted SMBH mass and the fiducial virial mass estimate based on SDSS single-epoch spectra, which is comparable to the systematic uncertainty in the virial mass estimate. Our results have direct implications for more efficient applications with future observations from the Vera C. Rubin Observatory. Our code, \textsf{AGNet}, is publicly available at \url{https://github.com/snehjp2/AGNet}.<br />Comment: 8 pages, 7 figures, 1 table, Accepted by MNRAS
- Subjects :
- High Energy Astrophysical Phenomena (astro-ph.HE)
FOS: Computer and information sciences
Computer Science - Machine Learning
Space and Planetary Science
Astrophysics of Galaxies (astro-ph.GA)
FOS: Physical sciences
Astronomy and Astrophysics
Astrophysics::Cosmology and Extragalactic Astrophysics
Astrophysics - High Energy Astrophysical Phenomena
Astrophysics - Astrophysics of Galaxies
Astrophysics::Galaxy Astrophysics
Machine Learning (cs.LG)
Subjects
Details
- ISSN :
- 13652966 and 00358711
- Volume :
- 518
- Database :
- OpenAIRE
- Journal :
- Monthly Notices of the Royal Astronomical Society
- Accession number :
- edsair.doi.dedup.....f35a843150099f11b0b20eafae645867