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AGNet: weighing black holes with deep learning

Journal Article · · Monthly Notices of the Royal Astronomical Society
 [1];  [2];  [3];  [3];  [3];  [3]
  1. University of Illinois at Urbana-Champaign, IL (United States); National Center for Supercomputing Applications, Champaign, IL (United States); Flatiron Institute, New York, NY (United States)
  2. University of Illinois at Urbana-Champaign, IL (United States); National Center for Supercomputing Applications, Champaign, IL (United States)
  3. National Center for Supercomputing Applications, Champaign, IL (United States); University of Illinois at Urbana-Champaign, IL (United States)
+ Show Author Affiliations
Supermassive black holes (SMBHs) are commonly found at the centres of most massive galaxies. Measuring SMBH mass is crucial for understanding the origin and evolution of SMBHs. Traditional approaches, on the other hand, necessitate the collection of spectroscopic data, which is costly. We present an algorithm that weighs SMBHs using quasar light time series information, including colours, multiband magnitudes, and the variability of the light curves, 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 non-linear encoding between SMBH mass and multiband optical light curves. We find a 1σ 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, AGNet, is publicly available at https://github.com/snehjp2/AGNet.
Research Organization:
US Department of Energy (USDOE), Washington, DC (United States). Office of Science, Sloan Digital Sky Survey (SDSS)
Sponsoring Organization:
National Science Foundation (NSF); USDOE
OSTI ID:
1902795
Alternate ID(s):
OSTI ID: 2425097
Journal Information:
Monthly Notices of the Royal Astronomical Society, Journal Name: Monthly Notices of the Royal Astronomical Society Journal Issue: 4 Vol. 518; ISSN 0035-8711
Publisher:
Oxford University PressCopyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

79 ASTRONOMY AND ASTROPHYSICS
Astronomy & Astrophysics
accretion
accretion discs
black hole physics
galaxies: active
galaxies: nuclei
quasars: general