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Deep Modeling of Quasar Variability

Tachibana, Yutaro and Graham, Matthew J. and Kawai, Nobuyuki and Djorgovski, S. G. and Drake, Andrew J. and Mahabal, Ashish A. and Stern, Daniel (2020) Deep Modeling of Quasar Variability. Astrophysical Journal, 903 (1). Art. No. 54. ISSN 1538-4357. https://resolver.caltech.edu/CaltechAUTHORS:20200414-070718405

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Abstract

Quasars have long been known as intrinsically variable sources, but the physical mechanism underlying the temporal optical/UV variability is still not well understood. We propose a novel nonparametric method for modeling and forecasting the optical variability of quasars utilizing an AE neural network to gain insight into the underlying processes. The AE is trained with ~15,000 decade-long quasar light curves obtained by the Catalina Real-time Transient Survey selected with negligible flux contamination from the host galaxy. The AE's performance in forecasting the temporal flux variation of quasars is superior to that of the damped random walk process. We find a temporal asymmetry in the optical variability and a novel relation—the amplitude of the variability asymmetry decreases as luminosity and/or black hole mass increases—is suggested with the help of autoencoded features. The characteristics of the variability asymmetry are in agreement with those from the self-organized disk instability model, which predicts that the magnitude of the variability asymmetry decreases as the ratio of the diffusion mass to inflow mass in the accretion disk increases.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.3847/1538-4357/abb9a9DOIArticle
https://arxiv.org/abs/2003.01241arXivDiscussion Paper
ORCID:
AuthorORCID
Tachibana, Yutaro0000-0001-6584-6945
Graham, Matthew J.0000-0002-3168-0139
Kawai, Nobuyuki0000-0001-9656-0261
Djorgovski, S. G.0000-0002-0603-3087
Mahabal, Ashish A.0000-0003-2242-0244
Stern, Daniel0000-0003-2686-9241
Additional Information:© 2020 The American Astronomical Society. Received 2020 March 2; revised 2020 September 2; accepted 2020 September 16; published 2020 November 2. We thank the anonymous referee for helpful comments. This work was supported in part by NSF grants AST-1518308, and AST-1815034, and NASA grant 16-ADAP16-0232. The work of D.S. was carried out at the Jet Propulsion Laboratory at the California Institute of Technology, under a contract with NASA.Y.T. was funded by JSPS KAKENHI grant No. JP16J05742. Y.T. studied as a Global Relay of Observatories Watching Transients Happen (GROWTH) intern at Caltech during the summer and fall of 2017. GROWTH is funded by the National Science Foundation under Partnerships for International Research and Education grant No. 1545949. N.K. acknowledges the support by MEXT Kakenhi grant No. 17H06362 and the JPSP PIRE program.
Group:Astronomy Department
Funders:
Funding AgencyGrant Number
NSFAST-1518308
NSFAST-1815034
NASA16-ADAP16-0232
NASA/JPL/CaltechUNSPECIFIED
Japan Society for the Promotion of Science (JSPS)JP16J05742
NSFOISE-1545949
Ministry of Education, Culture, Sports, Science and Technology (MEXT)17H06362
Subject Keywords:Galaxy accretion disks ; Active galaxies ; Astrostatistics ; Neural networks
Issue or Number:1
Classification Code:Unified Astronomy Thesaurus concepts: Galaxy accretion disks (562); Active galaxies (17); Astrostatistics (1882); Neural networks (1933)
Record Number:CaltechAUTHORS:20200414-070718405
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200414-070718405
Official Citation:Yutaro Tachibana et al 2020 ApJ 903 54
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:102515
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:14 Apr 2020 16:47
Last Modified:04 Nov 2020 20:58

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