Fast and Flexible Inference Framework for Continuum Reverberation Mapping Using Simulation-based Inference with Deep Learning

Journal Article · 16 December 2024 · The Astrophysical Journal

DOI: https://doi.org/10.3847/1538-4357/ad900d · OSTI ID:2481634

Li, Jennifer I-Hsiu ; Johnson, Sean D. ; Avestruz, Camille ; Jarugula, Sreevani ; Shen, Yue ; Kesler, Elise ; Liu, Zhuoqi (Will) ; Mishra, Nishant

Abstract Continuum reverberation mapping (CRM) of active galactic nuclei (AGN) monitors multiwavelength variability signatures to constrain accretion disk structure and supermassive black hole (SMBH) properties. The upcoming Vera Rubin Observatory’s Legacy Survey of Space and Time will survey tens of millions of AGN over the next decade, with thousands of AGN monitored with almost daily cadence in the deep drilling fields. However, existing CRM methodologies often require long computation time and are not designed to handle such large amounts of data. In this paper, we present a fast and flexible inference framework for CRM using simulation-based inference (SBI) with deep learning to estimate SMBH properties from AGN light curves. We use a long short-term memory summary network to reduce the high dimensionality of the light curve data and then use a neural density estimator to estimate the posterior of SMBH parameters. Using simulated light curves, we find SBI can produce more accurate SMBH parameter estimation with 10 ³ –10 ⁵ times speed up in inference efficiency compared to traditional methods. The SBI framework is particularly suitable for wide-field CRM surveys as the light curves will have identical observing patterns, which can be incorporated into the SBI simulation. We explore the performance of our SBI model on light curves with irregular-sampled, realistic observing cadence and alternative variability characteristics to demonstrate the flexibility and limitation of the SBI framework.

• Research Organization: Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States); Illinois U., Urbana (main); Illinois U., Urbana, Astron. Dept.; Michigan U.; U. Michigan, Ann Arbor; U. Michigan, Dept. Astron.
• Sponsoring Organization: US Department of Energy; USDOE
• Grant/Contract Number: 89243024CSC000002; AC02-07CH11359
• OSTI ID: 2481634
• Report Number(s): FERMILAB-PUB-24-1021-CSAID
• Journal Information: The Astrophysical Journal, Journal Name: The Astrophysical Journal Journal Issue: 2 Vol. 977; ISSN 0004-637X
• Publisher: American Astronomical SocietyCopyright Statement
• Country of Publication: United States
• Language: English