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

JI Li, SD Johnson, C Avestruz, S Jarugula… - arXiv preprint arXiv …, 2024 - arxiv.org
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… 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 …

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BibTeX

@article{2407.14621v1,
Author        = {Jennifer I-Hsiu Li and Sean D. Johnson and Camille Avestruz and Sreevani Jarugula and Yue Shen and Elise Kesler and Zhuoqi Will Liu and Nishant Mishra},
Title         = {Fast and Flexible Inference Framework for Continuum Reverberation
  Mapping using Simulation-based Inference with Deep Learning},
Eprint        = {2407.14621v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.GA},
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 (LSST) 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 amount 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 (LSTM) 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 RM 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.},
Year          = {2024},
Month         = {Jul},
Url           = {http://arxiv.org/abs/2407.14621v1},
File          = {2407.14621v1.pdf}
}

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