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

TL;DR

This paper introduces a rapid, flexible inference framework for continuum reverberation mapping of active galactic nuclei using simulation-based inference with deep learning, significantly improving speed and adaptability over traditional methods.

Contribution

It develops a novel SBI-based approach employing LSTM networks and neural density estimators for efficient SMBH parameter estimation from AGN light curves, suitable for large upcoming surveys.

Findings

SBI achieves 10^3-10^5 times faster inference than traditional methods.

The framework accurately estimates SMBH parameters from simulated light curves.

It demonstrates robustness with irregular sampling and varying variability characteristics.

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.