Inferring Black Hole Properties from Astronomical Multivariate Time Series with Bayesian Attentive Neural Processes

TL;DR

This paper introduces a Bayesian neural process-based method to reconstruct and infer black hole properties from multivariate time series of active galactic nuclei, handling irregular sampling and gaps.

Contribution

It presents the first end-to-end probabilistic approach for reconstructing AGN time series and inferring black hole parameters simultaneously.

Findings

Achieved 0.4 dex precision in black hole mass inference

Demonstrated effective reconstruction of irregularly sampled time series

Applied method successfully to a simulated dataset of 11,000 AGN

Abstract

Among the most extreme objects in the Universe, active galactic nuclei (AGN) are luminous centers of galaxies where a black hole feeds on surrounding matter. The variability patterns of the light emitted by an AGN contain information about the physical properties of the underlying black hole. Upcoming telescopes will observe over 100 million AGN in multiple broadband wavelengths, yielding a large sample of multivariate time series with long gaps and irregular sampling. We present a method that reconstructs the AGN time series and simultaneously infers the posterior probability density distribution (PDF) over the physical quantities of the black hole, including its mass and luminosity. We apply this method to a simulated dataset of 11,000 AGN and report precision and accuracy of 0.4 dex and 0.3 dex in the inferred black hole mass. This work is the first to address probabilistic time series reconstruction and parameter inference for AGN in an end-to-end fashion.