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

06/02/2021
by   Ji-won Park, et al.
9

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.

READ FULL TEXT

Authors

page 1

page 2

page 3

page 4

11/30/2020

AGNet: Weighing Black Holes with Machine Learning

Supermassive black holes (SMBHs) are ubiquitously found at the centers o...
09/17/2021

TS-MULE: Local Interpretable Model-Agnostic Explanations for Time Series Forecast Models

Time series forecasting is a demanding task ranging from weather to fail...
08/17/2021

AGNet: Weighing Black Holes with Deep Learning

Supermassive black holes (SMBHs) are ubiquitously found at the centers o...
11/26/2018

Bayesian Nonparametric Analysis of Multivariate Time Series: A Matrix Gamma Process Approach

While there is an increasing amount of literature about Bayesian time se...
06/14/2016

A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification

We present a general framework for classification of sparse and irregula...
06/16/2022

MAGIC: Microlensing Analysis Guided by Intelligent Computation

The modeling of binary microlensing light curves via the standard sampli...
05/04/2018

Using Quantum Mechanics to Cluster Time Series

In this article we present a method by which we can reduce a time series...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.