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

• DOI: 10.48550/arxiv.2106.01450
• Publication Date: 2021-01-01
• Authors: Ji Won Park; A. Villar; Yin Li; Yan-Fei Jiang; Shirley Ho; Joshua Lin; Philip J. Marshall; A. Roodman
• Keywords: Black hole (networking)

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