Kavli Affiliate: Philip J. Marshall
| First 5 Authors: Ji Won Park, Ashley Villar, Yin Li, Yan-Fei Jiang, Shirley Ho
| Summary:
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.
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