A Generative Model for Quasar Spectra

Abstract

We build a multi-output generative model for quasar spectra and the properties of their black hole engines, based on a Gaussian process latent-variable model. This model treats every quasar as a vector of latent properties such that the spectrum and all physical properties of the quasar are associated with non-linear functions of those latent parameters; the Gaussian process kernel functions define priors on the function space. Our generative model is trained with a justifiable likelihood function that allows us to treat heteroscedastic noise and missing data correctly, which is crucial for all astrophysical applications. It can predict simultaneously unobserved spectral regions, as well as the physical properties of quasars in held-out test data. We apply the model to rest-frame ultraviolet and optical quasar spectra for which precise black hole masses (based on reverberation mapping measurements) are available. Unlike reverberation-mapping studies, which require multi-epoch data, our model predicts black hole masses from single-epoch spectra, even with limited spectral coverage. We demonstrate the capabilities of the model by predicting black hole masses and unobserved spectral regions. We find that we predict black hole masses at close to the best possible accuracy.

accepted for publication in ApJ

References in corpus (10)

• The NumPy array: a structure for efficient numerical computation
• Evidence of patchy hydrogen reionization from an extreme Ly$\alpha$ trough below redshift six
• The black hole mass - stellar velocity dispersion relation of narrow-line Seyfert 1 galaxies
• The Lick AGN Monitoring Project 2011: Spectroscopic Campaign and Emission-Line Light Curves
• Correcting CIV-Based Virial Black Hole Masses
• Extremely metal-poor gas at a redshift of 7
• IGM damping wing constraints on reionisation from covariance reconstruction of two $z\gtrsim 7$ QSOs
• New Constraints on Quasar Evolution: Broad Line Velocity Shifts over $1.5\lesssim z\lesssim 7.5$
• Data-driven, interpretable photometric redshifts trained on heterogeneous and unrepresentative data
• Automated Measurement of Quasar Redshift with a Gaussian Process