Quasar Factor Analysis—An Unsupervised and Probabilistic Quasar Continuum Prediction Algorithm with Latent Factor Analysis

Abstract

Since their first discovery, quasars have been essential probes of the distant Universe. However, due to our limited knowledge of its nature, predicting the intrinsic quasar continua has bottlenecked their usage. Existing methods of quasar continuum recovery often rely on a limited number of high-quality quasar spectra, which might not capture the full diversity of the quasar population. In this study, we propose an unsupervised probabilistic model, quasar factor analysis (QFA), which combines factor analysis with physical priors of the intergalactic medium to overcome these limitations. QFA captures the posterior distribution of quasar continua through generatively modeling quasar spectra. We demonstrate that QFA can achieve the state-of-the-art performance, ∼2% relative error, for continuum prediction in the Lyα forest region compared to previous methods. We further fit 90,678 2 < z < 3.5, signal-to-noise ratio >2 quasar spectra from Sloan Digital Sky Survey Data Release 16 and found that for ∼30% quasar spectra where the continua were ill-determined with previous methods, QFA yields visually more plausible continua. QFA also attains ≲1% error in the 1D Lyα power spectrum measurements at z ∼ 3 and ∼4% in z ∼ 2.4. In addition, QFA determines latent factors representing more physical motivation than principal component analysis. We investigate the evolution of the latent factors and report no significant redshift or luminosity dependency except for the Baldwin effect. The generative nature of QFA also enables outlier detection robustly; we showed that QFA is effective in selecting outlying quasar spectra, including damped Lyα systems and potential Type II quasar spectra.