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Modeling quasar spectra is a fundamental task in astrophysics as quasars arethe tell-tale sign of cosmic evolution. We introduce a novel unsupervisedlearning algorithm, Quasar Factor Analysis (QFA), for recovering the intrinsicquasar continua from noisy quasar spectra. QFA assumes that the Ly$\alpha$forest can be approximated as a Gaussian process, and the continuum can be welldescribed as a latent factor model. We show that QFA can learn, throughunsupervised learning and directly from the quasar spectra, the quasar continuaand Ly$\alpha$ forest simultaneously. Compared to previous methods, QFAachieves state-of-the-art performance for quasar continuum prediction robustlybut without the need for predefined training continua. In addition, thegenerative and probabilistic nature of QFA paves the way to understanding theevolution of black holes as well as performing out-of-distribution detectionand other Bayesian downstream inferences.

An Unsupervised Learning Approach for Quasar Continuum Prediction