Quasar Factor Analysis -- An Unsupervised and Probabilistic Quasar Continuum Prediction Algorithm with Latent Factor Analysis by Zechang Sun et al. on Wednesday 23 November

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, \textit{Quasar Factor Analysis} (QFA), which combines factor analysis

(FA) with physical priors of the intergalactic medium (IGM) 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, $\sim 2\%$ relative error, for continuum

prediction in the Ly$\alpha$ forest region compared to previous methods. We

further fit 90,678 $2<\mathrm{z}<3.5$, SNR$>2$ quasar spectra from Sloan

Digital Sky Survey Data Release 16 and found that for $\sim 30\%$ quasar

spectra where the continua were ill-determined with previous methods, QFA

yields visually more plausible continua. QFA also attains $\lesssim 1\%$ error

in the 1D Ly$\alpha$ power spectrum measurements at $\mathrm{z}\sim 3$ and

$\sim 4\%$ in $\mathrm{z}\sim 2.4$. In addition, QFA determines latent factors

representing more physically motivated than PCA. 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 Ly$\alpha$ systems and

potential Type II quasar spectra.

arXiv: http://arxiv.org/abs/http://arxiv.org/abs/2211.11784v1