English

Fully probabilistic quasar continua predictions near Lyman-{\alpha} with conditional neural spline flows

Cosmology and Nongalactic Astrophysics 2020-06-04 v2 Astrophysics of Galaxies Instrumentation and Methods for Astrophysics Machine Learning Machine Learning

Abstract

Measurement of the red damping wing of neutral hydrogen in quasar spectra provides a probe of the epoch of reionization in the early Universe. Such quantification requires precise and unbiased estimates of the intrinsic continua near Lyman-α\alpha (Lyα\alpha), a challenging task given the highly variable Lyα\alpha emission profiles of quasars. Here, we introduce a fully probabilistic approach to intrinsic continua prediction. We frame the problem as a conditional density estimation task and explicitly model the distribution over plausible blue-side continua (1190 \unicodexC5λrest<1290 \unicodexC51190\ \unicode{xC5} \leq \lambda_{\text{rest}} < 1290\ \unicode{xC5}) conditional on the red-side spectrum (1290 \unicodexC5λrest<2900 \unicodexC51290\ \unicode{xC5} \leq \lambda_{\text{rest}} < 2900\ \unicode{xC5}) using normalizing flows. Our approach achieves state-of-the-art precision and accuracy, allows for sampling one thousand plausible continua in less than a tenth of a second, and can natively provide confidence intervals on the blue-side continua via Monte Carlo sampling. We measure the damping wing effect in two z>7z>7 quasars and estimate the volume-averaged neutral fraction of hydrogen from each, finding xˉHI=0.304±0.042\bar{x}_\text{HI}=0.304 \pm 0.042 for ULAS J1120+0641 (z=7.09z=7.09) and xˉHI=0.384±0.133\bar{x}_\text{HI}=0.384 \pm 0.133 for ULAS J1342+0928 (z=7.54z=7.54).

Keywords

Cite

@article{arxiv.2006.00615,
  title  = {Fully probabilistic quasar continua predictions near Lyman-{\alpha} with conditional neural spline flows},
  author = {David M. Reiman and John Tamanas and J. Xavier Prochaska and Dominika Ďurovčíková},
  journal= {arXiv preprint arXiv:2006.00615},
  year   = {2020}
}

Comments

19 pages, 16 figures, 2 tables, submitted to MNRAS, code available at https://github.com/davidreiman/spectre