Fully probabilistic quasar continua predictions near Lyman-α with conditional neural spline flows

Code

• github.com/davidreiman/spectre
  OfficialIn paperpytorch★ 6
• github.com/jtamanas/mssm
  jax★ 1

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- (Ly), a challenging task given the highly variable Ly 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\ xC5 _rest < 1290\ xC5) conditional on the red-side spectrum (1290\ xC5 _rest < 2900\ 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>7 quasars and estimate the volume-averaged neutral fraction of hydrogen from each, finding x_HI=0.304 0.042 for ULAS J1120+0641 (z=7.09) and x_HI=0.384 0.133 for ULAS J1342+0928 (z=7.54).

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