Modeling of the High Column Density systems in The Lyman-Alpha Forest

TL;DR

This paper develops a physically motivated model to account for high-column-density systems in the Lyman-alpha forest, improving the accuracy of cosmological parameter extraction from quasar spectra.

Contribution

The authors introduce a model based on the distribution and profiles of HCDs that accurately describes their impact on the Lyman-alpha forest power spectrum.

Findings

Model matches the accuracy of existing ad-hoc models

Effectively describes the scale-dependent bias caused by HCDs

Applicable to mock data with known HCD content

Abstract

The Lyman-$\alpha$ forests observed in the spectra of high-redshift quasars can be used as a tracer of the cosmological matter density to study baryon acoustic oscillations (BAO) and the Alcock-Paczynski effect. Extraction of cosmological information from these studies requires modeling of the forest correlations. While the models depend most importantly on the bias parameters of the intergalactic medium (IGM), they also depend on the numbers and characteristics of high-column-density systems (HCDs) ranging from Lyman-limit systems with column densities $\log_{10}\!\bigl(N_{\mathrm{HI}}/\mathrm{cm}^{-2}\bigr)>17$ to damped Lyman-$\alpha$ systems (DLAs) with $\log_{10}\!\bigl(N_{\mathrm{HI}}/\mathrm{cm}^{-2}\bigr)>20.2$. These HCDs introduce broad damped absorption characteristic of a Voigt profile. Consequently they imprint a component on the power spectrum whose modes in the radial direction are suppressed, leading to a scale-dependent bias. Using mock data sets of known HCD content, we test a model that describes this effect in terms of the distribution of column densities of HCDs, the Fourier transforms of their Voigt profiles and the bias of the halos containing the HCDs. Our results show that this physically well-motivated model describes the effects of HCDs with an accuracy comparable to that of the ad-hoc models used in published forest analyses. We also discuss the problems of applying the model to real data, where the HCD content and their bias is uncertain.