# Simulating the effect of high column density absorbers on the one-dimensional Lyman-alpha forest flux power spectrum

## TL;DR

This paper investigates how high column density hydrogen absorbers affect the 1D Lyman-alpha forest flux power spectrum using hydrodynamical simulations, providing models to improve cosmological analyses.

## Contribution

It introduces the first simulation-based analysis of high column density absorbers' impact on the Lyman-alpha flux power spectrum and offers templates for residual contamination modeling.

## Key findings

- High column density absorbers bias the flux power spectrum through damping wings.
- Provided templates as a function of column density and redshift for modeling.
- Fitted functions enable straightforward incorporation into data analysis pipelines.

## Abstract

We measure the effect of high column density absorbing systems of neutral hydrogen (HI) on the one-dimensional (1D) Lyman-alpha forest flux power spectrum using cosmological hydrodynamical simulations from the Illustris project. High column density absorbers (which we define to be those with HI column densities $N(\mathrm{HI}) > 1.6 \times 10^{17}\,\mathrm{atoms}\,\mathrm{cm}^{-2}$) cause broadened absorption lines with characteristic damping wings. These damping wings bias the 1D Lyman-alpha forest flux power spectrum by causing absorption in quasar spectra away from the location of the absorber itself. We investigate the effect of high column density absorbers on the Lyman-alpha forest using hydrodynamical simulations for the first time. We provide templates as a function of column density and redshift, allowing the flexibility to accurately model residual contamination, i.e., if an analysis selectively clips out the largest damping wings. This flexibility will improve cosmological parameter estimation, e.g., allowing more accurate measurement of the shape of the power spectrum, with implications for cosmological models containing massive neutrinos or a running of the spectral index. We provide fitting functions to reproduce these results so that they can be incorporated straightforwardly into a data analysis pipeline.