LyaCoLoRe: Synthetic Datasets for Current and Future Lyman-${\alpha}$ Forest BAO Surveys

TL;DR

LyaCoLoRe is a fast, flexible tool for generating synthetic Lyman-alpha forest datasets to validate BAO analysis pipelines and assess systematic effects in upcoming large-scale surveys.

Contribution

The paper introduces LyaCoLoRe, a new package for creating realistic mock Lyman-alpha forest datasets for BAO studies, including methods to incorporate astrophysical effects.

Findings

Successfully recovers the BAO signal in simulated data.

Measures large-scale bias parameters consistent with literature.

Demonstrates the ability to add astrophysical effects like high column density systems.

Abstract

The statistical power of Lyman-${\alpha}$ forest Baryon Acoustic Oscillation (BAO) measurements is set to increase significantly in the coming years as new instruments such as the Dark Energy Spectroscopic Instrument deliver progressively more constraining data. Generating mock datasets for such measurements will be important for validating analysis pipelines and evaluating the effects of systematics. With such studies in mind, we present LyaCoLoRe: a package for producing synthetic Lyman-${\alpha}$ forest survey datasets for BAO analyses. LyaCoLoRe transforms initial Gaussian random field skewers into skewers of transmitted flux fraction via a number of fast approximations. In this work we explain the methods of producing mock datasets used in LyaCoLoRe, and then measure correlation functions on a suite of realisations of such data. We demonstrate that we are able to recover the correct BAO signal, as well as large-scale bias parameters similar to literature values. Finally, we briefly describe methods to add further astrophysical effects to our skewers - high column density systems and metal absorbers - which act as potential complications for BAO analyses.