The statistical power of Lyman-α 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-α 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.
@article{arxiv.1912.02763,
title = {LyaCoLoRe: Synthetic Datasets for Current and Future Lyman-${\alpha}$ Forest BAO Surveys},
author = {James Farr and Andreu Font-Ribera and Hélion du Mas des Bourboux and Andrea Muñoz-Gutiérrez and Francisco Javier Sanchez Lopez and Andrew Pontzen and Alma Xochitl González-Morales and David Alonso and David Brooks and Peter Doel and Thomas Etourneau and Julien Guy and Jean-Marc Le Goff and Axel de al Macorra and Nathalie Palanque-Delabrouille and Ignasi Pérez-Ràfols and James Rich and Anže Slosar and Gregory Tarle and Duan Yutong and Kai Zhang},
journal= {arXiv preprint arXiv:1912.02763},
year = {2020}
}