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The statistical power of Lyman-${\alpha}$ forest Baryon Acoustic Oscillation(BAO) measurements is set to increase significantly in the coming years as newinstruments such as the Dark Energy Spectroscopic Instrument deliverprogressively more constraining data. Generating mock datasets for suchmeasurements will be important for validating analysis pipelines and evaluatingthe effects of systematics. With such studies in mind, we present LyaCoLoRe: apackage for producing synthetic Lyman-${\alpha}$ forest survey datasets for BAOanalyses. LyaCoLoRe transforms initial Gaussian random field skewers intoskewers of transmitted flux fraction via a number of fast approximations. Inthis 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 aslarge-scale bias parameters similar to literature values. Finally, we brieflydescribe methods to add further astrophysical effects to our skewers - highcolumn density systems and metal absorbers - which act as potentialcomplications for BAO analyses.

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