LYACOLORE: synthetic datasets for current and future Lyman-alpha forest BAO surveys
JOURNAL OF COSMOLOGY AND ASTROPARTICLE PHYSICS 2020:3 (2020) 68
Authors:
James Farr, Andreu Font-Ribera, Helion du Mas des Bourboux, Andrea Munoz-Gutierrez, F Javier Sanchez, Andrew Pontzen, Alma Xochitl Gonzalez-Morales, David Alonso, David Brooks, Peter Doel, Thomas Etourneau, Julien Guy, Jean-Marc Le Goff, Axel de la Macorra, Nathalie Palanque-Delabrouille, Ignasi Perez-Rafols, James Rich, ArCie Slosar, Gregory Tarle, Duan Yutong, Kai Zhang
Abstract:
© 2020 IOP Publishing Ltd and Sissa Medialab. 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.
