Mock catalogues are a crucial tool in the analysis of galaxy surveys data , both for the accurate computation of covariance matrices , and for the optimisation of analysis methodology and validation of data sets .
In this paper , we present a set of 1800 galaxy mock catalogues designed to match the Dark Energy Survey Year-1 BAO sample ( 25 ) in abundance , observational volume , redshift distribution and uncertainty , and redshift dependent clustering .
The simulated samples were built upon halogen ( 4 ) halo catalogues , based on a 2 LPT density field with an empirical halo bias .
For each of them , a lightcone is constructed by the superposition of snapshots in the redshift range 0.45 < z < 1.4 .
Uncertainties introduced by so-called photometric redshifts estimators were modelled with a double-skewed-Gaussian curve fitted to the data .
We populate halos with galaxies by introducing a hybrid Halo Occupation Distribution - Halo Abundance Matching model with two free parameters .
These are adjusted to achieve a galaxy bias evolution b ( z _ { ph } ) that matches the data at the 1- \sigma level in the range 0.6 < z _ { ph } < 1.0 .
We further analyse the galaxy mock catalogues and compare their clustering to the data using the angular correlation function w ( \theta ) , the comoving transverse separation clustering \xi _ { \mu < 0.8 } ( s _ { \perp } ) and the angular power spectrum C _ { \ell } , finding them in agreement .
This is the first large set of three-dimensional { ra , dec , z } galaxy mock catalogues able to simultaneously accurately reproduce the photometric redshift uncertainties and the galaxy clustering .