Dark Energy Survey Year 1 Results: galaxy mock catalogues for BAO

TL;DR

This paper presents 1800 galaxy mock catalogues for the Dark Energy Survey Year-1 BAO analysis, enabling improved covariance estimation and methodology validation through detailed clustering comparisons.

Contribution

The creation of a large set of galaxy mock catalogues with realistic properties and a novel hybrid HOD-HAM model for bias evolution matching data at 1-sigma.

Findings

Mock catalogues accurately reproduce observed clustering statistics.

The hybrid HOD-HAM model effectively matches galaxy bias evolution.

Clustering analyses validate the mock catalogues against real data.

Abstract

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 (Crocce et al. 2017) in abundance, observational volume, redshift distribution and uncertainty, and redshift dependent clustering. The simulated samples were built upon HALOGEN (Avila et al. 2015) halo catalogues, based on a $2LPT$ density field with an exponential 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 \textit{double-skewed-Gaussian} curve fitted to the data. We also introduce a hybrid HOD-HAM model with two free parameters that are adjusted to achieve a galaxy bias evolution $b(z_{\rm ph})$ that matches the data at the 1-$\sigma$ level in the range $0.6<z_{\rm ph}<1.0$. 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 $C_\ell$.