The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: mock galaxy catalogues for the BOSS Final Data Release

Abstract

We reproduce the galaxy clustering catalogue from the SDSS-III Baryon Oscillation Spectroscopic Survey Final Data Release (BOSS DR11&DR12) with high fidelity on all relevant scales in order to allow a robust analysis of baryon acoustic oscillations and redshift space distortions. We have generated (6000) 12 288 MultiDark PATCHY BOSS (DR11) DR12 light cones corresponding to an effective volume of ∼192 000 [h −1 Gpc] 3 (the largest ever simulated volume), including cosmic evolution in the redshift range from 0.15 to 0.75. The mocks have been calibrated using a reference galaxy catalogue based on the halo abundance matching modelling of the BOSS DR11&DR12 galaxy clustering data and on the data themselves. The production follows three steps. First, we apply the PATCHY code to generate a dark matter field and an object distribution including non-linear stochastic galaxy bias. Secondly, we run the halo/stellar distribution reconstruction HADRON code to assign masses to the various objects. This step uses the mass distribution as a function of local density and non-local indicators (i.e. tidal field tensor eigenvalues and relative halo exclusion separation for massive objects) from the reference simulation applied to the corresponding patchy dark matter and galaxy distribution. Finally, we apply the SUGAR code to build the light cones. The resulting MultiDarkPATCHY mock light cones reproduce the number density, selection function, survey geometry, and in general within 1σ , for arbitrary stellar mass bins, the power spectrum up to k = 0.3 h Mpc −1 , the two-point correlation functions down to a few Mpc scales, and the three-point statistics of the BOSS DR11&DR12 galaxy samples.

The clustering of the SDSS-IV extended Baryon Oscillation Spectroscopic Survey DR14 quasar sample: first measurement of baryon acoustic oscillations between redshift 0.8 and 2.2

“…These choices match the fiducial cosmology adopted for BOSS DR12 analyses (Alam et al 2016). One set of mocks we use, the EZmocks (see Section 5.1), use the cosmology of MultiDark-PATCHY (Kitaura et al 2014(Kitaura et al , 2016 used in previous BOSS analyses. The other set of mocks we use, QPM mocks (see Section 5.2), uses a geometry that matches our fiducial cosmology but with Ων = 0.…”

“…5 and 6, demonstrating that a good match has been produced. The EZmocks use the same initial power spectrum used by the mock catalogues of the final BOSS data release (DR12; Kitaura et al 2016;Alam et al 2016). The fiducial cosmology model is ΛCDM with Ωm=0.307115, h=0.6777, σ8=0.8225, Ω b =0.048206, ns=0.9611 (see Kitaura et al 2016 for details).…”

barry and the BAO model comparison

“…Nonetheless, we note that this is a possible way of extending the analysis and Barry in the future. The MultiDarkPATCHY mocks detailed in Kitaura et al (2016) provide us with a thousand mock simulations for both pre-and post-reconstruction of the BAO signal, for both P (k) and ξ(s). The fiducial cosmology in our analysis of these mocks is the Flat ΛCDM cosmology parameterised by Ωm = 0.31, h0 = 0.676, Ω b = 0.04814, ns = 0.97, which matches that used in Beutler et al (2017a); Ross et al (2017) to make the clustering measurements 3 .…”

The bias of dark matter tracers: assessing the accuracy of mapping techniques

“…The precision of this type of approach in the resulting halo catalogs is only acceptable, according to scientific requirements of forthcoming surveys, until intermediate scales (k ∼ 0.2 − 0.3 h Mpc −1 in Fourier space), see e.g. Kitaura et al 2016;Blot et al 2019;Colavincenzo et al 2018;Lippich et al 2018. Higher precision (∼ 1 − 2%) towards smaller scales beyond k = 1 h Mpc −1 (including the Nyquist frequency) has been only reached by the recently published Bias Assignment Method (BAM hereafter, Balaguera-Antolínez et al 2019a,b).…”

“…Chuang et al 2015b;Kitaura et al 2015;Vakili et al 2017), being able to make large amount of precise mock galaxy catalogues (e.g. Kitaura et al 2016) with very low memory and computational requirements, while BAM has been shown to replicate to percent precision summary statistics of halo catalogs (with approximately same computational time and memory requirements as PATCHY ). It is therefore timely to assess whether these methods can be still applied to the expected galaxy catalogs from Euclid or DESI, probing lower halo mass ranges, or if a change towards non-parametric approaches forecasts better results.…”