Mock galaxy catalogues using the quick particle mesh method

By M. White, J. Tinker, Cameron McBride. 2013

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Author(s): White, M; Tinker, JL; McBride, CK | Abstract: Sophisticated analysis of modern large-scale structure surveys requires mock catalogues. Mock catalogues are used to optimize survey design, test reduction and analysis pipelines, make theoretical predictions for basic observables and propagate errors through complex analysis chains.We present a new method, which we call 'quick particle mesh', for generating many large volume, approximate mock catalogues at low computational cost. The method is based on using rapid, low-resolution particle mesh simulations that accurately reproduce the large-scale dark matter density field. Particles are sampled from the density field based on their local density such that they have N-point statistics nearly equivalent to the haloes resolved in high-resolution simulations, creating a set of mock haloes that can be populated using halo occupation methods to create galaxy mocks for a variety of possible target classes. © 2013 The Authors. Published by Oxford University Press on behalf of the Royal Astronomical Society.

Subfields: Astronomy and Astrophysics, Ecology, Instrumentation

View Paper: https://www.semanticscholar.org/paper/bd046481f8b51bbf94d81f1064d65786e89ebdc8

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Citations in the corpus

A volumetric deep Convolutional Neural Network for simulation of dark matter halo catalogues (2018, background): “…of large scale structure which attempt to create simulations of a satisfactory accuracy at minimal computational cost (Bond & Myers 1996a; Monaco et al. 2013; Tassev et al. 2013; Izard et al. 2016; Feng et al. 2016; Avila et al. 2015; Kitaura et al. 2014; Chuang et al. 2015; White et al. 2014).”
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 (2017, methodology): “…in this paper, we have adjusted the parameters of White et al. (2014) that map local density to halo mass to account for the change in redshift (z ∼ 2.5 rather than z ∼ 0.5 in White et al. 2014), as well as extending this mapping to lower halo masses, as required by the halos occupied by quasars.”
The clustering of galaxies in the completed SDSS-III Baryon Oscillation Spectroscopic Survey: cosmological analysis of the DR12 galaxy sample (2016, methodology): “For this work, we utilized two distinct methods of mock galaxy creation: MultiDark-Patchy (hereafter MD-Patchy; Kitaura et al. 2016) and Quick Particle Mesh (QPM; White et al. 2014).”
The clustering of galaxies in the completed SDSS-III Baryon Oscillation Spectroscopic Survey: Cosmological implications of the configuration-space clustering wedges (2016, methodology): “A covariance matrix derived from a set of 1000 QPMs (White et al. 2014) simulations with a box size of 2.5 h−1 Gpc and an HOD matching the clustering of the CMASS sample was used for all the fits.”
The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: RSD measurement from the power spectrum and bispectrum of the DR12 BOSS galaxies (2016, methodology): “…model, the sound horizon at drag redshift is rs(zd) = 147.66 Mpc. Additionally, we also use the set of 1000 realisations of Quick-Particle-Mesh mocks (hereafter qpm) (White et al. 2014) to test the potential systematics introduced by the collided galaxies in the bispectrum signal (Appendix B).”
The clustering of galaxies in the completed SDSS-III Baryon Oscillation Spectroscopic Survey: observational systematics and baryon acoustic oscillations in the correlation function (2016, methodology): “We use the QPM CMASS NGC mocks and for all tests we use the ξ0,2 covariance matrix determined from 1000 realizations of the fiducial case (i).”
The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: mock galaxy catalogues for the BOSS Final Data Release (2015, methodology): “A new generation of methods that can meet these high requirements have been developed during the past two years, in particular, PATCHY (Kitaura, Yepes & Prada 2014), QPM (White et al. 2014), and EZMOCKS (Chuang et al. 2015a).”