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Article overview
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Accurate halo-galaxy mocks from automatic bias estimation and particle mesh gravity solvers | Mohammadjavad Vakili
; Francisco-Shu Kitaura
; Yu Feng
; Gustavo Yepes
; Cheng Zhao
; Chia-Hsun Chuang
; ChangHoon Hahn
; | Date: |
13 Jan 2017 | Abstract: | Reliable extraction of cosmological information from clustering measurements
of galaxy surveys requires estimation of the error covariance matrices of
observables. The accuracy of covariance matrices is limited by our ability to
generate sufficiently large number of independent mock catalogs that can
describe the physics of galaxy clustering across a wide range of scales.
Furthermore, galaxy mock catalogs are required to study systematics in galaxy
surveys and to test analysis tools. In this investigation, we present a fast
and accurate approach for generation of mock catalogs for the upcoming galaxy
surveys. Our method relies on low-resolution approximate gravity solvers to
simulate the large scale dark matter field, which we then populate with halos
according to a flexible nonlinear and stochastic bias model. In particular, we
extend the extsc{patchy} code with an efficient particle mesh algorithm to
simulate the dark matter field (the extsc{FastPM} code), and with an
efficient and robust MCMC method relying on the extsc{emcee} code for
constraining the parameters of the bias model. Using the halos in the
BigMultiDark high-resolution $N$-body simulation as a reference catalog, we
demonstrate that our technique can model the bivariate probability distribution
function, power spectrum, and bispectrum of halos in the reference catalog.
Specifically, we show that the new ingredients permit us to reach percentage
accuracy in the power spectrum up to $ksim 0.4; hmathrm{Mpc}^{-1}$ (within
5$\%$ up to $ksim 0.6; hmathrm{Mpc}^{-1}$) with accurate bispectra improving
previous results based on Lagrangian perturbation theory. | Source: | arXiv, 1701.3765 | Services: | Forum | Review | PDF | Favorites |
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