Enhancing BOSS bispectrum cosmological constraints with maximal compression
Enhancing BOSS bispectrum cosmological constraints with maximal compression
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DOI：
10.1093/mnras/stz051
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发表时间：
2018-06
影响因子：
4.8
通讯作者：
D. Gualdi;H. Gil-Marín;R. Schuhmann;M. Manera;B. Joachimi;O. Lahav
中科院分区：
文献类型：
--
作者：
D. Gualdi;H. Gil-Marín;R. Schuhmann;M. Manera;B. Joachimi;O. Lahav
We apply two compression methods to the galaxy power spectrum monopole/quadrupole and bispectrum monopole measurements from the Baryon Oscillation Spectroscopic Survey DR12 CMASS sample. Both methods reduce the dimension of the original data vector to the number of cosmological parameters considered, using the Karhunen–Loeve (KL) algorithm with an analytic covariance model. In the first case, we infer the posterior through Markov chain Monte Carlo (MCMC) sampling from the likelihood of the compressed data vector (MC–KL). The second, faster option, works by first Gaussianizing and then orthogonalizing the parameter space before the compression; in this option (G-PCA) we only need to run a low-resolution preliminary MCMC sample for the Gaussianization to compute our posterior. Both compression methods accurately reproduce the posterior distributions obtained by standard MCMC sampling on the CMASS data set for a k-space range of |$0.03\text{--}0.12\, h\, \mathrm{Mpc}^{-1}$|. The compression enables us to increase the number of bispectrum measurements by a factor of ∼23 over the standard binning (from 116 to 2734 triangle bins used), which is otherwise limited by the number of mock catalogues available. This reduces the |$68{{\ \rm per\ cent}}$| credible intervals for the parameters (b_1, b_2, f, σ_8) by (|$-24.8{{\ \rm per\ cent}},-52.8{{\ \rm per\ cent}},-26.4{{\ \rm per\ cent}},-21{{\ \rm per\ cent}}$|), respectively. Using these methods for future redshift surveys such as DESI, Euclid, and PFS will drastically reduce the number of simulations needed to compute accurate covariance matrices and will facilitate tighter constraints on cosmological parameters.
We apply two compression methods to the galaxy power spectrum monopole/quadrupole and bispectrum monopole measurements from the Baryon Oscillation Spectroscopic Survey DR12 CMASS sample. Both methods reduce the dimension of the original data vector to the number of cosmological parameters considered, using the Karhunen–Loeve (KL) algorithm with an analytic covariance model. In the first case, we infer the posterior through Markov chain Monte Carlo (MCMC) sampling from the likelihood of the compressed data vector (MC–KL). The second, faster option, works by first Gaussianizing and then orthogonalizing the parameter space before the compression; in this option (G-PCA) we only need to run a low-resolution preliminary MCMC sample for the Gaussianization to compute our posterior. Both compression methods accurately reproduce the posterior distributions obtained by standard MCMC sampling on the CMASS data set for a k-space range of |$0.03\text{--}0.12\, h\, \mathrm{Mpc}^{-1}$|. The compression enables us to increase the number of bispectrum measurements by a factor of ∼23 over the standard binning (from 116 to 2734 triangle bins used), which is otherwise limited by the number of mock catalogues available. This reduces the |$68{{\ \rm per\ cent}}$| credible intervals for the parameters (b_1, b_2, f, σ_8) by (|$-24.8{{\ \rm per\ cent}},-52.8{{\ \rm per\ cent}},-26.4{{\ \rm per\ cent}},-21{{\ \rm per\ cent}}$|), respectively. Using these methods for future redshift surveys such as DESI, Euclid, and PFS will drastically reduce the number of simulations needed to compute accurate covariance matrices and will facilitate tighter constraints on cosmological parameters.