Full-shape cosmology analysis of the SDSS-III BOSS galaxy power spectrum using an emulator-based halo model: A 5% determination of σ8

We present the results obtained from the full-shape cosmology analysis of the redshift-space power spectra for four galaxy samples of the SDSS-III BOSS DR12 galaxy catalog over 0.2 < z < 0.75. For the theoretical template, we use an emulator that was built from an ensemble set of N-body simulations, which enables fast and accurate computation of the redshift-space power spectrum of "halos." Combining with the halo occupation distribution to model the galaxy-halo connection, we can compute the redshift-space power spectrum of BOSS-like galaxies in less than a CPU second, for an input model under flat Lambda CDM cosmology. In our cosmology inference, we use the monopole, quadrupole, and hexadecapole moments of the redshift-space power spectrum and include seven nuisance parameters, with broad priors, to model uncertainties in the galaxy-halo connection for each galaxy sample, but do not use any information on the abundance of galaxies. We demonstrate a validation of our analysis pipeline using the mock catalogs of BOSS-like galaxies, generated using different recipes of the galaxy-halo connection and including the assembly bias effect. Assuming weak priors on cosmological parameters, except for the BBN prior on Omega(b)h(2) and the CMB prior on n(s), we show that our model well reproduces the BOSS power spectra. Including the power-spectrum information up to k(m)(ax) = 0.25h Mpc(-1), we find Omega(m) = 0.301(-0.011)(+0.012), H-0 = 68.2 +/- 1.4 km s(-1) Mpc(-1), and sigma(8) = 0.786(-0.037)(+0.036) for the mode and 68% credible interval, after marginalization over galaxy-halo connection parameters. We find little improvement in the cosmological parameters beyond a maximum wavelength k(max) similar or equal to 0.2h Mpc(-1) due to the shot noise domination and marginalization of the galaxy-halo connection parameters. Our results are consistent with the Planck CMB results within 1 sigma statistical uncertainties.