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We analyse the broad-range shape of the monopole and quadrupole correlationfunctions of the BOSS Data Release 12 (DR12) CMASS and LOWZ galaxy sample toobtain constraints on the Hubble expansion rate $H(z)$, the angular-diameterdistance $D_A(z)$, the normalised growth rate $f(z)\sigma_8(z)$, and thephysical matter density $\Omega_mh^2$. We adopt wide and flat priors on allmodel parameters in order to ensure the results are those of a `single-probe'galaxy clustering analysis. We also marginalise over three nuisance terms thataccount for potential observational systematics affecting the measuredmonopole. However, such Monte Carlo Markov Chain analysis is computationallyexpensive for advanced theoretical models, thus we develop a new methodology tospeed up our analysis. We obtain $\{D_A(z)r_{s,fid}/r_s$Mpc,$H(z)r_s/r_{s,fid}$kms$^{-1}$Mpc$^{-1}$, $f(z)\sigma_8(z)$, $\Omega_m h^2\}$ =$\{956\pm28$ , $75.0\pm4.0$ , $0.397 \pm 0.073$, $0.143\pm0.017\}$ at $z=0.32$and $\{1421\pm23$, $96.7\pm2.7$ , $0.497 \pm 0.058$, $0.137\pm0.015\}$ at$z=0.59$ where $r_s$ is the comoving sound horizon at the drag epoch and$r_{s,fid}=147.66$Mpc for the fiducial cosmology in this study. In addition, wedivide the galaxy sample into four redshift bins to increase the sensitivity ofredshift evolution. However, we do not find improvements in terms ofconstraining dark energy model parameters. Combining our measurements withPlanck data, we obtain $\Omega_m=0.306\pm0.009$,$H_0=67.9\pm0.7$kms$^{-1}$Mpc$^{-1}$, and $\sigma_8=0.815\pm0.009$ assuming$\Lambda$CDM; $\Omega_k=0.000\pm0.003$ assuming oCDM; $w=-1.01\pm0.06$ assuming$w$CDM; and $w_0=-0.95\pm0.22$ and $w_a=-0.22\pm0.63$ assuming $w_0w_a$CDM. Ourresults show no tension with the flat $\Lambda$CDM cosmological paradigm. Thispaper is part of a set that analyses the final galaxy clustering dataset fromBOSS.

monthly notices of the royal astronomical society

The Clustering of Galaxies in the Completed SDSS-III Baryon Oscillation  Spectroscopic Survey: single-probe measurements from DR12 galaxy clustering  -- towards an accurate model