Cosmological parameters from the BOSS galaxy power spectrum
Abstract
We present cosmological parameter measurements from the publicly available Baryon Oscillation Spectroscopic Survey (BOSS) data on anisotropic galaxy clustering in Fourier space. Compared to previous studies, our analysis has two main novel features. First, we use a complete perturbation theory model that properly takes into account the non-linear effects of dark matter clustering, short-scale physics, galaxy bias, redshift-space distortions, and large-scale bulk flows. Second, we employ a Markov-Chain Monte-Carlo technique and consistently reevaluate the full power spectrum likelihood as we scan over different cosmologies. Our baseline analysis assumes minimal ΛCDM, varies the neutrino masses within a reasonably tight range, fixes the primordial power spectrum tilt, and uses the big bang nucleosynthesis prior on the physical baryon density ωb. In this setup, we find the following late-Universe parameters: Hubble constant H0=(67.9± 1.1) km s-1Mpc-1, matter density fraction Ωm=0.295± 0.010, and the mass fluctuation amplitude σ8=0.721± 0.043. These parameters were measured directly from the BOSS data and independently of the Planck cosmic microwave background observations. Scanning over the power spectrum tilt or relaxing the other priors do not significantly alter our main conclusions. Finally, we discuss the information content of the BOSS power spectrum and show that it is dominated by the location of the baryon acoustic oscillations and the power spectrum shape. We argue that the contribution of the Alcock-Paczynski effect is marginal in ΛCDM, but becomes important for non-minimal cosmological models.
- Publication:
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Journal of Cosmology and Astroparticle Physics
- Pub Date:
- May 2020
- DOI:
- 10.1088/1475-7516/2020/05/042
- arXiv:
- arXiv:1909.05277
- Bibcode:
- 2020JCAP...05..042I
- Keywords:
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- Astrophysics - Cosmology and Nongalactic Astrophysics;
- General Relativity and Quantum Cosmology;
- High Energy Physics - Phenomenology
- E-Print:
- 67 pages, 17 figures, some additional explanations and cross-checks added, version published in JCAP, likelihoods available at https://github.com/Michalychforever/lss_montepython