Non-linear perturbation theory extension of the Boltzmann code CLASS

Abstract

We present a new open-source code that calculates one-loop power auto- and cross-power spectra for matter fields and biased tracers in real and redshift space. These spectra incorporate all ingredients required for a direct application to data: non-linear bias, redshift-space distortions, infra-red resummation, counterterms, and the Alcock-Paczynski effect. Our code is based on the Boltzmann solver CLASS and inherits its advantage: user friendliness, ease of modification, high speed, and simple interface with other software. We present detailed descriptions of the theoretical model, the code structure, approximations, and accuracy tests. A typical end-to-end run for one cosmology takes $\sim 0.3$ seconds, which is sufficient for Markov Chain Monte Carlo parameter extraction. As an example, we apply the code to data from the Baryon Oscillation Spectroscopic Survey (BOSS) and infer cosmological parameters from the shape of the galaxy power spectrum.

v1: 48 pages, 9 figures. v2: 56 pages, 14 figures, minor edits, version to appear in PRD. The code and custom-built BOSS likelihoods are available at https://github.com/Michalychforever/CLASS-PT and https://github.com/Michalychforever/lss_montepython

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• Redshift-Space Distortions in Lagrangian Perturbation Theory
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• Constraints on the curvature of the Universe and dynamical dark energy from the Full-shape and BAO data
• Cosmological constraints from BOSS with analytic covariance matrices
• Precision analysis of the redshift-space galaxy bispectrum
• Cosmology with the EFTofLSS and BOSS: dark energy constraints and a note on priors
• ShapeFit: extracting the power spectrum shape information in galaxy surveys beyond BAO and RSD
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• BAO+BBN revisited -- Growing the Hubble tension with a 0.7km/s/Mpc constraint
• Modeling Galaxies in Redshift Space at the Field Level
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• Optimizing large-scale structure data analysis with the theoretical error likelihood
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