lss-montepython (deprecated)

NB: The latest full-shape likelihoods can be found here

Contributors: Mikhail (Misha) Ivanov, Anton Chudaykin, Oliver Philcox

These are various large-scale structure likelihoods for the MCMC sampler Montepython that were analyzed in the papers

• Measuring neutrino masses with large-scale structure: Euclid forecast with controlled theoretical error
• Cosmological Parameters from the BOSS Galaxy Power Spectrum
• Cosmological Parameters and Neutrino Masses from the Final Planck and Full-Shape BOSS Data
• Combining Full-Shape and BAO Analyses of Galaxy Power Spectra: A 1.6% CMB-independent constraint on H0

The repo includes:

• Mock power spectrum and bispectrum likelihoods for a Euclid-like survey spanning over eight redshift bins from z=0.6 to z=2 with one-loop or two-loop theoretical error covariances
• Custom-built BOSS DR12 pre-reconstructed full-shape (FS) power spectrum likelihods for four independent data chunks: North and South Galactic Caps (NGC and SGC) at z=0.38 and z=0.61
• BAO-only likelihood that extracts the anisotropic BAO signal from the post-reconstructed power spectra
• Joint FS+BAO likelihoods with the appropriate covariance matrices for the same data chunks

Note that you need CLASS-PT to evaluate FS and FS+BAO likelihoods. We recommend using the '_marg' likelihoods, which include exact analytic marginalization over Gaussian parameters and significant optimizations.

Update from 11/25/2020: minor typos in the window function treatement have been corrected; the power spectra and covariance matrices are replaced by the new measurements carried out in the project Cosmological constraints from BOSS with analytic covariance matrices