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arXiv · 2206.00731

Constraining CMB physical processes using Planck 2018 data

Abstract

This paper aims to perform a phenomenological parametrisation of the standard cosmological model, $\Lambda$CDM, to weigh the different physical processes that define the pattern of the angular power spectra of the Cosmic Microwave Background (CMB) anisotropies. We use six phenomenological amplitudes to account for the Sachs-Wolfe, early and late Integrated Sachs-Wolfe, polarization contribution, Doppler and lensing effects. To do so, we have adapted the CLASS Boltzmann code and used the Markov Chain Monte Carlo (MCMC) sampler of Cobaya to explore the Planck 2018 likelihood to constrain different combinations of cosmological and phenomenological parameters. Observing deviations of the mean values of the phenomenological amplitudes from the predictions of the $\Lambda$CDM model could be useful to resolve existing cosmological tensions. For the first time, a comprehensive analysis of the physical processes of the CMB has been performed using the Planck 2018 temperature, polarization and lensing power spectra. In a previous work, the phenomenological amplitudes were constrained using only the TT data, however, by including the polarization and lensing data we find that the constraints on these physical contributions are tighter. In addition, some degeneracies that appear only when considering TT data are completely broken by taking into account all Planck 2018 data. Consequently, models with more than three phenomenological amplitudes can be studied, which is prohibitive when only the temperature power spectrum is used. The results presented in this paper show that the Planck experiment can constrain all phenomenological amplitudes except the late Integrated Sachs-Wolfe effect. No inconsistencies were found with the $\Lambda$CDM model, and the largest improvements were obtained for the models that include the lensing parameter, $A_L$.

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BibTeXRIS

M. Ruiz-Granda, P. Vielva. 2022-06-01. Constraining CMB physical processes using Planck 2018 data. https://doi.org/10.1088/1475-7516%2F2022%2F11%2F043

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