PRISM: Recovery of the primordial spectrum from Planck data
F. Lanusse, P. Paykari, J.-L. Starck, F. Sureau, J. Bobin, A. Rassat

TL;DR
This paper introduces PRISM, a sparsity-based algorithm to recover the primordial power spectrum from Planck CMB data, capable of detecting features and deviations from scale invariance without strong priors.
Contribution
The paper presents PRISM, a novel non-parametric, sparsity-driven method for reconstructing the primordial power spectrum from CMB data, allowing detection of features and deviations.
Findings
PRISM accurately recovers the primordial spectrum from simulated Planck data.
No significant deviations from the scale-invariant spectrum were found in Planck PR1 data.
PRISM can detect localized features in the primordial spectrum.
Abstract
The primordial power spectrum describes the initial perturbations that seeded the large-scale structure we observe today. It provides an indirect probe of inflation or other structure-formation mechanisms. In this letter, we recover the primordial power spectrum from the Planck PR1 dataset, using our recently published algorithm PRISM. PRISM is a sparsity-based inversion method, that aims at recovering features in the primordial power spectrum from the empirical power spectrum of the cosmic microwave background (CMB). This ill-posed inverse problem is regularised using a sparsity prior on features in the primordial power spectrum in a wavelet dictionary. Although this non-parametric method does not assume a strong prior on the shape of the primordial power spectrum, it is able to recover both its general shape and localised features. As a results, this approach presents a reliable way…
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