Reconstruction of the primordial power spectrum of curvature perturbations using multiple data sets
Paul Hunt, Subir Sarkar

TL;DR
This paper presents a robust, model-independent method using Tikhonov regularisation to reconstruct the primordial power spectrum from multiple cosmological data sets, revealing features and an infrared cutoff consistent with observed anomalies.
Contribution
It introduces a Tikhonov regularisation approach for deconvolving the primordial spectrum, quantifies uncertainties, and applies it to combined data sets to identify features and cutoffs.
Findings
Reconstructed spectrum shows an infrared cutoff at low wavenumbers.
Identified features in the spectrum at specific scales with ~2 sigma significance.
Spectrum is not scale-free, indicating deviations from simple models.
Abstract
Detailed knowledge of the primordial power spectrum of curvature perturbations is essential both in order to elucidate the physical mechanism (`inflation') which generated it, and for estimating the cosmological parameters from observations of the cosmic microwave background and large-scale structure. Hence it ought to be extracted from such data in a model-independent manner, however this is difficult because relevant cosmological observables are given by a convolution of the primordial perturbations with some smoothing kernel which depends on both the assumed world model and the matter content of the universe. Moreover the deconvolution problem is ill-conditioned so a regularisation scheme must be employed to control error propagation. We demonstrate that `Tikhonov regularisation' can robustly reconstruct the primordial spectrum from multiple cosmological data sets, a significant…
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