Reconstructing inflationary features on large scales using genetic algorithm

Abstract

[Abridged] A variety of model-dependent as well as model-independent approaches suggest that certain localized features in the primordial scalar power spectrum can lead to a significantly better fit to the observed anisotropies in the cosmic microwave background (CMB). In this work, we focus on three types of such features and examine whether these features can be generated in inflationary scenarios driven by a single, canonical scalar field. We consider a slowly rolling baseline model that is described by a specific time-dependence of the first slow roll parameter and we generate the desired features in the power spectrum through suitable modifications to the functional form of the slow roll parameter. To systematically reconstruct the desired features in the scalar power spectrum (or, equivalently, the modifications in the behavior of the first slow roll parameter) that are consistent with the data, we implement a machine learning pipeline based on the genetic algorithm (GA). Assuming the standard values for the background $\Lambda$CDM model (arrived at for a nearly scale-invariant primordial scalar power spectrum), we apply our method to the Planck 2018 CMB data, and show that the reconstructed features improve the fit to the observed angular power spectra by $\Delta \chi^2 \lesssim -10$. Moreover, we find that GA points to other sets of background parameters and primordial features, which lead to a similar level of improvement in the fit to the data. Such alternative sets of background parameters and scalar power spectra offer possible pathways to alleviate existing cosmological tensions. Our approach provides effective single-field inflationary dynamics to generate features that are supported by the data.