Description
Inflationary models involving a canonical, slowly rolling, scalar field
predict a nearly scale-invariant and featureless scalar power spectrum, which is
remarkably consistent with the observed anisotropies in the cosmic microwave
background (CMB) and the distribution of the large-scale structure. However, a
variety of model-dependent as well as model-independent approaches suggest
that certain localized features in the power spectrum can lead to a significantly
better fit to the CMB data. In this talk, I will present our recent work wherein, guided
by machine learning techniques, we have explored whether such features can
naturally arise in single-field inflation. After a brief introduction to inflation, I will first
describe three classes of features and outline the motivations for considering these
forms. Thereafter, I will introduce the genetic algorithm and describe the manner in
which it can be used to arrive at scalar power spectra containing features that
improve agreement with the recent Planck data. Lastly, I will also discuss how GA
points to other sets of background parameters and primordial features, which lead
to a similar level of improvement in fit to the data. Such alternative sets of
background parameters offer potential pathways to alleviate existing cosmological
tensions. I will conclude with a brief summary of our key findings.
