Oscillations in the inflaton potential: Complete numerical treatment and comparison with the recent and forthcoming CMB datasets

TL;DR

This paper performs a detailed numerical analysis of inflationary models with oscillatory features in the scalar power spectrum, comparing their fits to current CMB data and forecasting parameter constraints with future Planck data.

Contribution

It provides a comprehensive numerical treatment of oscillatory inflationary models and compares their data fits, highlighting the axion monodromy model's superior fit over the sinusoidally modulated quadratic potential.

Findings

Axion monodromy model fits CMB data better than standard spectrum.

Sinusoidally modulated quadratic potential does not significantly improve data fit.

Planck data constrains model parameters more effectively than current datasets.

Abstract

Amongst the multitude of inflationary models currently available, models that lead to features in the primordial scalar spectrum are drawing increasing attention, since certain features have been found to provide a better fit to the CMB data than the conventional, nearly scale invariant, primordial spectrum. In this work, we carry out a complete numerical analysis of two models that lead to oscillations over all scales in the scalar power spectrum. We consider the model described by a quadratic potential which is superposed by a sinusoidal modulation and the recently popular axion monodromy model. Since the oscillations continue even on to arc minute scales, in addition to the WMAP data, we also compare the models with the small scale data from ACT. Though, both the models, broadly, result in oscillations in the spectrum, interestingly, we find that, while the monodromy model leads to a considerably better fit to the data in comparison to the standard power law spectrum, the quadratic potential superposed with a sinusoidal modulation does not improve the fit to a similar extent. We also carry out forecasting of the parameters using simulated Planck data for both the models. We show that the Planck mock data performs better in constraining the model parameters as compared to the presently available CMB datasets.