Probing Primordial Features with the Stochastic Gravitational Wave Background

TL;DR

This paper explores the potential of future space-based gravitational wave detectors, like LISA, to identify primordial features from inflation through the stochastic gravitational wave background, highlighting the importance of spectral templates.

Contribution

It introduces a detailed analysis of primordial feature signals in the SGWB, including the development of spectral templates for data analysis and the exploration of inflationary models producing these features.

Findings

Primordial features can produce detectable oscillatory signals in the SGWB.

Spectral templates can effectively distinguish different inflationary physics.

Fine-tuning is required for signals to be observable within experimental sensitivity.

Abstract

The stochastic gravitational wave background (SGWB) offers a new opportunity to observe signals of primordial features from inflationary models. We study their detectability with future space-based gravitational waves experiments, focusing our analysis on the frequency range of the LISA mission. We compute gravitational wave spectra from primordial features by exploring the parameter space of a two-field inflation model capable of generating different classes of features. Fine-tuning in scales and amplitudes is necessary for these signals to fall in the observational windows. Once they show up, several classes of frequency-dependent oscillatory signals, characteristic of different underlying inflationary physics, may be distinguished and the SGWB provides a window on dynamics of the primordial universe independent of cosmic microwave background and large-scale structure. To connect with future experimental data, we discuss two approaches of how the results may be applied to data analyses. First, we discuss the possibility of reconstructing the signal with LISA, which requires a high signal-to-noise ratio. The second more sensitive approach is to apply templates representing the spectra as estimators. For the latter purpose, we derive templates that can accurately capture the spectral features of several classes of feature signals and compare them with the SGWB produced by other physical mechanisms.