Semantic Scholar Open Access 2020 105 sitasi

Probing primordial features with the stochastic gravitational wave background

M. Braglia Xingang Chen D. K. Hazra

Abstrak

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. In some cases the scalar power spectrum can significantly exceed the ns=5 limit in single-field inflation and grow as fast as ns=9.1. 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 construct 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.

Topik & Kata Kunci

Penulis (3)

M

M. Braglia

X

Xingang Chen

D

D. K. Hazra

Format Sitasi

Braglia, M., Chen, X., Hazra, D.K. (2020). Probing primordial features with the stochastic gravitational wave background. https://doi.org/10.1088/1475-7516/2021/03/005

Akses Cepat

Informasi Jurnal
Tahun Terbit
2020
Bahasa
en
Total Sitasi
105×
Sumber Database
Semantic Scholar
DOI
10.1088/1475-7516/2021/03/005
Akses
Open Access ✓