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arXiv · 2002.04615

New Sensitivity Curves for Gravitational-Wave Signals from Cosmological Phase Transitions

Abstract

Gravitational waves (GWs) from strong first-order phase transitions (SFOPTs) in the early Universe are a prime target for upcoming GW experiments. In this paper, I construct novel peak-integrated sensitivity curves (PISCs) for these experiments, which faithfully represent their projected sensitivities to the GW signal from a cosmological SFOPT by explicitly taking into account the expected shape of the signal. Designed to be a handy tool for phenomenologists and model builders, PISCs allow for a quick and systematic comparison of theoretical predictions with experimental sensitivities, as I illustrate by a large range of examples. PISCs also offer several advantages over the conventional power-law-integrated sensitivity curves (PLISCs); in particular, they directly encode information on the expected signal-to-noise ratio for the GW signal from a SFOPT. I provide semianalytical fit functions for the exact numerical PISCs of LISA, DECIGO, and BBO. In an appendix, I moreover present a detailed review of the strain noise power spectra of a large number of GW experiments. The numerical results for all PISCs, PLISCs, and strain noise power spectra presented in this paper can be downloaded from the Zenodo online repository [https://doi.org/10.5281/zenodo.3689582]. In a companion paper [1909.11356], the concept of PISCs is used to perform an in-depth study of the GW signal from the cosmological phase transition in the real-scalar-singlet extension of the standard model. The PISCs presented in this paper will need to be updated whenever new theoretical results on the expected shape of the signal become available. The PISC approach is therefore suited to be used as a bookkeeping tool to keep track of the theoretical progress in the field.

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BibTeXRIS

Kai Schmitz. 2020-02-11. New Sensitivity Curves for Gravitational-Wave Signals from Cosmological Phase Transitions. https://doi.org/10.1007/jhep01(2021)097

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