Constraining particle physics models with gravitational waves from the early universe

TL;DR

This thesis explores how gravitational wave observations from early universe phase transitions and topological defects can constrain various particle physics models beyond the Standard Model, providing new ways to test these theories.

Contribution

It introduces methods to constrain particle physics models using gravitational wave signals from cosmological phase transitions and defects, with detailed case studies on specific BSM scenarios.

Findings

GW signals constrain parameter space of BSM models

GW spectrum does not distinguish between certain models

GW observations can rule out many parameter points in specific models

Abstract

In this Ph.D. thesis, we study the methods to constrain particle physics models using the stochastic GW imprints from cosmological phase transitions (PTs). Beginning with the theory and background, we describe how the GW background from first-order PTs (FOPTs) and topological defects such as domain walls (DWs) can constrain the model parameter space at upcoming GW observatories. The first BSM scenario involves a flavon FOPT in two ultraviolet-complete models of the Froggatt-Nielsen (FN) mechanism. In both models, for the FN symmetry-breaking scale $v_s = 10^{4-7}\,$GeV, the parameter space is constrained by GWs in upcoming observatories such as BBO, DECIGO, CE, and ET. However, the GW spectrum does not discriminate between the two models. Next, we consider FOPT in the doublet left-right symmetric model (DLRSM) during $SU(2)_R\times U(1)_{B-L}$ breaking. For the breaking scale $v_R=20,\,30,\,50\,$TeV, the parameter space can be constrained by GW observations at BBO, FP-DECIGO, and Ultimate DECIGO. A large number of points with detectable GW signals can be ruled out from the precise measurement of the trilinear Higgs coupling at future colliders. Finally, we discuss the GW spectrum generated by DWs formed after the spontaneous breaking of the discrete $\mathcal{P}$-symmetry imposed on DLRSM. Using Bayesian analysis, we fit the 15-year NANOGrav dataset to the GW spectrum from DWs in DLRSM and determine the best-fit values of the DW surface tension and the bias potential. The techniques of this thesis can be applied to other BSM scenarios in the future.