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arXiv · 2106.05984

Observational prospects for phase transitions at LISA: Fisher matrix analysis

Abstract

A first order phase transition at the electroweak scale would lead to the production of gravitational waves that may be observable at upcoming space-based gravitational wave (GW) detectors such as LISA (Laser Interferometer Space Antenna). As the Standard Model has no phase transition, LISA can be used to search for new physics by searching for a stochastic gravitational wave background. In this work we investigate LISA's sensitivity to the thermodynamic parameters encoded in the stochastic background produced by a phase transition, using the sound shell model to characterise the gravitational wave power spectrum, and the Fisher matrix to estimate uncertainties. We explore a parameter space with transition strengths $\alpha < 0.5$ and phase boundary speeds $0.4 < v_\text{w} < 0.9$, for transitions nucleating at $T_{\text{N}} = 100$ GeV, with mean bubble spacings $0.1$ and $0.01$ of the Hubble length, and sound speed $c/\sqrt{3}$. We show that the power spectrum in the sound shell model can be well approximated by a four-parameter double broken power law, and find that the peak power and frequency can be measured to approximately 10% accuracy for signal-to-noise ratios (SNRs) above 20. Determinations of the underlying thermodynamic parameters are complicated by degeneracies, but in all cases the phase boundary speed will be the best constrained parameter. Turning to the principal components of the Fisher matrix, a signal-to-noise ratio above 20 produces a relative uncertainty less than 3% in the two highest-order principal components, indicating good prospects for combinations of parameters. The highest-order principal component is dominated by the wall speed. These estimates of parameter sensitivity provide a preliminary accuracy target for theoretical calculations of thermodynamic parameters.

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BibTeXRIS

Chloe Gowling, Mark Hindmarsh. 2021-06-10. Observational prospects for phase transitions at LISA: Fisher matrix analysis. https://doi.org/10.1088/1475-7516%2F2021%2F10%2F039

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