PPO reinforcement learning accelerates identification of gravitational wave signals from supercooled phase transitions in a minimal dark U(1)_x sector compared to Monte Carlo sampling.
Gravitational waves from a supercooled electroweak phase transition and their detection with pulsar timing arrays
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abstract
We investigate the properties of a stochastic gravitational wave background produced by a first-order electroweak phase transition in the regime of extreme supercooling. We study a scenario whereby the percolation temperature that signifies the completion of the transition, $T_p$, can be as low as a few MeV (nucleosynthesis temperature), while most of the true vacuum bubbles are formed much earlier at the nucleation temperature, $T_n\sim 50$ GeV. This implies that the gravitational wave spectrum is mainly produced by the collisions of large bubbles and characterised by a large amplitude and a peak frequency as low as $f \sim 10^{-9}-10^{-7}$ Hz. We show that such a scenario can occur in (but not limited to) a model based on a non-linear realisation of the electroweak gauge group, such that the Higgs vacuum configuration is altered by a cubic coupling. In order to carefully quantify the evolution of the phase transition of this model over such a wide temperature range, we go beyond the usual fast transition approximation, taking into account the expansion of the Universe as well as the behaviour of the nucleation probability at low temperatures. Our computation shows that there exists a range of parameters for which the gravitational wave spectrum lies at the edge between the exclusion limits of current pulsar timing array experiments and the detection band of the future Square Kilometre Array observatory.
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Supercool with PPO: Exploring Supercooled Phase Transitions via Reinforcement Learning
PPO reinforcement learning accelerates identification of gravitational wave signals from supercooled phase transitions in a minimal dark U(1)_x sector compared to Monte Carlo sampling.