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 very strong electroweak phase transition
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abstract
We investigate the production of a stochastic background of gravitational waves in the electroweak phase transition. We consider extensions of the Standard Model which can give very strongly first-order phase transitions, such that the transition fronts either propagate as detonations or run away. To compute the bubble wall velocity, we estimate the friction with the plasma and take into account the hydrodynamics. We track the development of the phase transition up to the percolation time, and we calculate the gravitational wave spectrum generated by bubble collisions, magnetohydrodynamic turbulence, and sound waves. For the kinds of models we consider, we find parameter regions for which the gravitational waves are potentially observable at the planned space-based interferometer eLISA. In such cases, the signal from sound waves is generally dominant, while that from bubble collisions is the least significant of them. Since the sound waves and turbulence mechanisms are diminished for runaway walls, the models with the best prospects of detection at eLISA are those which do not have such solutions. In particular, we find that heavy extra bosons provide stronger gravitational wave signals than tree-level terms.
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hep-ph 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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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.