Resonant di-Higgs production at gravitational wave benchmarks: a collider study using machine learning

• Main Authors: Alexandre Alves, Tathagata Ghosh, Huai-Ke Guo, Kuver Sinha
• Format: Article
• Language: English
• Published: SpringerOpen 2018-12-01
• Series: Journal of High Energy Physics
• Subjects: Beyond Standard Model; Hadron-Hadron scattering (experiments)
• Online Access: http://link.springer.com/article/10.1007/JHEP12(2018)070

Abstract We perform a complementarity study of gravitational waves and colliders in the context of electroweak phase transitions choosing as our template the xSM model, which consists of the Standard Model augmented by a real scalar. We carefully analyze the gravitational wave signal at benchmark points compatible with a first order phase transition, taking into account subtle issues pertaining to the bubble wall velocity and the hydrodynamics of the plasma. In particular, we comment on the tension between requiring bubble wall velocities small enough to produce a net baryon number through the sphaleron process, and large enough to obtain appreciable gravitational wave production. For the most promising benchmark models, we study resonant di-Higgs production at the high-luminosity LHC using machine learning tools: a Gaussian process algorithm to jointly search for optimum cut thresholds and tuning hyperparameters, and a boosted decision trees algorithm to discriminate signal and background. The multivariate analysis on the collider side is able either to discover or provide strong statistical evidence of the benchmark points, opening the possibility for complementary searches for electroweak phase transitions in collider and gravitational wave experiments.