Journal of High Energy Physics

, 2018:70 | Cite as

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

  • Alexandre Alves
  • Tathagata Ghosh
  • Huai-Ke Guo
  • Kuver SinhaEmail author
Open Access
Regular Article - Experimental Physics


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.


Beyond Standard Model Hadron-Hadron scattering (experiments) 


Open Access

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Copyright information

© The Author(s) 2018

Authors and Affiliations

  • Alexandre Alves
    • 1
  • Tathagata Ghosh
    • 2
  • Huai-Ke Guo
    • 3
  • Kuver Sinha
    • 3
    Email author
  1. 1.Departamento de FísicaUniversidade Federal de São Paulo, UNIFESPDiademaBrazil
  2. 2.Department of Physics & AstronomyUniversity of HawaiiHonoluluU.S.A.
  3. 3.Department of Physics and AstronomyUniversity of OklahomaNormanU.S.A.

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