Resonant di-Higgs production at gravitational wave benchmarks: a collider study using machine learning
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.
- Authors:
-
- Federal Univ. of Sao Paulo (Brazil). Dept. of Physics
- Univ. of Hawaii, Honolulu, HI (United States). Dept. of Physics and Astronomy
- Univ. of Oklahoma, Norman, OK (United States). Dept of Physics and Astronomy
- Publication Date:
- Research Org.:
- Univ. of Hawaii, Honolulu, HI (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), High Energy Physics (HEP)
- OSTI Identifier:
- 1597962
- Grant/Contract Number:
- SC0010504
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of High Energy Physics (Online)
- Additional Journal Information:
- Journal Name: Journal of High Energy Physics (Online); Journal Volume: 2018; Journal Issue: 12; Journal ID: ISSN 1029-8479
- Publisher:
- Springer Berlin
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS
Citation Formats
Alves, Alexandre, Ghosh, Tathagata, Guo, Huai-Ke, and Sinha, Kuver. Resonant di-Higgs production at gravitational wave benchmarks: a collider study using machine learning. United States: N. p., 2018.
Web. doi:10.1007/JHEP12(2018)070.
Alves, Alexandre, Ghosh, Tathagata, Guo, Huai-Ke, & Sinha, Kuver. Resonant di-Higgs production at gravitational wave benchmarks: a collider study using machine learning. United States. https://doi.org/10.1007/JHEP12(2018)070
Alves, Alexandre, Ghosh, Tathagata, Guo, Huai-Ke, and Sinha, Kuver. Sat .
"Resonant di-Higgs production at gravitational wave benchmarks: a collider study using machine learning". United States. https://doi.org/10.1007/JHEP12(2018)070. https://www.osti.gov/servlets/purl/1597962.
@article{osti_1597962,
title = {Resonant di-Higgs production at gravitational wave benchmarks: a collider study using machine learning},
author = {Alves, Alexandre and Ghosh, Tathagata and Guo, Huai-Ke and Sinha, Kuver},
abstractNote = {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.},
doi = {10.1007/JHEP12(2018)070},
journal = {Journal of High Energy Physics (Online)},
number = 12,
volume = 2018,
place = {United States},
year = {Sat Dec 01 00:00:00 EST 2018},
month = {Sat Dec 01 00:00:00 EST 2018}
}
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