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Constraining the Higgs Potential with Neural Simulation-based Inference for Di-Higgs Production

R Mastandrea, B Nachman, T Plehn - arXiv preprint arXiv:2405.15847, 2024 - arxiv.org
Mathematics paper hep-ph Suggest

… In this work, we have explored the use of neural simulation-based inference (nSBI) to enhance the sensitivity to searches for pair production of Higgs bosons. As our …

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BibTeX

@article{2405.15847v2,
Author        = {Radha Mastandrea and Benjamin Nachman and Tilman Plehn},
Title         = {Constraining the Higgs Potential with Neural Simulation-based Inference
  for Di-Higgs Production},
Eprint        = {2405.15847v2},
DOI           = {10.1103/PhysRevD.110.056004},
ArchivePrefix = {arXiv},
PrimaryClass  = {hep-ph},
Abstract      = {Determining the form of the Higgs potential is one of the most exciting
challenges of modern particle physics. Higgs pair production directly probes
the Higgs self-coupling and should be observed in the near future at the
High-Luminosity LHC. We explore how to improve the sensitivity to physics
beyond the Standard Model through per-event kinematics for di-Higgs events. In
particular, we employ machine learning through simulation-based inference to
estimate per-event likelihood ratios and gauge potential sensitivity gains from
including this kinematic information. In terms of the Standard Model Effective
Field Theory, we find that adding a limited number of observables can help to
remove degeneracies in Wilson coefficient likelihoods and significantly improve
the experimental sensitivity.},
Year          = {2024},
Month         = {May},
Url           = {http://arxiv.org/abs/2405.15847v2},
File          = {2405.15847v2.pdf}
}

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