Constraining the Higgs Potential with Neural Simulation-based Inference for Di-Higgs Production
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909388433784832 |
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| author | Mastandrea, Radha Nachman, Benjamin Plehn, Tilman |
| author_facet | Mastandrea, Radha Nachman, Benjamin Plehn, Tilman |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_15847 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Constraining the Higgs Potential with Neural Simulation-based Inference for Di-Higgs Production Mastandrea, Radha Nachman, Benjamin Plehn, Tilman High Energy Physics - Phenomenology Machine Learning 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. |
| title | Constraining the Higgs Potential with Neural Simulation-based Inference for Di-Higgs Production |
| topic | High Energy Physics - Phenomenology Machine Learning |
| url | https://arxiv.org/abs/2405.15847 |