Affiliation:
1. University of California
2. Lawrence Berkeley National Laboratory
3. Universität Heidelberg
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.
Published by the American Physical Society
2024
Funder
U.S. Department of Energy
Office of Science
John Templeton Foundation
National Science Foundation
National Energy Research Scientific Computing Center
Baden-Württemberg Stiftung
Deutsche Forschungsgemeinschaft
Germany’s Excellence Strategy
Publisher
American Physical Society (APS)