Speakers
Description
The Matrix Element Method (MEM) offers optimal statistical power for hypothesis testing in particle physics, but its application is hindered by the computationally intensive multi-dimensional integrals required to model detector effects. We present a novel approach that addresses this challenge by employing Transformers and generative machine learning (ML) models. Specifically, we utilize ML surrogates to efficiently sample parton-level events for numerical integration and to accurately encode the complex transfer functions describing detector reconstruction. We demonstrate this technique on the challenging ttH(bb) process in the semileptonic channel using the full CMS detector simulation. Furthermore, we extend the method to multiple processes relevant for double Higgs searches in the bbWW channel, constructing a comprehensive ML-based reconstruction surrogate across the entire analysis phase space. This advancement enables fully unbinned likelihood estimations of double Higgs Effective Field Theory (EFT) couplings directly from experimental data, with the potential of significantly enhancing sensitivity to new physics.
| Experiment context, if any | CMS experiment |
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