Speaker
Description
Theoretical interpretations of particle physics data, such as the determination of the Wilson coefficients of the Standard Model Effective Field Theory (SMEFT), often involve the inference of multiple parameters from a global dataset. Optimising such interpretations requires the identification of observables that exhibit the highest possible sensitivity to the underlying theory parameters. Based on our recently developed open source framework, ML4EFT, that combines machine learning regression and classification techniques to parameterise high-dimensional likelihood ratios, I will present the integration of unbinned multivariate observables into global SMEFT fits. As compared to traditional measurements, such observables enhance the sensitivity to the theory parameters by preventing the information loss incurred when binning in a subset of final-state kinematic variables. In particular, I will focus on optimal observables in top-quark pair and Higgs+Z production at the LHC and demonstrate their impact on the SMEFT parameter space as compared to binned measurements, and present the improved constraints associated to multivariate inputs.
| PhD Student | yes |
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