Data-driven high-dimensional statistical inference with generative models

• Autores: Amram, Oz; Szewc, Manuel
• Año de publicación: 2025
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: MACHINE LEARNING; STATISTICAL INFERENCE; DATA-DRIVEN BACKGROUNDS; DENSITY ESTIMATION
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

Crucial to many measurements at the LHC is the use of correlated multi-dimensional information to distinguish rare processes from large backgrounds, which is complicated by the poor modeling of many of the crucial backgrounds in Monte Carlo simulations. In this work, we introduce HI-SIGMA, a method to perform unbinned high-dimensional statistical inference with data-driven background distributions. In contradistinction to many applications of Simulation Based Inference in High Energy Physics, HI-SIGMA relies on generative ML models, rather than classifiers, to learn the signal and background distributions in the high-dimensional space. These ML models allow for interpretable inference while also incorporating model errors and other sources of systematic uncertainties. We showcase this methodology on a simplified version of a di-Higgs measurement in the bbγγ final state, where the di-photon resonance allows for background interpolation from sidebands into the signal region. We demonstrate that HI-SIGMA provides improved sensitivity as compared to standard classifier-based methods, and that systematic uncertainties can be straightforwardly incorporated by extending methods which have been used for histogram based analyses.
Fil: Amram, Oz. No especifíca;
Fil: Szewc, Manuel. University of Cincinnati; Estados Unidos. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Ciencias Fisicas. - Universidad Nacional de San Martin. Instituto de Ciencias Fisicas.; Argentina