Seminario de Santiago Tanco (Universidad Nacional de La Plata)

"Bayesian tools for the LHC: a proof of concept in di-Higgs searches"
Santiago Tanco
(Universidad Nacional de La Plata)

Abstract: Extracting reliable physical information from collider data
requires a combination of mathematical, computational and statistical
tools to model the observed distributions, often with the help of
powerful simulations. But when simulations cannot be fully trusted,
data-driven approaches become indispensable. In this talk, I will
present Bayesian tools that provide a flexible, data-driven method for
unsupervised training on probabilistic models of collider observables,
highlighting their advantages for parameter inference and uncertainty
estimation. I will illustrate these points in the context of pp > hh >
bbbb searches, where recent analyses use a variation of the widely used
data-driven "ABCD method". Within our framework, the ABCD method can be
generalized and improved, especially when its assumptions are not
exactly satisfied, leading to more robust background subtraction. Our
approach is able to exploit correlations in multi-dimensional data by
modelling kinematic variables and b-tagging scores at the event level.
These results also show a way in which simulations can be used as
priors, guiding the learning process for unsupervised estimation of the
true data distributions. Based on 2402.08001 and 2511.21832.

Fecha: jueves, 22 de enero de 2026
Hora: 12:10 horas
Lugar: Seminario de Física Teórica