A bstract 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 …

A bstract Extracting maximal information from experimental data requires access to the
likelihood function, which however is never directly available for complex experiments like …

In this work, we have explored the sensitivity of multilepton final states in probing the
gaugino sector of a R-parity violating supersymmetric scenario with specific lepton number …

We study the impact of machine-learning algorithms on LHC searches for leptoquarks in
final states with hadronically decaying tau leptons, multiple b-jets, and large missing …

A bstract We apply machine-learning techniques to the effective-field-theory analysis of the
e+ e−→ W+ W− processes at future lepton colliders, and demonstrate their advantages in …

Abstract Machine-learned likelihoods (MLL) combines machine-learning classification
techniques with likelihood-based inference tests to estimate the experimental sensitivity of …

The detection of Dark Matter (DM) remains a significant challenge in particle physics. This
study exploits advanced machine learning models to improve detection capabilities of liquid …

Modern machine learning (ML) has become a fundamental tool in experimental and
phenomenological analyses of high-energy physics. In order to estimate the experimental …

This thesis focuses on studying machine learning models for particle jet classification based
on Large Hadron Collider (LHC) data. The analysis of the computational challenges of the …

Machine-Learned Likelihood (MLL) is a method that, by combining modern machine-
learning techniques with likelihood-based inference tests, allows estimating the …