Speaker
Dr
Marco Letizia
(Università di Genova and INFN)
Description
The likelihood-ratio test can be used to perform a goodness-of-fit test between a reference model and observations if the alternative hypothesis is selected from data by exploring a rich parametrised family of functions. The New Physics Learning Machine (NPLM) methodology has been developed as a concrete realisation of this idea, to perform model-independent searches at collider experiments. In this presentation, I will focus on a recent implementation based on kernel methods, which is extremely efficient and highly flexible (arXiv:2204.02317). I will present studies on new physics searches, data quality monitoring, and recent results on the evaluation of generative models.
Primary authors
Andrea Wulzer
(ICREA & IFAE)
Dr
Gaia Grosso
(IAIFI-MIT)
Prof.
Lorenzo Rosasco
(Università di Genova)
Dr
Marco Letizia
(Università di Genova and INFN)
Maurizio Pierini
(CERN)
