Machine Learning (ML) techniques are emerging as a competitive tool for analysing and extracting information from large volumes of complex high-dimensional data. In the last few years, the High Energy Physics community has adopted and customised a variety of ML techniques for various steps of data analysis for e.g., trigger, event reconstruction, particle identification, jet tagging, signal/background classification, etc. ML is also emerging as an alternative approach to perform model independent searches for new phenomenon at particle physics experiments. 

In this talk, I will give a brief introduction to the recent advances in ML relating to model independent searches for BSM. Thereafter I will focus on my latest work where we have proposed a new semi-supervised algorithm for anomaly detection called Anomaly Awareness (AA). I will show how AA works by considering a well-known “Fat Jet” topology for new physics searches. I will also discuss our work using ML to exploit the kinematic information in VH channel where we parameterized the
effect of new physics in the SMEFT framework."

Per partecipare: gruppo Teams Università di Genova numero 1npbr0h (se si possiede un account Teams Unige, si può accedere con il codice.
Se invece si possiede solo un account Teams INFN, scrivere a simone.marzani@ge.infn.it per essere aggiunti come ospiti).