Antonio Romero Vidal and julio Novoa Fernández

Machine Learning Tools for Precision Tests of the Standard Model at LHCb

Supervisor: Julio Novoa Fernández

Leptons are a fundamental class in the Standard Model (SM) of particle physics. According to this model, there is no apparent difference between leptons apart from their masses - this is known as Lepton Flavour Universality (LFU). However, recent measurements of LFU observables show significant disagreement with respect to SM predictions, which could suggest new physics
beyond SM.

To reduce their uncertainty and solve the current situation, these LFU observables need to be measured with great precision. In particular, the experimental data has to be carefully processed to separate “signal” and “background” events. This selection includes Machine Learning (ML) tools that improve the efficiency of this procedure.

This project will introduce the student to the use of ML in high-energy physics. In particular, the
student will:

• Analyse real data from LHCb, one of the main experiments of the Large Hadron Collider at CERN (the world’s largest and most powerful particle accelerator).
• Learn how ML is used for measuring LFU observables with better accuracy, by implementing tools such as neural networks, decision trees and more.
• Train and optimise their own ML models with different Python environments, which they could apply to other physical phenomena and beyond.