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Summer Fellowships

Last modified by Ricardo Julio Rodríguez Fernández on 2025/07/11 13:42
From version 5.1
edited by Ricardo Julio Rodríguez Fernández
on 2025/07/10 20:58
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To version 6.1
edited by Ricardo Julio Rodríguez Fernández
on 2025/07/11 13:42
Change comment: There is no comment for this version

Summary

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1 -Xabier Cid Vidal and Miguel Fernández Gómez
1 +Antonio Romero Vidal and julio Novoa Fernández
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1 -=== Machine Learning techniques to look for rare physical signals ===
2 -==== Supervisor: Miguel Fernández Gómez
1 +=== Machine Learning Tools for Precision Tests of the Standard Model at LHCb ===
2 +==== Supervisor: Julio Novoa Fernández
3 3  ====
4 4  
5 -The Standard Model of Particle Physics is the most precise description of the subatomic world that exists. Despite that, we continue to subject it to strenuous experimental tests, which include the searches for physical processes that are very unlikely to occur. An observation with a higher probability than predicted would be a clear sign of new physics.
5 +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
6 +beyond SM.
6 6  
7 -Machine Learning provides us an excellent toolkit to analyze these very rare processes. In this project, we will take data collected by the LHCb experiment, one of the four main detectors at the Large Hadron Collider at CERN, and use Machine Learning techniques to look for rare physical signals.
8 +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.
9 +
10 +This project will introduce the student to the use of ML in high-energy physics. In particular, the
11 +student will:
12 +
13 +* 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).
14 +* Learn how ML is used for measuring LFU observables with better accuracy, by implementing tools such as neural networks, decision trees and more.
15 +* Train and optimise their own ML models with different Python environments, which they could apply to other physical phenomena and beyond.