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Summer Fellowships

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1 -Xabier Cid Vidal
1 +Antonio Romero Vidal and julio Novoa Fernández
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1 -=== Quantum simulation of real-time dynamics in high-energy physics ===
2 -==== Supervisor: Wenyang Qian
1 +=== Machine Learning Tools for Precision Tests of the Standard Model at LHCb ===
2 +==== Supervisor: Julio Novoa Fernández
3 3  ====
4 4  
5 -We will work on quantum simulation of real-time dynamics for high-energy physics problems using the tensor network and digital quantum computing approaches. We start with the Ising model to get familiarity with quantum simulation and then move on to more advanced real-time simulation of quantum field theory including lattice gauge theory relevant to topics in high-energy physics. Familiarity with quantum mechanics and programming are required. Background in quantum information science would be a plus but not necessary.
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.
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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.
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10 +This project will introduce the student to the use of ML in high-energy physics. In particular, the
11 +student will:
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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.