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Changes for page Richard Williams

Last modified by Ricardo Julio Rodríguez Fernández on 2026/06/15 19:48
From version 9.1
edited by Ricardo Julio Rodríguez Fernández
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To version 10.1
edited by Ricardo Julio Rodríguez Fernández
on 2026/06/15 19:31
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Summary

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1 -Camille Normand
1 +María Vieites Díaz
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1 -=== A first look into 2026 LHCb data ===
2 -==== Titor: [[Camille>>https://igfae.usc.es/igfae/persoa/normand-camille-ann/676/||target="_blank"]] Normand ====
3 -==== Supervisor: [[Camille>>https://igfae.usc.es/igfae/persoa/normand-camille-ann/676/||target="_blank"]] Normand
1 +=== Background Rejection in the Search for Λb⁰ → pKτ⁺τ⁻ at LHCb ===
2 +==== Titor: [[María>>https://igfae.usc.es/igfae/persoa/vieites-diaz-maria/201/||target="_blank"]] Vieites Díaz ====
3 +==== Supervisor: Richard Williams
4 4  ====
5 5  
6 -With the acquisition of new data at a faster pace than ever, maintaining and verifying its quality is of paramount importance for greater precision measurements and improved searches for physics beyond the Standard Model at LHCb. In this project, the student will give the very first look at the newly-acquired 2026 data, i.e. only a few months old, through the study of the decay B→KS J/ψ. This high-statistics channel allows for a very clear study of, on the one hand, reconstruction effects, providing results of great impact for the whole collaboration, and on the other hand, detailed aspects of signal selection tools such as ML algorithms and particle identification, directly contributing to a world-leading search for New Physics. In both aspects, the student will have the opportunity to develop their knowledge of standard Particle Physics techniques, as well as develop their own tools and measurables for data and simulation quality evaluations.
6 +The unobserved transition b → sττ is a promising probe to perform Lepton Flavour Universality tests and search for possible New Physics effects. In this project, the selected student will investigate signal and background characteristics in the decay Λb⁰ → pKτ⁺τ⁻ and develop strategies to maximize background rejection while preserving signal efficiency. The work will involve the use of modern machine-learning techniques, including BDTs, Neural Networks, and hyperparameter optimization tools such as Optuna, applied to realistic LHCb datasets.