Changes for page Antonio Romero Vidal and Julio Novoa Fernández
Last modified by Ricardo Julio Rodríguez Fernández on 2026/06/18 11:16
From version 13.1
edited by Ricardo Julio Rodríguez Fernández
on 2026/06/18 11:16
on 2026/06/18 11:16
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To version 6.1
edited by Ricardo Julio Rodríguez Fernández
on 2024/06/25 11:58
on 2024/06/25 11:58
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... ... @@ -1,1 +1,1 @@ 1 - AntonioRomero Vidal andJulio NovoaFernández1 +Garzón Heydt_Juan Antonio - Parent
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... ... @@ -1,1 +1,1 @@ 1 -SummerFellowship s2026.WebHome1 +Summer Fellowship.WebHome - Content
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... ... @@ -1,8 +1,5 @@ 1 -=== Chasing New Physics in Run 3: Machine Learning for Flavour Anomalies at LHCb === 2 -==== Titor: [[Antonio>>https://igfae.usc.es/igfae/persoa/romero-vidal-antonio/164/||target="_blank"]] Romero Vidal ==== 3 -==== Supervisor: [[Julio>>https://igfae.usc.es/igfae/persoa/novoa-fernandez-julio/390/||target="_blank"]] Nóvoa Fernández 4 -==== 1 +=== Reconstruccion de trazas en un detector de particulas. Comparacion de los metodos TimTrack Recursivo y Kalman Filter === 5 5 6 - TheLHCRun3 era has started a new chapter in particle physics, providing unprecedented collision rates and a completely upgraded detector at the LHCb experiment. This massive increase in statistics offers a golden opportunity to test Lepton Flavour Universality (LFU). According to the Standard Model, LFU dictates that all charged leptons (electrons, muons, and taus) interact with the fundamental forces in exactly the same way, differing only in their masses. However, recent intriguing anomalies in B-meson decays hint that this universality might be violated, potentially opening the door to New Physics. Achieving a level of precision never reached before is essential to confirm these hints, but the high-luminosity environment of Run 3 also brings significant challenges, as signal events must be cleanly isolated from much denser and more complex background environments.3 +=== === 7 7 8 - Thissummerproject will introducethestudentto theforefrontof experimentaldataanalysisusingthebrand-newRun 3datasets.The studentwilldevelopPython-basedMachineLearning(ML)techniques—suchas GradientBoostedDecisionTreesorDeepNeural Networks—tooptimizetheselectionofmulti-bodysemileptonic B-mesondecays.By trainingmodels tosuppressthechallengingbackgrounds specific to Run3conditions,thestudentwilldirectlycontributeto preparingthenextgenerationofhigh-precision testsof the StandardModel.5 +El Kalman Filter (KF) constituye el estandar universal para el ajuste de datos en sistemas dinamicos. Frente a el, el TimTrack Recursivo (TTR) ha sido desarrollado en la USC para su uso en detectores de rayos cosmicos tipo Trasgo. En este trabajo, se propone la comparacion de ambos metodos mediante trazas simuladas, comparando sus principales prestaciones: resolución, tiempo y número de pasos para la convergencia.
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... ... @@ -1,0 +1,1 @@ 1 +XWiki.RicardoJulioRodriguezFernandez - Comment
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... ... @@ -1,0 +1,1 @@ 1 +1 student - Date
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... ... @@ -1,0 +1,1 @@ 1 +2024-05-28 10:05:33.800