Changes for page Xabier Cid Vidal and Pablo Eduardo Menéndez-Valdés Pérez
Last modified by Ricardo Julio Rodríguez Fernández on 2025/09/29 11:43
From version 2.2
edited by Ricardo Julio Rodríguez Fernández
on 2025/07/10 14:16
on 2025/07/10 14:16
Change comment:
Update document after refactoring.
To version 6.1
edited by Ricardo Julio Rodríguez Fernández
on 2025/09/29 11:43
on 2025/09/29 11:43
Change comment:
There is no comment for this version
Summary
-
Page properties (2 modified, 0 added, 0 removed)
Details
- Page properties
-
- Title
-
... ... @@ -1,1 +1,1 @@ 1 -Xabier Cid Vidal 1 +Xabier Cid Vidal and Pablo Eduardo Menéndez-Valdés Pérez - Content
-
... ... @@ -1,5 +1,7 @@ 1 -=== Quantum simulation of real-time dynamics in high-energy physics === 2 -==== Supervisor: Wenyang Qian 1 +=== Classifiers for LLPs searches at LHCb === 2 +==== Supervisor: Pablo Eduardo Menéndez-Valdés Pérez ==== 3 +==== Summer Fellow: Enrique Rodríguez Ramons 3 3 ==== 5 +Several Beyond the Standard Model (BSM) theories predict the existence of Long-Lived Particles (LLPs), which may largely escape detection by current particle physics experiments. However, these particles could occasionally decay into known elementary particles—such as muons—making their detection possible under certain conditions. 4 4 5 - Wewillworkonquantumsimulationofreal-time dynamics forhigh-energyphysicsproblemsusingthe tensornetworkand digital quantumcomputingapproaches. We startwiththe Ising modelto get familiaritywithtumsimulation andthenmoveon tomoreadvancedreal-timesimulationofquantum fieldtheory including latticegauge theoryrelevanttotopicsin high-energyphysics. Familiaritywithquantum mechanicsandprogrammingarerequired.Backgroundin quantum informationsciencewould be a plusbutnot necessary.7 +The LHCb detector provides excellent precision in reconstructing both production and decay vertices. Leveraging this capability with Machine Learning (ML) techniques can significantly enhance the sensitivity to LLP signatures. In this project, we aim to develop and evaluate such ML tools by comparing the performance of various algorithms within the XGBoost library.