Use this URL to cite or link to this record in EThOS:
Title: A study of the CKM angle γ using the LHCb experiment and a distributed workload management system
Author: Li, Ying Ying
ISNI:       0000 0004 2706 9067
Awarding Body: University of Cambridge
Current Institution: University of Cambridge
Date of Award: 2009
Availability of Full Text:
Access from EThOS:
Full text unavailable from EThOS. Please try the link below.
Access from Institution:
The LHCb experiment, at the Large Hadron Collider (LHC), will make precision CP violation measurements and search for new physics beyond the Standard Model of particle physics. The 14 TeV proton-proton collisions at the LHC will produce the full range of B-hadrons, at a data rate of peta-bytes per year. In order to process this unprecedented amount of data, distributed computing resources (the Grid) located across four continents are coordinated by a series of workload management systems (Grid middlewares). DIRAC, the LHCb distributed workload management system for event simulation, reconstruction and user analysis is extended from the Linux operating platform to the Windows platform, allowing a transparent integration of Windows resources to the existing Linux system. The B± → D⁰/D⁰ (K0s π⁺ π⁻)K± decay, a key channel for the precision measurement of the CKM angle γ, is studied using the multiplatform DIRAC system. The expected annual yield from this channel for 2 fb⁻¹ of data (a full data taking year) is ~4200 events, with a total physics background to signal ratio (B/S) of < 0:54 at the 90% confidence level. The dominant background is expected to arise from a real D⁰/D⁰ candidate reconstructed with a fake kaon from the underlying event, with a B/S = 0.35 ± 0.03 where the error is statistical. The potential background arising from a real D⁰/D⁰ candidate reconstructed with a mis-identified pion is greatly reduced, with a B/S < 0:095 at the 90% confidence level, by the use of particle identification information from the RICH sub-detector. The estimated LHCb statistical sensitivity to γ is ~12° for 2 fb⁻¹ of data, using a model dependent Dalitz analysis with a model error of ~9°.
Supervisor: Gibson, Val Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral