Measurement of the top pair production cross section at CDF using neural networks
- The Ohio State Univ., Columbus, OH (United States)
In the Tevatron accelerator at Fermilab protons and antiprotons are collided at a 1.96 TeV center of mass energy. CDF and D0 are the two experiments currently operating at the Tevatron. At these energies top quark is mostly produced via strong interactions as a top anti-top pair (t$$\bar{t}$$). The top quark has an extremely short lifetime and according to the Standard Model it decays with ~ 100% probability into a b quark and a W boson. In the ''lepton+jets'' channel, the signal from top pair production is detected for those events where one of the two W bosons decays hadronically in two quarks which we see as jets in the detector, and the other W decays into an electrically charged lepton and a neutrino. A relatively unambiguous identification in the detector is possible when we require that the charged lepton must be an electron or muon of either charge. The neutrino does not interact in the detector and its presence is inferred from an imbalance in the transverse energy of the event. They present a measurement of the top pair production cross section in p$$\bar{p}$$ collisions at 1.96 TeV, from a data sample collected at CDF between March 2002 and September 2003 with an integrated luminosity of 193.5 pb-1. In order to bring the signal to background ratio at manageable levels, measurements in this channel traditionally use precision tracking information to identify at least one secondary vertex produced in the decay of a long lived b hadron. A different approach is taken here. Because of the large mass of the top quark, t$$\bar{t}$$ events tend to be more spherical and more energetic than most of the background processes which otherwise mimic the t$$\bar{t}$$ signature in the ''lepton+jets'' channel. A number of energy based and event shape variables can be used to statistically discriminate between signal and background events. Monte Carlo simulation is used to model the kinematics of t$$\bar{t}$$ and most of the background processes. A neural network technique is employed to combine multiple variables in order to enhance signal versus background separation. Such a measurement takes advantage of a larger data sample than the b-tagging based analyses and achieves a comparable level of precision. A binned likelihood fit to the neural network output distribution for a 519 events data sample yields a 17.6 ± 3.0(stat)% fraction of t$$\bar{t}$$ events. The inclusive top pair production cross section is measured to be σt$$\bar{t}$$ = 6.6 ± 1.1(stat) ± 1.5(syst) pb.
- Research Organization:
- Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC02-76CH03000
- OSTI ID:
- 15017322
- Report Number(s):
- FERMILAB-THESIS-2004-40; TRN: US0605205
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS
ACCELERATORS
ANTIPROTONS
B QUARKS
CROSS SECTIONS
DECAY
ELECTRONS
FERMILAB COLLIDER DETECTOR
FERMILAB TEVATRON
INTERMEDIATE BOSONS
LEPTONS
LIFETIME
LUMINOSITY
MUONS
NEURAL NETWORKS
NEUTRINOS
PAIR PRODUCTION
PROBABILITY
PROTONS
QUARKS
STANDARD MODEL
STRONG INTERACTIONS
T QUARKS
TRANSVERSE ENERGY
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