Measurement of the top pair production cross section at CDF using neural networks

Measurement of the top pair production cross section at CDF using neural networks

Bibliographic Details
Main Author: Marginean, Radu
Language:English
Published: The Ohio State University / OhioLINK 2004
Subjects:
top quark cross section measurement
proton anti-proton collisions
CDF experiment
neural networks
Online Access:http://rave.ohiolink.edu/etdc/view?acc_num=osu1101831484
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spelling ndltd-OhioLink-oai-etd.ohiolink.edu-osu11018314842021-08-03T05:49:29Z Measurement of the top pair production cross section at CDF using neural networks Marginean, Radu top quark cross section measurement proton anti-proton collisions CDF experiment neural networks In the Tevatron accelerator at Fermilab protons and antiprotons are collided at 1.96 TeV center of mass energy. At these energies top quark is mostly produced via strong interactions as a top anti-top pair. The top quark has an extremely short lifetime and according to the Standard Model it decays almost always into a 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 to two quarks which we see as jets in the detector, the other W decays into a 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. The neutrino does not interact in the detector and its presence is inferred from an imbalance in the transverse energy of the event. We present a measurement of the top pair production cross section in proton antiproton collisions at 1.96 TeV from a data sample collected at CDF with an integrated luminosity of 193.5 inverse picobarns. Because of the large mass of the top quark, top events tend to be more spherical and more energetic than most of the background processes which otherwise mimic its signature. 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 signal and most of the background processes. A neural network technique is employed to combine multiple variables in order to enhance signal versus background separation. 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 top anti-top events. The inclusive top pair production cross section is measured to be 6.6±1.1(stat)±1.5(sys) pb. 2004 English text The Ohio State University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=osu1101831484 http://rave.ohiolink.edu/etdc/view?acc_num=osu1101831484 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
collection NDLTD
language English
sources NDLTD
topic top quark cross section measurement
proton anti-proton collisions
CDF experiment
neural networks
spellingShingle top quark cross section measurement
proton anti-proton collisions
CDF experiment
neural networks
Marginean, Radu
Measurement of the top pair production cross section at CDF using neural networks
author Marginean, Radu
author_facet Marginean, Radu
author_sort Marginean, Radu
title Measurement of the top pair production cross section at CDF using neural networks
title_short Measurement of the top pair production cross section at CDF using neural networks
title_full Measurement of the top pair production cross section at CDF using neural networks
title_fullStr Measurement of the top pair production cross section at CDF using neural networks
title_full_unstemmed Measurement of the top pair production cross section at CDF using neural networks
title_sort measurement of the top pair production cross section at cdf using neural networks
publisher The Ohio State University / OhioLINK
publishDate 2004
url http://rave.ohiolink.edu/etdc/view?acc_num=osu1101831484
work_keys_str_mv AT margineanradu measurementofthetoppairproductioncrosssectionatcdfusingneuralnetworks
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