Measurement of the t$\bar{t}$ production cross section in the E<sub>T</sub>+jets channel at CDF

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This thesis is focused on an inclusive search of the t$\bar{t}$ → ET + jets decay channel by means of neural network tools in proton antiproton collisions at √s = 1.96 TeV recorded by the Collider Detector at Fermilab (CDF). At the Tevatron p$\bar{p}$ collider top quarks are mainly produced in pairs through quark-antiquark annihilation and gluon-gluon fusion processes; in the Standard Model description, the top quark then decays to a W boson and a b quark almost 100% of the times, so that its decay signatures are classified according to the W decay modes. When only one W decays leptonically, the t$\bar{t}$ event typically contains a charged lepton, missing transverse energy due to the presence of a neutrino escaping from the detector, and four high transverse momentum jets, two of which originate from b quarks. In this thesis we describe a t$\bar{t}$ production cross section measurement which uses data collected by a 'multijet' trigger, and selects this kind of top decays by requiring a high-PT neutrino signature and by using an optimized neural network to discriminate top quark pair production from backgrounds. In Chapter 1, a brief review of the Standard Model of particle physics will be discussed, focusing on top quark properties and experimental signatures. In Chapter 2 will be presented an overview of the Tevatron accelerator chain that provides p$\bar{p}$ collisions at the center-of-mass energy of √s = 1.96 TeV, and proton and antiproton beams production procedure will be discussed. The CDF detector and its components and subsystems used for the study of p{bar p} collisions provided by the Tevatron will be described in Chapter 3. Chapter 4 will detail the reconstruction procedures used in CDF to detect physical objects exploiting the features of the different detector subsystems. Chapter 5 will provide an overview of the main concepts regarding Artificial Neural Networks, one of the most important tools we will use in the analysis. Chapter 6 will be devoted to the description of the main characteristics of the t$\bar{t}$ → ET + jets decay channel used to train our neural network to discriminate the top pair production from background processes. We will discuss the event selection method and the technique used for background prediction, that will rely on b-jets identification rate parameterization. Finally, Chapter 7 will provide a description of the final data sample and a detailed discussion of the systematic uncertainties before determining the cross section measurement by means of a likelihood maximization.