Study of t$$\bar{t}$$ production in tau jets channel at CDFII using neural networks
- Univ. of Trento (Italy)
CDF (Collider Detector at Fermilab) is a particle detector located at Fermi National Laboratories, near Chicago. it allows to study decay products of p$$\bar{p}$$ collisions at center-of-mass energy of 1.96 TeV. During its first period of data taking (RunI), CDF observed for the first time the top quark (1995). The current period of data taking (RunII) is devoted to precise measurements of top properties and to search for new physics. This thesis work is about the top decay channel named τ + jets. A t$$\bar{t}$$ pair decays in two W bosons and two b quarks. In a τ + jets event, one out of the two W decays into two jets of hadrons, while the other produces a τ lepton and a neutrino; the τ decays semileptonically in one or more charged and neutral pions while b quarks hadronize producing two jets of particles. Thus the final state of a τ + jets event has this specific signature: five jets, one τ-like, i.e. narrow and with low track multiplicity, two from b quarks, two from a W boson and a large amount of missing energy from two τ neutrinos. They search for this signal in 311 pb-1 of data collected with TOP{_}MULTIJET trigger. They use neural networks to separate signal from background and on the selected sample they perform a t$$\bar{t}$$ production cross section measurement. The thesis is structured as follows: in Chapter 1 they outline the physics of top and τ, concentrating on their discovery, production mechanisms and current physics results involving them. Chapter 2 is devoted to the description of the experimental setup: the accelerator complex first and CDF detector then. The trigger system is described in Chapter 3, while Chapter 4 shows how particles are reconstructed exploiting information from different CDF subdetectors. With Chapter 5 they begin to present their analysis: we use a feed forward neural network based on a minimization algorithm developed in Trento University, called Reactive Taboo Search (RTS), especially designed to rapidly escape from local minima. Using this neural network, they explore two techniques to select t$$\bar{t}$$ → τ + jets events, the first based on a single net, the second on two neural networks in cascade; both techniques are described in Chapter 6, together with the variables used as inputs for the nets. Finally, in Chapter 7 they present a method to measure cross section on the sample of events selected by neural networks.
- Research Organization:
- Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC02-07CH11359
- OSTI ID:
- 948188
- Report Number(s):
- FERMILAB-THESIS-2005-94; TRN: US0901592
- Country of Publication:
- United States
- Language:
- English
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