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Measurement of the Top Quark Mass with Neural Networks
Sánchez, Carlos Andrés

2002, Doctor of Philosophy, Ohio State University, Physics.

At the Tevatron, protons and antiprotons collide with a center-of-mass energy of 1.8 TeV. In this energy range the dominant source of top quarks is the production of tt̅ pairs via quark-antiquark annhilation and gluon-gluon fusion. We present our analyses to determine the mass of the top quark reconstructed through the “lepton+jets” decay channel in the 106 ± 4.1 pb-1 of data collected by the Collider Detector at Fermilab (CDF) from 1992-1996.

In the past, the top mass was obtained by comparing the observed kinematic features of top events to those predicted for different top quark masses. These distributions are known as templates. While any kinematic variable, which exhibits sensitivity to the top mass can be used to calculate the mass of the top quark, the lowest statistical uncertainty is achieved by reconstructing the top mass from the decay products of the tt̅ pair. It was also observed that it is possible to obtain a better estimate of the top mass by fitting the different templates to a smooth function. Previously, we have used a combination of a Gaussian and a Gamma function to fit the distributions. In this analysis, we find that using a Neural Network (NN) to fit the distribution gives slightly better results, and that the NN fitting technique is applicable to any kinematic variable. Following this recipe the top mass is measured to be 177.9 ± 4.7(stat.) ± 4.6(syst.) GeV/c2 when using the reconstructed mass, Mrec. When we use the total transverse energy of the events, Ht, the mass of the top quark is found to be 204.4 ± 9.2(stat.) ± 9.2(syst.) GeV/c2.

As noted, there are different kinematic variables that can be used to calculate the top mass. A Neural Network provides a simple and elegant way of combining all of these variables which have mass information. The idea is that combining the information from more than one kinematic variable would result in a more accurate measurement of the top mass. Therefore, this NN based technique uses a combination of Mrec, HT, the invariant mass of the tt̅ system, Mtt̅, and the sum of the PT's of the two leading jets, PT(1) + PT(2). These variables were chosen because they exhibit the greatest mass dependence. The Neural Networks attempt to classify the events as tt̅ signal or background. For each event the NN provides a set of probabilities that it has come from any of the top masses used in this analysis, as well as background. Using this information we construct a discrete likelihood function from which the top mass is calculated to be 181.9 ± 5.1 (stat.) ± 5.2(syst.) GeV/c2.

Richard Hughes (Advisor)
Brian Winer (Other)
Tom Humanic (Other)
Stephen Pinsky (Other)
148 p.

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Sánchez, C. (2002). Measurement of the Top Quark Mass with Neural Networks. (Electronic Thesis or Dissertation). Retrieved from

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Sánchez, Carlos. "Measurement of the Top Quark Mass with Neural Networks." Electronic Thesis or Dissertation. Ohio State University, 2002. OhioLINK Electronic Theses and Dissertations Center. 21 Feb 2018.

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Sánchez, Carlos "Measurement of the Top Quark Mass with Neural Networks." Electronic Thesis or Dissertation. Ohio State University, 2002.


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