Professor Chris Hays

Abstract:

We report a measurement of the top-quark mass M_{t} in the dilepton decay channel tt[over ] --> bl;{'+} nu_{l};{'}b[over ]l;{-}nu[over ]_{l}. Events are selected with a neural network which has been directly optimized for statistical precision in top-quark mass using neuroevolution, a technique modeled on biological evolution. The top-quark mass is extracted from per-event probability densities that are formed by the convolution of leading order matrix elements and detector resolution functions. The joint probability is the product of the probability densities from 344 candidate events in 2.0 fb;{-1} of pp[over ] collisions collected with the CDF II detector, yielding a measurement of M_{t} = 171.2 +/- 2.7(stat) +/- 2.9(syst) GeV / c;{2}.

Abstract:

We report on a search for the non-standard-model process u(c) + g --> t using pp[over ] collision data collected by the Collider Detector at Fermilab II detector corresponding to 2.2 fb;{-1}. The candidate events are classified as signal-like or backgroundlike by an artificial neural network. The observed discriminant distribution yields no evidence for flavor-changing neutral current top-quark production, resulting in an upper limit on the production cross section sigma(u(c) + g --> t) < 1.8 pb at the 95% C.L. Using theoretical predictions we convert the cross section limit to upper limits on flavor-changing neutral current branching ratios: B(t --> u + g) < 3.9 x 10;{-4} and B(t --> c + g) < 5.7 x 10;{-3}.

Abstract:

We report a measurement of the top-quark mass Mt in the dilepton decay channel tt̄→bl′+νl′b̄l-ν̄l. Events are selected with a neural network which has been directly optimized for statistical precision in top-quark mass using neuroevolution, a technique modeled on biological evolution. The top-quark mass is extracted from per-event probability densities that are formed by the convolution of leading order matrix elements and detector resolution functions. The joint probability is the product of the probability densities from 344 candidate events in 2.0fb-1 of pp̄ collisions collected with the CDF II detector, yielding a measurement of Mt=171.2±2.7(stat)±2.9(syst)GeV/c2. © 2009 The American Physical Society.

Abstract:

We present a measurement of the top quark mass with tt̄ dilepton events produced in pp̄ collisions at the Fermilab Tevatron (s=1.96TeV) and collected by the CDF II detector. A sample of 328 events with a charged electron or muon and an isolated track, corresponding to an integrated luminosity of 2.9fb-1, are selected as tt̄ candidates. To account for the unconstrained event kinematics, we scan over the phase space of the azimuthal angles (ν1,ν2) of neutrinos and reconstruct the top quark mass for each ν1, ν2 pair by minimizing a χ2 function in the tt̄ dilepton hypothesis. We assign χ2-dependent weights to the solutions in order to build a preferred mass for each event. Preferred mass distributions (templates) are built from simulated tt̄ and background events, and parametrized in order to provide continuous probability density functions. A likelihood fit to the mass distribution in data as a weighted sum of signal and background probability density functions gives a top quark mass of 165.5-3.3+3.4(stat)±3. 1(syst)GeV/c2. © 2009 The American Physical Society.

Abstract:

We report a measurement of the top quark mass, mt, obtained from pp̄ collisions at s=1.96TeV at the Fermilab Tevatron using the CDF II detector. We analyze a sample corresponding to an integrated luminosity of 1.9fb-1. We select events with an electron or muon, large missing transverse energy, and exactly four high-energy jets in the central region of the detector, at least one of which is tagged as coming from a b quark. We calculate a signal likelihood using a matrix element integration method, where the matrix element is modified by using effective propagators to take into account assumptions on event kinematics. Our event likelihood is a function of mt and a parameter JES (jet energy scale) that determines in situ the calibration of the jet energies. We use a neural network discriminant to distinguish signal from background events. We also apply a cut on the peak value of each event likelihood curve to reduce the contribution of background and badly reconstructed events. Using the 318 events that pass all selection criteria, we find mt=172.7±1.8(stat+JES) ±1.2(syst)GeV/c2. © 2009 The American Physical Society.