We present the first evidence for electroweak single top quark production using nearly 1 fb{sup -1} of Tevatron Run II data at {radical}s = 1.96 TeV. We select single-top-like data events in the lepton+jets decay channel and separate them from backgrounds using the matrix element analysis method. This technique uses leading order matrix elements to compute an event probability for both signal and background hypotheses. Using the expected signal acceptance, background, and observed data we measure the single top quark cross section: {sigma}(p{bar p} {yields} tb + tqb + X) = 4.6{sub -1.5}{sup +}1.8 pb. The probability for the background to have fluctuated up to give at least the cross section measured in this analysis is 0.21%, which corresponds to a Gaussian equivalent significance of 2.9{sigma}.

The objective of this work is to investigate the thick diffusion limit of various spatial discretizations of the one-dimensional, steady-state, monoenergetic, discrete ordinates neutron transport equation. This work specifically addresses the two lowest order nodal methods, AHOT-N0 and AHOT-N1, as well as reconsiders the asymptotic limit of the Diamond Difference method. The asymptotic analyses of the AHOT-N0 and AHOT-N1 nodal methods show that AHOT-N0 does not possess the thick diffusion limit for cell edge or cell average fluxes except under very limiting conditions, which is to be expected considering the AHOT-N0 method limits to the Step method in the thick diffusion limit. The AHOT-N1 method, which uses a linear in-cell representation of the flux, was shown to possess the thick diffusion limit for both cell average and cell edge fluxes. The thick diffusion limit of the DD method, including the boundary conditions, was derived entirely in terms of cell average scalar fluxes. It was shown that, for vacuum boundaries, only when {sigma}{sub t}, h, and Q are constant and {sigma}{sub a} = 0 is the asymptotic limit of the DD method close to the finite-differenced diffusion equation in the system interior, and that the boundary conditions between the systems will …

Since the X(3872) was discovered by the Belle Collaboration in August 2003, it's interpretation through the Standard Model has been difficult. Many possible interpretations have been proposed due to its close proximity to the D{bar D}* mass threshold, ranging from a new state in the charmonium spectrum to a 4-quark state to a weakly bound meson molecule. Probing the X(3872) is also made difficult due to low statistical samples at e{sup +}e{sup -} colliders and large combinatoric backgrounds at hadron colliders such as the Tevatron. This paper presented the results of probes of this state performed at the D0 detector.

We present a search for B{sub S}{sup 0} oscillations using semileptonic B{sub S} {yields} D{sub s}{mu}X (D{sub S} {yields} K{sub S}{sup 0}K). The data were collected using the D0 detector from events produced in {radical}s = 1.96 TeV proton-antiproton collisions at the Fermilab Tevatron. The Tevatron is currently the only place in the world that produces B{sub S}{sup 0} mesons and will be until early 2008 when the Large Hadron Collider begins operating at CERN. One of the vital ingredients for the search for B s oscillations is the determination of the flavor of the B{sub S}{sup 0} candidate (B{sub S}{sup 0} or {bar B}{sub S}{sup 0} ) at the time of its production, called initial state flavor tagging. We develop an likelihood based initial state flavor tagger that uses objects on the side of the event opposite to the reconstructed B meson candidate. To improve the performance of this flavor tagger, we have made it multidimensional so that it takes correlations between discriminants into account. This tagging is then certified by applying it to sample of semimuonic B{sup (0,+)} decays and measuring the well-known oscillation frequency {delta}m{sub d}. We obtain {delta}m{sub d} = 0.486 {+-} 0.021 ps{sup -1}, consistent …

We present a measurement of the mass of the top quark using data from proton-antiproton collisions recorded at the CDF experiment in Run II of the Fermilab Tevatron. Events are selected from the single lepton plus jets final state (t{bar t} {yields} W{sup +}bW{sup -}{bar b} {yields} {ell}{nu}bq{bar q}{prime}{bar b}). The top quark mass is extracted using a calculation of the probability density for a t{bar t} final state to resemble a data event. This probability density is a function of both top quark mass and energy scale of calorimeter jets, constrained in situ with the hadronic W boson mass. Using 167 events observed in 955 pb{sup -1} integrated luminosity, we achieve the single most precise measurement of top quark mass to date of 170.8 {+-} 2.2 (stat.) {+-} 1.4 (syst.) GeV/c{sup 2}, where the quoted statistical uncertainty includes uncertainty from the determination of the jet energy scale.

Search engines, such as Google, assign scores to news articles based on their relevancy to a query. However, not all relevant articles for the query may be interesting to a user. For example, if the article is old or yields little new information, the article would be uninteresting. Relevancy scores do not take into account what makes an article interesting, which would vary from user to user. Although methods such as collaborative filtering have been shown to be effective in recommendation systems, in a limited user environment there are not enough users that would make collaborative filtering effective. I present a general framework for defining and measuring the ''interestingness'' of articles, called iScore, incorporating user-feedback including tracking multiple topics of interest as well as finding interesting entities or phrases in a complex relationship network. I propose and have shown the validity of the following: 1. Filtering based on only topic relevancy is insufficient for identifying interesting articles. 2. No single feature can characterize the interestingness of an article for a user. It is the combination of multiple features that yields higher quality results. For each user, these features have different degrees of usefulness for predicting interestingness. 3. Through user-feedback, a …