Search for the Production of Scalar Bottom Quarks in pp̅ Collisions at √s=1.96 TeV

We report on a search for direct scalar bottom quark (sbottom) pair production in $p \bar{p}$ collisions at $\sqrt{s}=1.96$~TeV, in events with large missing transverse energy and two jets of hadrons in the final state, where at least one of the jets is required to be identified as originating from a $b$ quark. The study uses a CDF Run~II data sample corresponding to 2.65~fb${}^{-1}$ of integrated luminosity. The data are in agreement with the standard model. In an R-parity conserving minimal supersymmetric scenario, and assuming that the sbottom decays exclusively into a bottom quark and a neutralino, 95$\%$ confidence-level upper limits on the sbottom pair production cross section of 0.1~pb are obtained. For neutralino masses below 70~GeV/$c^2$, sbottom masses up to 230~GeV/$c^2$ are excluded at 95$\%$ confidence level.

Search for Gluino-Mediated Bottom Squark Production in pp̅ Collisions at √s=1.96 TeV

We report on a search for the supersymmetric partner of the bottom quark produced from gluino decays in data from 2.5 fb-1 of integrated luminosity collected by the Collider Detector at Fermilab at sqrt(s)=1.96 TeV. Candidate events are selected requiring two or more jets and large missing transverse energy. At least two of the jets are required to be tagged as originating from a b quark to enhance the sensitivity. The results are in good agreement with the prediction of the standard model processes, giving no evidence for gluino decay to sbottom quarks. This result constrains the gluino-pair-production cross section to be less than 40fb at 95% credibility level for a gluino mass of 350 GeV.

Observation of New Charmless Decays of Bottom Hadrons

We search for new charmless decays of neutral b-hadrons to pairs of charged hadrons with the upgraded Collider Detector at the Fermilab Tevatron. Using a data sample corresponding to 1 fb-1 of integrated luminosity, we report the first observation of the B0s->K-pi+ decay, with a significance of 8.2 sigma, and measure BR(B0s->K-pi+)= (5.0+-0.7(stat)+-0.8(syst))*10^{-6}. We also report the first observation of charmless b-baryon decays in the channels Lambda_b -> p pi and Lambda_b -> pK with significances of 6.0 sigma and 11.5 sigma respectively, and we measure BR(Lambda_b->p pi-) = (3.5+-0.6(stat)+-0.9(syst))*10^{-6} and BR(Lambda_b->p K-) = (5.6+-0.8(stat)+-1.5(syst))*10^{-6}. No evidence is found for the decays B0->K+K- and B0s -> pi+pi-, and we set an improved upper limit BR(B0s -> pi+pi-) K+pi-)$ as a reference.

Simulating and Forecasting the Demand for New Consumer Durables

A diffusion/replacement model for new consumer durables designed to be used as a long-term forecasting tool is developed. The model simulates new demand as well as replacement demand over time. The model is called DEMSIM and is built upon a counteractive adoption model specifying the basic forces affecting the adoption behaviour of individual consumers. These forces are the promoting forces and the resisting forces. The promoting forces are further divided into internal and external influences. These influences are operationalized within a multi-segmental diffusion model generating the adoption behaviour of the consumers in each segment as an expected value. This diffusion model is combined with a replacement model built upon the same segmental structure as the diffusion model. This model generates, in turn, the expected replacement behaviour in each segment. To be able to use DEMSIM as a forecasting tool in early stages of a diffusion process estimates of the model parameters are needed as soon as possible after product launch. However, traditional statistical techniques are not very helpful in estimating such parameters in early stages of a diffusion process. To enable early parameter calibration an optimization algorithm is developed by which the main parameters of the diffusion model can be estimated on the basis of very few sales observations. The optimization is carried out in iterative simulation runs. Empirical validations using the optimization algorithm reveal that the diffusion model performs well in early long-term sales forecasts, especially as it comes to the timing of future sales peaks.