Search for standard model Higgs boson production in association with a W boson using a neural network discriminant at CDF

We present a search for standard model Higgs boson production in association with a W boson in proton-antiproton collisions at a center of mass energy of 1.96 TeV. The search employs data collected with the CDF II detector that correspond to an integrated luminosity of approximately 1.9 inverse fb. We select events consistent with a signature of a single charged lepton, missing transverse energy, and two jets. Jets corresponding to bottom quarks are identified with a secondary vertex tagging method, a jet probability tagging method, and a neural network filter. We use kinematic information in an artificial neural network to improve discrimination between signal and background compared to previous analyses. The observed number of events and the neural network output distributions are consistent with the standard model background expectations, and we set 95% confidence level upper limits on the production cross section times branching fraction ranging from 1.2 to 1.1 pb or 7.5 to 102 times the standard model expectation for Higgs boson masses from 110 to $150 GeV/c^2, respectively.

Automatic Lameness Detection in a Milking Robot : Instrumentation, measurement software, algorithms for data analysis and a neural network model

The aim of this thesis is to develop a fully automatic lameness detection system that operates in a milking robot. The instrumentation, measurement software, algorithms for data analysis and a neural network model for lameness detection were developed. Automatic milking has become a common practice in dairy husbandry, and in the year 2006 about 4000 farms worldwide used over 6000 milking robots. There is a worldwide movement with the objective of fully automating every process from feeding to milking. Increase in automation is a consequence of increasing farm sizes, the demand for more efficient production and the growth of labour costs. As the level of automation increases, the time that the cattle keeper uses for monitoring animals often decreases. This has created a need for systems for automatically monitoring the health of farm animals. The popularity of milking robots also offers a new and unique possibility to monitor animals in a single confined space up to four times daily. Lameness is a crucial welfare issue in the modern dairy industry. Limb disorders cause serious welfare, health and economic problems especially in loose housing of cattle. Lameness causes losses in milk production and leads to early culling of animals. These costs could be reduced with early identification and treatment. At present, only a few methods for automatically detecting lameness have been developed, and the most common methods used for lameness detection and assessment are various visual locomotion scoring systems. The problem with locomotion scoring is that it needs experience to be conducted properly, it is labour intensive as an on-farm method and the results are subjective. A four balance system for measuring the leg load distribution of dairy cows during milking in order to detect lameness was developed and set up in the University of Helsinki Research farm Suitia. The leg weights of 73 cows were successfully recorded during almost 10,000 robotic milkings over a period of 5 months. The cows were locomotion scored weekly, and the lame cows were inspected clinically for hoof lesions. Unsuccessful measurements, caused by cows standing outside the balances, were removed from the data with a special algorithm, and the mean leg loads and the number of kicks during milking was calculated. In order to develop an expert system to automatically detect lameness cases, a model was needed. A probabilistic neural network (PNN) classifier model was chosen for the task. The data was divided in two parts and 5,074 measurements from 37 cows were used to train the model. The operation of the model was evaluated for its ability to detect lameness in the validating dataset, which had 4,868 measurements from 36 cows. The model was able to classify 96% of the measurements correctly as sound or lame cows, and 100% of the lameness cases in the validation data were identified. The number of measurements causing false alarms was 1.1%. The developed model has the potential to be used for on-farm decision support and can be used in a real-time lameness monitoring system.

Search for WW and WZ production in lepton plus jets final state at CDF

We present a search for WW and WZ production in final states that contain a charged lepton (electron or muon) and at least two jets, produced in sqrt(s) = 1.96 TeV ppbar collisions at the Fermilab Tevatron, using data corresponding to 1.2 fb-1 of integrated luminosity collected with the CDF II detector. Diboson production in this decay channel has yet to be observed at hadron colliders due to the large single W plus jets background. An artificial neural network has been developed to increase signal sensitivity, as compared with an event selection based on conventional cuts. We set a 95% confidence level upper limit of sigma_{WW}* BR(W->lnu,W->jets)+ sigma_{WZ}*BR(W->lnu,Z->jets)

Search for Top-Quark Production via Flavor-Changing Neutral Currents in W+1 Jet Events at CDF

We report on the first search for top-quark production via flavor-changing neutral-current (FCNC) interactions in the non-standard-model process u(c)+g -> t using ppbar collision data collected by the CDF II detector. The data set corresponds to an integrated luminosity of 2.2/fb. The candidate events feature the signature of semileptonic top-quark decays and are classified as signal-like or background-like by an artificial neural network trained on simulated events. The observed discriminant distribution is in good agreement with the one predicted by the standard model and provides no evidence for FCNC top-quark production, resulting in a Bayesian upper limit on the production cross section sigma (u(c)+g -> t) u+g) c+g)