Single Top Quarks at the Tevatron

After many years searching for electroweak production of top quarks, the Tevatron collider experiments have now moved from obtaining first evidence for single top quark production to an impressive array of measurements that test the standard model in several directions. This paper describes measurements of the single top quark cross sections, limits set on the CKM matrix element |Vtb|, searches for production of single top quarks produced via flavor-changing neutral currents and from heavy W-prime and H+ boson resonances, and studies of anomalous Wtb couplings. It concludes with projections for future expected significance as the analyzed datasets grow

Observation of Single Top Quark Production

The field of experimental particle physics has become more sophisticated over time, as fewer, larger experimental collaborations search for small signals in samples with large components of background. The search for and the observation of electroweak single top quark production by the CDF and DZero collaborations at Fermilab's Tevatron collider are an example of an elaborate effort to measure the rate of a very rare process in the presence of large backgrounds and to learn about the properties of the top quark's weak interaction. We present here the techniques used to make this groundbreaking measurement and the interpretation of the results in the context of the Standard Model.Comment: 33 pages, 14 figures, 4 tables, to appear in Annual Review of Nuclear and Particle Science, Vol. 61, November 201

Enhancing the Biological Relevance of Machine Learning Classifiers for Reverse Vaccinology.

Reverse vaccinology (RV) is a bioinformatics approach that can predict antigens with protective potential from the protein coding genomes of bacterial pathogens for subunit vaccine design. RV has become firmly established following the development of the BEXSERO® vaccine against Neisseria meningitidis serogroup B. RV studies have begun to incorporate machine learning (ML) techniques to distinguish bacterial protective antigens (BPAs) from non-BPAs. This research contributes significantly to the RV field by using permutation analysis to demonstrate that a signal for protective antigens can be curated from published data. Furthermore, the effects of the following on an ML approach to RV were also assessed: nested cross-validation, balancing selection of non-BPAs for subcellular localization, increasing the training data, and incorporating greater numbers of protein annotation tools for feature generation. These enhancements yielded a support vector machine (SVM) classifier that could discriminate BPAs (n = 200) from non-BPAs (n = 200) with an area under the curve (AUC) of 0.787. In addition, hierarchical clustering of BPAs revealed that intracellular BPAs clustered separately from extracellular BPAs. However, no immediate benefit was derived when training SVM classifiers on data sets exclusively containing intra- or extracellular BPAs. In conclusion, this work demonstrates that ML classifiers have great utility in RV approaches and will lead to new subunit vaccines in the future

Position measurement in the ALEPH inner tracking chamber

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Enhancing the biological relevance of machine learning classifiers for reverse vaccinology

Reverse vaccinology (RV) is a bioinformatics approach that can predict antigens with protective potential from the protein coding genomes of bacterial pathogens for subunit vaccine design. RV has become firmly established following the development of the BEXSERO® vaccine against Neisseria meningitidis serogroup B. RV studies have begun to incorporate machine learning (ML) techniques to distinguish bacterial protective antigens (BPAs) from non-BPAs. This research contributes significantly to the RV field by using permutation analysis to demonstrate that a signal for protective antigens can be curated from published data. Furthermore, the effects of the following on an ML approach to RV were also assessed: nested cross-validation, balancing selection of non-BPAs for subcellular localization, increasing the training data, and incorporating greater numbers of protein annotation tools for feature generation. These enhancements yielded a support vector machine (SVM) classifier that could discriminate BPAs (n = 200) from non-BPAs (n = 200) with an area under the curve (AUC) of 0.787. In addition, hierarchical clustering of BPAs revealed that intracellular BPAs clustered separately from extracellular BPAs. However, no immediate benefit was derived when training SVM classifiers on data sets exclusively containing intra- or extracellular BPAs. In conclusion, this work demonstrates that ML classifiers have great utility in RV approaches and will lead to new subunit vaccines in the future

Top Quark Physics: Future Measurements

We discuss the study of the top quark at future experiments and machines. Top’s large mass makes it a unique probe of physics at the natural electroweak scale. We emphasize measurements of the top quark’s mass, width, and couplings, as well as searches for rare or nonstandard decays, and discuss the complementary roles played by hadron and lepton colliders. I