arXiv subjects
Publications and source records attributed to Kyle S. Cranmer.
The expected sensitivity of the LHC experiments to the discovery of the Higgs boson and the measurement of its properties is presented in the context of both the standard model and the its minimal supersymmetric extension. Prospects for a luminosity-upgraded ``Super-LHC'' are also presented.
Multivariate Analysis is an increasingly common tool in experimental high energy physics; however, many of the common approaches were borrowed from other fields. We clarify what the goal of a multivariate algorithm should be for the search for a new particle and compare different approaches. We also translate the Neyman-Pearson theory into the language of statistical learning theory.
We consider the standard Neyman-Pearson hypothesis test of a signal-plus-background hypothesis and background-only hypothesis in the presence of uncertainty on the background-only prediction. Surprisingly, this problem has not been addressed in the recent conferences on statistical techniques in high-energy physics -- although the its confidence-interval equivalent has been. We discuss the issues of power, similar tests, coverage, and ordering rules. The method presented is compared to the Cousins-Highland technique, the ratio of Poisson means, and ``profile'' method.
Kernel Estimation provides an unbinned and non-parametric estimate of the probability density function from which a set of data is drawn. In the first section, after a brief discussion on parametric and non-parametric methods, the theory of Kernel Estimation is developed for univariate and multivariate settings. The second section discusses some of the applications of Kernel Estimation to high-energy physics. The third section provides an overview of the available univariate and multivariate packages. This paper concludes with a discussion of the inherent advantages of kernel estimation techniques and systematic errors associated with the estimation of parent distributions.