Determination of Fundamental Supersymmetry Parameters from Chargino Production at Lepii

If accessible at LEP II, chargino production is likely to be one of the few available supersymmetric signals for many years. We consider the prospects for the determination of fundamental supersymmetry parameters in such a scenario. The study is complicated by the dependence of observables on a large number of these parameters. We propose a straightforward procedure for disentangling these dependences and demonstrate its effectiveness by presenting a number of case studies at representative points in parameter space. Working in the context of the minimal supersymmetric standard model, we find that chargino production by itself is a fairly sensitive probe of the supersymmetry-breaking sector. For significant regions of parameter space, it is possible to test the gaugino mass unification hypothesis and to measure the gaugino contents of the charginos and neutralinos, thereby testing the predictions of grand unification and the viability of the lightest supersymmetric particle as a dark matter candidate. For much of the parameter space, it is also possible to set limits on the mass of the electron sneutrino, which provide a valuable guide for future particle searches.Comment: 52pp, Revtex, 30 figures available upon request, SLAC-PUB-6497, RU-94-67 (text and figures available in ps form by anonymous ftp from preprint.slac.stanford.edu, directory pub/preprints/hep-ph/9408

Parsimonious Higher-Order Hidden Markov Models for Improved Array-CGH Analysis with Applications to Arabidopsis thaliana

Array-based comparative genomic hybridization (Array-CGH) is an important technology in molecular biology for the detection of DNA copy number polymorphisms between closely related genomes. Hidden Markov Models (HMMs) are popular tools for the analysis of Array-CGH data, but current methods are only based on first-order HMMs having constrained abilities to model spatial dependencies between measurements of closely adjacent chromosomal regions. Here, we develop parsimonious higher-order HMMs enabling the interpolation between a mixture model ignoring spatial dependencies and a higher-order HMM exhaustively modeling spatial dependencies. We apply parsimonious higher-order HMMs to the analysis of Array-CGH data of the accessions C24 and Col-0 of the model plant Arabidopsis thaliana. We compare these models against first-order HMMs and other existing methods using a reference of known deletions and sequence deviations. We find that parsimonious higher-order HMMs clearly improve the identification of these polymorphisms. Moreover, we perform a functional analysis of identified polymorphisms revealing novel details of genomic differences between C24 and Col-0. Additional model evaluations are done on widely considered Array-CGH data of human cell lines indicating that parsimonious HMMs are also well-suited for the analysis of non-plant specific data. All these results indicate that parsimonious higher-order HMMs are useful for Array-CGH analyses. An implementation of parsimonious higher-order HMMs is available as part of the open source Java library Jstacs (www.jstacs.de/index.php/PHHMM)