Mass of the lightest Higgs Boson in Supersymmetric Left-Right Models

We consider the lightest Higgs boson in naturally R-parity conserving supersymmetric left-right models. We obtain an upper bound on the tree level mass of this lightest Higgs boson. This upper bound depends on the $SU(2)_L$ and $SU(2)_R$ gauge couplings, and the vacuum expectation values of bidoublet Higgs fields, which are needed to break $SU(2)_L\times U(1)_Y$. The upper bound does not depend on either the $SU(2)_R$ breaking scale or the supersymmetry breaking scale. We evaluate the bound numerically by assuming that the theory remains perturbative upto some scale $\Lambda$. We find that the bound can be considerably larger than in MSSM. The dominant radiative corrections to the upper bound due to top-stop and bottom-sbottom systems are of the same form as in the minimal supersymmetric standard model.Comment: 14 pages including 2 figures, LaTe

Phenomenology of SUSY-models with spontaneously broken R-parity

We review the consequences of spontaneously broken R-parity in present and planned lepton-lepton colliders. In the left-right models the R-parity, $R=(-1)^{3(B-L)+2S}$, is preserved due to the gauge symmetry, but it must be spontaneously broken in order to the scalar spectrum to be physically consistent. The spontaneous breaking is generated via a non-vanishing VEV of at least one of the sneutrinos, which necessarily means non-conservation of lepton number $L$. The R-parity violating couplings are parametrized in terms of mixing angles, whose values depend on model parameters. Combined with the constraints derived from low-energy measurements this yields allowed ranges for various R-parity breaking couplings. The R-parity breaking allows for the processes in which a single chargino or neutralino is produced, subsequently decaying at the interaction point to non-supersymmetric particles.Comment: 6 pages, Latex, talk given in Beyond the Standard Model V in Balholm, Norwa

Multiple Hypothesis Testing in Pattern Discovery

The problem of multiple hypothesis testing arises when there are more than one hypothesis to be tested simultaneously for statistical significance. This is a very common situation in many data mining applications. For instance, assessing simultaneously the significance of all frequent itemsets of a single dataset entails a host of hypothesis, one for each itemset. A multiple hypothesis testing method is needed to control the number of false positives (Type I error). Our contribution in this paper is to extend the multiple hypothesis framework to be used with a generic data mining algorithm. We provide a method that provably controls the family-wise error rate (FWER, the probability of at least one false positive) in the strong sense. We evaluate the performance of our solution on both real and generated data. The results show that our method controls the FWER while maintaining the power of the test.Comment: 28 page

Subjectively Interesting Subgroup Discovery on Real-valued Targets

Deriving insights from high-dimensional data is one of the core problems in data mining. The difficulty mainly stems from the fact that there are exponentially many variable combinations to potentially consider, and there are infinitely many if we consider weighted combinations, even for linear combinations. Hence, an obvious question is whether we can automate the search for interesting patterns and visualizations. In this paper, we consider the setting where a user wants to learn as efficiently as possible about real-valued attributes. For example, to understand the distribution of crime rates in different geographic areas in terms of other (numerical, ordinal and/or categorical) variables that describe the areas. We introduce a method to find subgroups in the data that are maximally informative (in the formal Information Theoretic sense) with respect to a single or set of real-valued target attributes. The subgroup descriptions are in terms of a succinct set of arbitrarily-typed other attributes. The approach is based on the Subjective Interestingness framework FORSIED to enable the use of prior knowledge when finding most informative non-redundant patterns, and hence the method also supports iterative data mining.Comment: 12 pages, 10 figures, 2 tables, conference submissio