A Rotating Charged Black Hole Solution in f(R) Gravity

In the context of f(R) theories of gravity, we address the problem of finding a rotating charged black hole solution in the case of constant curvature. The new metric is obtained by solving the field equations and we show that the behavior of it is typical of a rotating charged source. In addition, we analyze the thermodynamics of the new black hole. The results ensures that the thermodynamical properties in f(R) gravities are qualitatively similar to those of standard General Relativity.Comment: 9 pages, no figure

Bayesian model selection for electromagnetic kaon production on the nucleon

We present the results of a Bayesian analysis of a Regge model to describe the background contribution for K+ Lambda and K+ Sigma0 photoproduction. The model is based on the exchange of K+(494) and K*+(892) trajectories in the t-channel. We utilise the Bayesian evidence Z to determine the best model variant for each channel. The Bayesian evidence integrals were calculated using the Nested Sampling algorithm. For different prior widths, we find decisive Bayesian evidence (\Delta ln Z ~ 24) for a K+ Lambda photoproduction Regge model with a positive vector coupling and a negative tensor coupling constant for the K*+(892) trajectory, and a rotating phase factor for both trajectories. Using the chi^2 minimisation method, one could not draw this conclusion from the same dataset. For the K+ Sigma0 photoproduction Regge model, on the other hand, the difference between the evidence integrals is insufficient to pinpoint one model variant.Comment: 13 pages, 4 figure

Regge-model predictions for K+Sigma photoproduction from the nucleon

We present Regge-model predictions for the p(gamma,K+)Sigma0 and n(gamma,K+)Sigma- differential cross sections and photon-beam asymmetries in the resonance region. The reaction amplitude encompasses the exchange of K+(494) and K*+(892) Regge-trajectories, introducing a mere three free parameters. These are fitted to the available p(gamma,K+)Sigma0 data beyond the resonance region. The n(gamma,K+)Sigma- amplitude is obtained from the p(gamma,K+)Sigma0 one through SU(2) isospin symmetry considerations.Comment: 4 pages, 2 figures; Proceedings Tenth Conference on the Intersections of Particle and Nuclear Physics, San Diego, 200

Modeling and visualizing uncertainty in gene expression clusters using Dirichlet process mixtures

Although the use of clustering methods has rapidly become one of the standard computational approaches in the literature of microarray gene expression data, little attention has been paid to uncertainty in the results obtained. Dirichlet process mixture (DPM) models provide a nonparametric Bayesian alternative to the bootstrap approach to modeling uncertainty in gene expression clustering. Most previously published applications of Bayesian model-based clustering methods have been to short time series data. In this paper, we present a case study of the application of nonparametric Bayesian clustering methods to the clustering of high-dimensional nontime series gene expression data using full Gaussian covariances. We use the probability that two genes belong to the same cluster in a DPM model as a measure of the similarity of these gene expression profiles. Conversely, this probability can be used to define a dissimilarity measure, which, for the purposes of visualization, can be input to one of the standard linkage algorithms used for hierarchical clustering. Biologically plausible results are obtained from the Rosetta compendium of expression profiles which extend previously published cluster analyses of this data