High Energy Emission from the Starburst Galaxy NGC253

Measurement sensitivity in the energetic gamma-ray region has improved considerably, and is about to increase further in the near future, motivating a detailed calculation of high-energy (>100 MeV) and very-high-energy (VHE: >100 GeV) gamma-ray emission from the nearby starburst galaxy NGC253. Adopting the convection-diffusion model for energetic electron and proton propagation, and accounting for all the relevant hadronic and leptonic processes, we determine the steady-state energy distributions of these particles by a detailed numerical treatment. The electron distribution is directly normalized by the measured synchrotron radio emission from the central starburst region; a commonly expected theoretical relation is then used to normalize the proton spectrum in this region. Doing so fully specifies the electron spectrum throughout the galactic disk, and with an assumed spatial profile of the magnetic field, the predicted radio emission from the full disk matches well the observed spectrum, confirming the validity of our treatment. The resulting radiative yields of both particles are calculated; the integrated HE and VHE fluxes from the entire disk are predicted to be f(>100 MeV)~2x10^-8 cm^-2 s^-1 and f(>100 GeV)~4x10^-12 cm^-2 s^-1, respectively. We discuss the feasibility of measuring emission at these levels with the space-borne Fermi and the ground-based Cherenkov telescopes.Comment: 7 pages, 4 figures; accepted for publication in the MNRA

Comparison of Some Approaches to Determine Spatial Dependence of Soil Properties

Knowledge of variability and spatial structure of soil properties is essential for optimal design for collecting soil samples and effectively applying management decisions in the field. The objective of this study is to compare some approaches for characterizing, and comparing spatial dependence of isotropic second-order stationary processes. The evaluated approaches are the nugget to sill ratio (NR), normalized (by fitted sill) semivariogram, correlograms, and two integral scales. Soil samples, collected at a regular 50 m × 50 m grid from 0-15 cm depths, were analyzed for sand and clay, bulk density (b), saturated hydraulic conductivity (Ks), wilting point, available water content (AWC), pH, electrical conductivity (EC), nitrate-nitrogen (NO3- N), and chloride (Cl) were determined. Geostatistical software (GS+, Gamma Design Software, Plainwell, MI) was used to estimate the variance structure of various measured soil properties. Analysis include using data on the spatial variability of various properties from four published studies. NR displayed spatial dependence ignoring the influence of range, normalized semivariogram and correlogram provided the visual comparison, and both integral scales incorporated the influence of range and provided single number spatial dependence summaries. Either of the integral scale formulations can be used to characterize the spatial dependence of soil properties from agricultural fields

Location Dependent Dirichlet Processes

Dirichlet processes (DP) are widely applied in Bayesian nonparametric modeling. However, in their basic form they do not directly integrate dependency information among data arising from space and time. In this paper, we propose location dependent Dirichlet processes (LDDP) which incorporate nonparametric Gaussian processes in the DP modeling framework to model such dependencies. We develop the LDDP in the context of mixture modeling, and develop a mean field variational inference algorithm for this mixture model. The effectiveness of the proposed modeling framework is shown on an image segmentation task

A unifying representation for a class of dependent random measures

We present a general construction for dependent random measures based on thinning Poisson processes on an augmented space. The framework is not restricted to dependent versions of a specific nonparametric model, but can be applied to all models that can be represented using completely random measures. Several existing dependent random measures can be seen as specific cases of this framework. Interesting properties of the resulting measures are derived and the efficacy of the framework is demonstrated by constructing a covariate-dependent latent feature model and topic model that obtain superior predictive performance

Cell shape analysis of random tessellations based on Minkowski tensors

To which degree are shape indices of individual cells of a tessellation characteristic for the stochastic process that generates them? Within the context of stochastic geometry and the physics of disordered materials, this corresponds to the question of relationships between different stochastic models. In the context of image analysis of synthetic and biological materials, this question is central to the problem of inferring information about formation processes from spatial measurements of resulting random structures. We address this question by a theory-based simulation study of shape indices derived from Minkowski tensors for a variety of tessellation models. We focus on the relationship between two indices: an isoperimetric ratio of the empirical averages of cell volume and area and the cell elongation quantified by eigenvalue ratios of interfacial Minkowski tensors. Simulation data for these quantities, as well as for distributions thereof and for correlations of cell shape and volume, are presented for Voronoi mosaics of the Poisson point process, determinantal and permanental point processes, and Gibbs hard-core and random sequential absorption processes as well as for Laguerre tessellations of polydisperse spheres and STIT- and Poisson hyperplane tessellations. These data are complemented by mechanically stable crystalline sphere and disordered ellipsoid packings and area-minimising foam models. We find that shape indices of individual cells are not sufficient to unambiguously identify the generating process even amongst this limited set of processes. However, we identify significant differences of the shape indices between many of these tessellation models. Given a realization of a tessellation, these shape indices can narrow the choice of possible generating processes, providing a powerful tool which can be further strengthened by density-resolved volume-shape correlations.Comment: Chapter of the forthcoming book "Tensor Valuations and their Applications in Stochastic Geometry and Imaging" in Lecture Notes in Mathematics edited by Markus Kiderlen and Eva B. Vedel Jense

Modeling for seasonal marked point processes: An analysis of evolving hurricane occurrences

Seasonal point processes refer to stochastic models for random events which are only observed in a given season. We develop nonparametric Bayesian methodology to study the dynamic evolution of a seasonal marked point process intensity. We assume the point process is a nonhomogeneous Poisson process and propose a nonparametric mixture of beta densities to model dynamically evolving temporal Poisson process intensities. Dependence structure is built through a dependent Dirichlet process prior for the seasonally-varying mixing distributions. We extend the nonparametric model to incorporate time-varying marks, resulting in flexible inference for both the seasonal point process intensity and for the conditional mark distribution. The motivating application involves the analysis of hurricane landfalls with reported damages along the U.S. Gulf and Atlantic coasts from 1900 to 2010. We focus on studying the evolution of the intensity of the process of hurricane landfall occurrences, and the respective maximum wind speed and associated damages. Our results indicate an increase in the number of hurricane landfall occurrences and a decrease in the median maximum wind speed at the peak of the season. Introducing standardized damage as a mark, such that reported damages are comparable both in time and space, we find that there is no significant rising trend in hurricane damages over time.Comment: Published at http://dx.doi.org/10.1214/14-AOAS796 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org