Particle Acceleration at Ultra-Relativistic Shocks and the Spectra of Relativistic Fireballs

We examine Fermi-type acceleration at relativistic shocks, and distinguish between the initial boost of the first shock crossing cycle, where the energy gain per particle can be very large, and the Fermi process proper with repeated shock crossings, in which the typical energy gain is of order unity. We calculate by means of numerical simulations the spectrum and angular distribution of particles accelerated by this Fermi process, in particular in the case where particle dynamics can be approximated as small-angle scattering. We show that synchrotron emission from electrons or positrons accelerated by this process can account remarkably well for the observed power-law spectra of GRB afterglows and Crab-like supernova remnants. In the context of a decelerating relativistic fireball, we calculate the maximum particle energy attainable by acceleration at the external blast wave, and discuss the minimum energy for this acceleration process and its consequences for the observed spectrum.Comment: To appear in Proceedings of the 5th Huntsville Gamma-Ray Burst Symposium. LaTeX, 6 pages, 2 figures, uses aipproc.sty and epsfi

An eigenfunction method for particle acceleration at ultra-relativistic shocks

We adapt and modify the eigenfunction method of computing the power-law spectrum of particles accelerated at a relativistic shock front via the first-order Fermi process (Kirk, J.G., Schneider, P., Astrophysical Journal 315, 425 (1987)) to apply to shocks of arbitrarily high Lorentz factor. The power-law index of accelerated particles undergoing isotropic small-angle scattering at an ultrarelativistic, unmagnetized shock is found to be s=4.23 +/- 0.2 (where s=d\ln f/ d\ln p, with f the Lorentz-invariant phase-space density and p the momentum), in agreement with the results of Monte-Carlo simulations. We present results for shocks in plasmas with different equations of state and for Lorentz factors ranging from 5 to infinity.Comment: 4 pages, 2 figures, contribution to the Proceedings of the 5th Huntsville GRB Symposiu

PrAGMATiC: a Probabilistic and Generative Model of Areas Tiling the Cortex

Much of the human cortex seems to be organized into topographic cortical maps. Yet few quantitative methods exist for characterizing these maps. To address this issue we developed a modeling framework that can reveal group-level cortical maps based on neuroimaging data. PrAGMATiC, a probabilistic and generative model of areas tiling the cortex, is a hierarchical Bayesian generative model of cortical maps. This model assumes that the cortical map in each individual subject is a sample from a single underlying probability distribution. Learning the parameters of this distribution reveals the properties of a cortical map that are common across a group of subjects while avoiding the potentially lossy step of co-registering each subject into a group anatomical space. In this report we give a mathematical description of PrAGMATiC, describe approximations that make it practical to use, show preliminary results from its application to a real dataset, and describe a number of possible future extensions

Pycortex: an interactive surface visualizer for fMRI.

Surface visualizations of fMRI provide a comprehensive view of cortical activity. However, surface visualizations are difficult to generate and most common visualization techniques rely on unnecessary interpolation which limits the fidelity of the resulting maps. Furthermore, it is difficult to understand the relationship between flattened cortical surfaces and the underlying 3D anatomy using tools available currently. To address these problems we have developed pycortex, a Python toolbox for interactive surface mapping and visualization. Pycortex exploits the power of modern graphics cards to sample volumetric data on a per-pixel basis, allowing dense and accurate mapping of the voxel grid across the surface. Anatomical and functional information can be projected onto the cortical surface. The surface can be inflated and flattened interactively, aiding interpretation of the correspondence between the anatomical surface and the flattened cortical sheet. The output of pycortex can be viewed using WebGL, a technology compatible with modern web browsers. This allows complex fMRI surface maps to be distributed broadly online without requiring installation of complex software

The Monoceros very-high-energy gamma-ray source

The H.E.S.S. telescope array has observed the complex Monoceros Loop SNR/Rosette Nebula region which contains unidentified high energy EGRET sources and potential very-high-energy (VHE) gamma-ray source. We announce the discovery of a new point-like VHE gamma-ray sources, HESS J0632+057. It is located close to the rim of the Monoceros SNR and has no clear counterpart at other wavelengths. Data from the NANTEN telescope have been used to investigate hadronic interactions with nearby molecular clouds. We found no evidence for a clear association. The VHE gamma-ray emission is possibly associated with the lower energy gamma-ray source 3EG J0634+0521, a weak X-ray source 1RXS J063258.3+054857 and the Be-star MWC 148.Comment: 4 pages, 4 figures, Contribution to the 30th ICRC, Merida Mexico, July 200

Dissociating visuo-spatial and verbal working memory: It’s all in the features

Echoing many of the themes of the seminal work of Atkinson and Shiffrin (1968), this paper uses the Feature Model (Nairne, 1988, 1990; Neath & Nairne, 1995) to account for performance in working memory tasks. The Brooks verbal and visuo-spatial matrix tasks were performed alone, with articulatory suppression, or with a spatial suppression task; the results produced the expected dissociation. We used Approximate Bayesian Computation techniques to fit the Feature Model to the data and showed that the similarity-based interference process implemented in the model accounted for the data patterns well. We then fit the model to data from Guérard and Tremblay (2008); the latter study produced a double dissociation while calling upon more typical order reconstruction tasks. Again, the model performed well. The findings show that a double dissociation can be modelled without appealing to separate systems for verbal and visuo-spatial processing. The latter findings are significant as the Feature Model had not been used to model this type of dissociation before; importantly, this is also the first time the model is quantitatively fit to data. For the demonstration provided here, modularity was unnecessary if two assumptions were made: (1) the main difference between spatial and verbal working memory tasks is the features that are encoded; (2) secondary tasks selectively interfere with primary tasks to the extent that both tasks involve similar features. It is argued that a feature-based view is more parsimonious (see Morey, 2018) and offers flexibility in accounting for multiple benchmark effects in the field