Properties of the Interstellar Medium and the Propagation of Cosmic Rays in the Galaxy

The problem of the origin of cosmic rays in the shocks produced by supernova explosions at energies below the so called 'knee' (at ~3*10$^6$ GeV) in the energy spectrum is addressed, with special attention to the propagation of the particles through the inhomogenious interstellar medium and the need to explain recent anisotropy results, [1]. It is shown that the fractal character of the matter density and magnetic field distribution leads to the likelihood of a substantial increase of spatial fluctuations in the cosmic ray energy spectra. While the spatial distribution of cosmic rays in the vicinity of their sources (eg. inside the Galactic disk) does not depend much on the character of propagation and is largely determined by the distribution of their sources, the distribution at large distances from the Galactic disk depends strongly on the character of the propagation. In particular, the fractal character of the ISM leads to what is known as 'anomalous diffusion' and such diffusion helps us to understand the formation of Cosmic Ray Halo. Anomalous diffusion allows an explanation of the recent important result from the Chacaltaya extensive air shower experiment [1], viz. a Galactic Plane Enhancement of cosmic ray intensity in the Outer Galaxy, which is otherwise absent for the case of the so-called 'normal' diffusion. All these effects are for just one reason: anomalous diffusion emphasizes the role of local phenomena in the formation of cosmic ray characteristics in our Galaxy and elsewhere.Comment: 18 pages, 5 figures, accepted by Astropartoicle Physic

Physical Processes in Star Formation

© 2020 Springer-Verlag. The final publication is available at Springer via https://doi.org/10.1007/s11214-020-00693-8.Star formation is a complex multi-scale phenomenon that is of significant importance for astrophysics in general. Stars and star formation are key pillars in observational astronomy from local star forming regions in the Milky Way up to high-redshift galaxies. From a theoretical perspective, star formation and feedback processes (radiation, winds, and supernovae) play a pivotal role in advancing our understanding of the physical processes at work, both individually and of their interactions. In this review we will give an overview of the main processes that are important for the understanding of star formation. We start with an observationally motivated view on star formation from a global perspective and outline the general paradigm of the life-cycle of molecular clouds, in which star formation is the key process to close the cycle. After that we focus on the thermal and chemical aspects in star forming regions, discuss turbulence and magnetic fields as well as gravitational forces. Finally, we review the most important stellar feedback mechanisms.Peer reviewedFinal Accepted Versio

Highly-parallelized simulation of a pixelated LArTPC on a GPU

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on 10^3 pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype

In vitro frictional behavior and wear patterns between contemporary and aesthetic composite orthodontic brackets and archwires

10.1016/j.wear.2006.03.035Wear261101121-1139WEAR

Extrapolation of sparse tensor fields: application to the modeling of brain variability.

International audienceModeling the variability of brain structures is a fundamental problem in the neurosciences. In this paper, we start from a dataset of precisely delineated anatomical structures in the cerebral cortex: a set of 72 sulcal lines in each of 98 healthy human subjects. We propose an original method to compute the average sulcal curves, which constitute the mean anatomy in this context. The second order moment of the sulcal distribution is modeled as a sparse field of covariance tensors (symmetric, positive definite matrices). To extrapolate this information to the full brain, one has to overcome the limitations of the standard Euclidean matrix calculus. We propose an affine-invariant Riemannian framework to perform computations with tensors. In particular, we generalize radial basis function (RBF) interpolation and harmonic diffusion PDEs to tensor fields. As a result, we obtain a dense 3D variability map which proves to be in accordance with previously published results on smaller samples subjects. Moreover, leave one (sulcus) out tests show that our model is globally able to recover the missing information when there is a consistent neighboring variability. Last but not least, we propose innovative methods to analyze the asymmetry of brain variability. As expected, the greatest asymmetries are found in regions that includes the primary language areas. Interestingly, such an asymmetry in anatomical variance could explain why there may be greater power to detect group activation in one hemisphere than the other in fMRI studies