Related papers: Inverting cosmic ray propagation by Convolutional …
Galactic rotation curves are crucial for understanding the distribution of mass in galaxies. Despite advances in precision observations, there are discrepancies between the inferred mass from luminosity and the observed rotational…
Traditionally, nonlinear inversion, direct inversion, or wave estimation methods have been used for reconstructing images from MRE displacement data. In this work, we propose a convolutional neural network architecture that can map MRE…
We have made a new calculation of the cosmic-ray secondary positron spectrum using a diffusive halo model for Galactic cosmic-ray propagation. The code computes self-consistently the spectra of primary and secondary nucleons, primary…
We study the impact of possible spiral-arm distributions of Galactic cosmic-ray sources on the flux of various cosmic-ray nuclei throughout our Galaxy. We investigate model cosmic-ray spectra at the nominal position of the sun and at…
We propose a machine learning approach to the blind detection of extragalactic point sources on maps of the temperature anisotropies of the cosmic microwave background. Using realistic simulations of the microwave sky as seen by Planck, we…
Precise measurements of the spectra of secondary and primary cosmic rays are crucial for understanding the origin and propagation of those energetic particles. The High Energy cosmic-Radiation Detection (HERD) facility onboard China`s Space…
We present a new machine learning model for estimating photometric redshifts with improved accuracy for galaxies in Pan-STARRS1 data release 1. Depending on the estimation range of redshifts, this model based on neural networks can handle…
This paper proposes a fractional order gradient method for the backward propagation of convolutional neural networks. To overcome the problem that fractional order gradient method cannot converge to real extreme point, a simplified…
We study the problem of reconstruction of high-energy cosmic rays mass composition from the experimental data of extensive air showers. We develop several machine learning methods for the reconstruction of energy spectra of separate primary…
We introduce a novel diffusion model for the propagation of cosmic rays (CRs) that incorporates an anisotropic diffusion tensor of a general form within a realistically modeled large-scale Galactic magnetic field. The parameters of the…
Propagation of cosmic rays (CRs) from their sources to the observer is described mainly as plain diffusion at high energies, while at lower energies there are other physical processes involved, both in the interstellar space and in the…
The AMS-02 experiment measured several secondary-to-primary ratios enabling a detailed study of Galactic cosmic-ray transport. We constrain previously derived benchmark scenarios (based on AMS-02 B/C data only) using other…
In this paper, we study the propagation of cosmic-ray nuclei and protons. We emphasize the influence of the source composition on the expected spectrum and composition on Earth as well as on the phenomenology of the transition from Galactic…
In a recent paper (Moskalenko et al., 2002), it has been shown that the flux of secondary cosmic ray (CR) antiprotons appears to be contradictory to measurements of secondary to primary nuclei ratios in cosmic rays when calculated in the…
The ratio between secondary and primary cosmic ray particles is the main source of information about cosmic ray propagation in the Galaxy. Primary cosmic rays are thought to be accelerated mainly in Supernova Remnant (SNR) shocks and then…
We use the GALPROP cosmic ray (CR) propagation framework to model the diffuse neutrino and gamma-ray emissions from the Galaxy. A collection of realistic bounding models are developed and predictions of the resulting neutrino and gamma-ray…
The description of the transport of cosmic rays in magnetized media is central to both acceleration and propagation of these particles in our Galaxy and outside. The investigation of the process of particle acceleration, especially at shock…
In this document, a neural network is employed in order to estimate the solution of the initial value problem in the context of non linear trajectories. Such trajectories can be subject to gravity, thrust, drag, centrifugal force,…
We establish a series of deep convolutional neural networks to automatically analyze position averaged convergent beam electron diffraction patterns. The networks first calibrate the zero-order disk size, center position, and rotation…
The excess of continuum gamma-ray emission from the Galaxy above 1 GeV is an unsolved puzzle. It may indicate that the interstellar nucleon or electron spectra are harder than local direct measurements, as could be the case if a local…