Related papers: Inverting cosmic ray propagation by Convolutional …
Recent success in training deep neural networks have prompted active investigation into the features learned on their intermediate layers. Such research is difficult because it requires making sense of non-linear computations performed by…
Recent advances in both the MHD turbulence theory and cosmic ray observations call for revisions in the paradigm of cosmic ray transport. We use the models of magnetohydrodynamic turbulence that were tested in numerical simulation, in which…
After the successful detection of cosmic high-energy neutrinos, the field of multiwavelength photon studies of active galactic nuclei (AGN) is entering an exciting new phase. The first hint of a possible neutrino signal from the blazar TXS…
We apply and compare various Artificial Neural Network (ANN) and other algorithms for automatic morphological classification of galaxies. The ANNs are presented here mathematically, as non-linear extensions of conventional statistical…
We employ a data-driven approach to investigate the rigidity and spatial dependence of the diffusion of cosmic rays in the turbulent magnetic field of the Milky Way. Our analysis combines data sets from the experiments Voyager, AMS-02,…
The Galactic cosmic-ray propagation code GALPROP is designed to make predictions of many kinds of data self-consistently, including direct cosmic-ray measurements, gamma rays and synchrotron radiation. In the decade since its conception it…
Precise measurements of energy spectra of different cosmic ray species were obtained in recent years, by particularly the AMS-02 experiment on the International Space Station. It has been shown that apparent differences exist in different…
The secondary-to-primary B/C ratio is widely used to study Galactic cosmic-ray propagation processes. The 2H/4He and 3He/4He ratios probe a different Z/A regime, therefore testing the `universality' of propagation. We revisit the…
Reproducibility of a deep-learning fully convolutional neural network is evaluated by training several times the same network on identical conditions (database, hyperparameters, hardware) with non-deterministic Graphics Processings Unit…
To maximize the accuracy of background simulation and event reconstruction, high-energy neutrino telescopes require detailed knowledge of light propagation over a large volume of detection medium. If light scattering and absorption leng ths…
Geophysical inversion attempts to estimate the distribution of physical properties in the Earth's interior from observations collected at or above the surface. Inverse problems are commonly posed as least-squares optimization problems in…
Data from the Voyager probes have provided us with the first measurement of cosmic ray intensities at MeV energies, an energy range which had previously not been explored. Simple extrapolations of models that fit data at GeV energies, e.g.…
Astrophysical processes such as feedback from supernovae and active galactic nuclei modify the properties and spatial distribution of dark matter, gas, and galaxies in a poorly understood way. This uncertainty is one of the main theoretical…
We briefly describe the energy loss processes of ultrahigh energy protons, heavier nuclei and gamma rays in interactions with the universal photon fields of the Universe. We then discuss the modification of the accelerated cosmic ray energy…
Many experiments have confirmed the spectral hardening in a few hundred GV of cosmic ray (CR) nuclei spectra, and 3 different origins have been proposed: the primary source acceleration, the propagation, and the superposition of different…
We have made a new calculation of the cosmic ray electron spectrum using an anomalous diffusion model to describe the propagation of electrons in the Galaxy. The parameters defining the anomalous diffusion have been recently determined from…
Inverse problems exist in many domains such as phase imaging, image processing, and computer vision. These problems are often solved with application-specific algorithms, even though their nature remains the same: mapping input image(s) to…
We aim to prepare the machine-learning ground for the next generation of spectroscopic surveys, such as 4MOST and WEAVE. Our goal is to show that convolutional neural networks can predict accurate stellar labels from relevant spectral…
Recent precise measurements of cosmic ray spectral revealed an anomalous hardening at ~200 GV for nuclei from PAMELA, CREAM, ATIC, AMS02 experiments and at tens of GeV for primary electron derived from AMS02 experiment. Particularly, the…
In many tasks, in particular in natural science, the goal is to determine hidden system parameters from a set of measurements. Often, the forward process from parameter- to measurement-space is a well-defined function, whereas the inverse…