Related papers: Identification of Patterns in Cosmic-Ray Arrival D…
We present a novel method to search for structures of coherently aligned patterns in ultra-high energy cosmic-ray arrival directions simultaneously across the entire sky. This method can be used to obtain information on the Galactic…
We present a method to correct for deflections of ultra-high energy cosmic rays in the galactic magnetic field. We perform these corrections by simulating the expected arrival directions of protons using a parameterization of the field…
We present a method to analyse arrival directions of ultra-high-energy cosmic rays (UHECRs) using a classifier defined by a deep convolutional neural network trained on a HEALPix grid. To illustrate a high effectiveness of the method, we…
Upcoming cosmological surveys will provide unprecedented amount of data, which will require innovative statistical methods to maximize the scientific exploitation. Standard cosmological analyses based on abundances, two-point and…
From deflections in galactic and extragalactic magnetic fields energy dependent structures in the arrival directions of ultra-high energy cosmic rays (UHECR) are expected. We propose to characterize these structures by the strength of…
Detecting and characterizing the anisotropy pattern of the arrival directions of the highest energy cosmic rays are crucial steps towards the identification of their sources. We discuss a possible distortion of the cosmic ray flux induced…
Substantial amount of information both on the source and on characteristics of intercepting magnetic fields is encoded in the distribution in arrival times, directions, and energies of charged ultra-high energy cosmic rays from discrete…
Ultra-high energy cosmic rays are mostly charged particles and they are therefore deflected by magnetic fields on their path from their sources to Earth. An interesting phenomenon arising from these deflections is the appearance of multiple…
Using advanced machine learning techniques, we developed a method for reconstructing precisely the arrival direction and energy of ultra-high-energy cosmic rays from the voltage traces they induced on ground-based radio detector arrays. In…
We propose a new way to detect individual bright Ultra-High Energy Cosmic Ray (UHECR) sources above background if the Galactic Magnetic Field (GMF) gives the main contribution to UHECR deflections. This method can be directly applied to…
We introduce a novel method for identifying the mass composition of ultra-high-energy cosmic rays using deep learning. The key idea of the method is to use a chain of two neural networks. The first network predicts the type of a primary…
Most existing star-galaxy classifiers use the reduced summary information from catalogs, requiring careful feature extraction and selection. The latest advances in machine learning that use deep convolutional neural networks allow a machine…
The deflection of ultra-high energy cosmic rays depends on the shape of the injection spectrum of the source and the pervasive cosmic magnetic fields. In this work it is applied the wavelet transform on the sphere to search for energy…
At GeV energies, the sky is dominated by the interstellar emission from the Galaxy. With limited statistics and spatial resolution, accurately separating point sources is therefore challenging. Here we present the first application of deep…
We propose a machine learning method to investigate the propagation of cosmic rays based on the precisely measured spectra of the primary and secondary cosmic ray nuclei of Li, Be, B, C, and O from AMS-02, ACE, and Voyager-1. We train two…
The coarse-grained propagation of Galactic cosmic rays (CRs) is traditionally constrained by phenomenological models of Milky Way CR propagation fit to a variety of direct and indirect observables; however, constraining the fine-grained…
Recent studies, supported by updated hadronic interaction models, suggest that the mass composition of ultra-high-energy cosmic rays may be heavier than previously assumed. This has significant implications for source identification, as the…
Cosmic ray astronomy attempts to identify and study the sources of ultrahigh energy cosmic rays. It is unique in its reliance on charged particles as the information carriers. While no discrete source of ultrahigh energy cosmic rays has…
We demonstrate the potential of Deep Learning methods for measurements of cosmological parameters from density fields, focusing on the extraction of non-Gaussian information. We consider weak lensing mass maps as our dataset. We aim for our…
The ability to discover new transients via image differencing without direct human intervention is an important task in observational astronomy. For these kind of image classification problems, machine Learning techniques such as…