Related papers: Machine learning driven reconstruction of cosmic-r…
We describe a method of reconstructing air showers induced by cosmic rays using deep learning techniques. We simulate an observatory consisting of ground-based particle detectors with fixed locations on a regular grid. The detector's…
The atmospheric depth of the air shower maximum $X_{\mathrm{max}}$ is an observable commonly used for the determination of the nuclear mass composition of ultra-high energy cosmic rays. Direct measurements of $X_{\mathrm{max}}$ are…
Cosmic ray shower detection using large radio arrays has gained significant traction in recent years. With massive improvements in signal modelling and microscopic simulations, the analysis of incoming events is still severely limited by…
Cosmic ray shower detection using large radio arrays has gained significant traction in recent years. With massive improvements in signal modelling and microscopic simulations, the analysis of incoming events is still severely limited by…
{Measurements of cosmic-ray composition based on air-shower measurements rely mostly on the determination of the position of the shower maximum ($X_\mathrm{max}$). One efficient technique is to image the development of the air shower using…
The mass composition of cosmic rays contains important clues about their origin. Accurate measurements are needed to resolve long-standing issues such as the transition from Galactic to extragalactic origin, and the nature of the cutoff…
The IceCube Neutrino Observatory at the geographic South Pole is, with its surface and in-ice detectors, used for both neutrino and cosmic-ray physics. The surface array, named IceTop, consists of ice-Cherenkov tanks grouped in 81 pairs…
Machine learning is a useful tool for identifying radio pulses from cosmic-ray air showers and for cleaning such pulses from radio background. This can lower the detection threshold and increase the accuracy for the pulse time and…
Deep neural networks are a powerful technique that have found ample applications in several branches of Physics. In this work, we apply machine learning algorithms to a specific problem of Cosmic Ray Physics: the estimation of the muon…
The Surface Enhancement of the IceTop air-shower array will include the addition of radio antennas and scintillator panels, co-located with the existing ice-Cherenkov tanks and covering an area of about 1 km$^2$. Together, these will…
We developed a radio interferometric technique for the observation of extensive air showers initiated by cosmic particles. In this proof-of-principle study we show that properties of extensive air showers can be derived with high accuracy…
We present an improved method for the precise reconstruction of cosmic ray air showers above $10^{17}$ eV with sparse radio arrays. The method is based on the comparison of predictions for radio pulse shapes by CoREAS simulations to…
A dramatic progress in the field of computer vision has been made in recent years by applying deep learning techniques. State-of-the-art performance in image recognition is thereby reached with Convolutional Neural Networks (CNNs). CNNs are…
Knowledge of the mass composition of ultra-high-energy cosmic rays is crucial to understanding their origins; however, current approaches have limited event-by-event resolution. With fluorescence telescope measurements of the longitudinal…
The standard method to estimate the mass of a cosmic ray is the measurement of the atmospheric depth of the shower maximum ($X_\text{max}$). This depth is strongly correlated with the mass of the primary because it depends on the…
Radio pulses generated by cosmic-ray air showers can be used to reconstruct key properties like the energy and depth of the electromagnetic component of cosmic-ray air showers. Radio detection threshold, influenced by natural and…
Radio emission from air showers enables measurements of cosmic particle kinematics and identity. The radio signals are detected in broadband Megahertz antennas among continuous background noise. We present two deep learning concepts and…
Multi-detector observations of individual air showers are critical to make significant progress to precisely determine cosmic-ray quantities such as mass and energy of individual events and thus bring us a step forward in answering the open…
We present a method to reconstruct the longitudinal profile of electrons in showers using Cherenkov telescopes. We show how the Cherenkov light collected by an array of telescopes can be transformed into the number of electrons as a…
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…