Related papers: Neural Network for Simulating Radio Emission from …
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…
Surface radio antenna-based measurements of cosmic-ray air showers present significant computational challenges in accurately reconstructing physics observables, in particular, the depth of shower maximum, X$_{max}$. State-of-the-art…
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…
The burst of radio emission by the extensive air shower provides a promising alternative for detecting ultra-high energy cosmic rays.We have developed an independent numerical program to simulate these radio signals. Our code is based on a…
Simulations of the radio emission from extensive air showers have been key in establishing radio detection as a mature and competitive technique. In particular, microscopic Monte Carlo simulations have proven to very accurately describe the…
The radio detection method for cosmic rays relies on coherent emission from electrons and positrons which is beamed in a narrow cone along the shower axis. Currently the only mod- els to reproduce this emission with sufficient accuracy are…
A prototype system for detecting radio pulses associated with extensive cosmic ray air showers is described. Sensitivity is compared with that in previous experiments, and lessons are noted for future studies.
Over the last decades, radio detection of air showers has been established as a promising detection technique for ultrahigh-energy cosmic rays and neutrinos. Very large or dense antenna arrays are necessary to be proficient at collecting…
Radio emission from cosmic ray air showers has the potential to become an additional, cost-effective observing technique for cosmic ray research, being largely complementary to the well-established particle detector and air fluorescence…
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…
In that paper we discuss possibilities of using the Artificial Neural Network technic for the individual Extensive Air Showers data evaluation. It is shown that the recently developed new computational methods can be used in studies of EAS…
A precise understanding of the radio emission from extensive air showers is of fundamental importance for the design of cosmic ray radio detectors as well as the analysis and interpretation of their data. In recent years, tremendous…
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…
A new SPHERE-3 telescope is being developed for cosmic rays spectrum and mass composition studies in the 5--1000 PeV energy range. Registration of extensive air showers using reflected Cherenkov light method applied in the SPHERE detector…
Firm evidence for a radio emission counterpart of cosmic ray air showers is presented. By the use of an antenna array set up in coincidence with ground particle detectors, we find a collection of events for which both time and arrival…
The detection of air-shower events via radio signals requires to develop a trigger algorithm for a clean discrimination between signal and background events in order to reduce the data stream coming from false triggers. In this contribution…
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…
We present a macroscopic calculation of coherent electro-magnetic radiation from air showers initiated by ultra-high energy cosmic rays, based on currents obtained from Monte Carlo simulations of air showers in a realistic geo-magnetic…
We present a neural net algorithm for parameter estimation in the context of large cosmological data sets. Cosmological data sets present a particular challenge to pattern-recognition algorithms since the input patterns (galaxy redshift…