Reconstruction of air-shower parameters with a sparse radio array

The present study consists of two main parts: a theoretical description of the methods of air-shower reconstruction using the radio technique, and analysis of Tunka-Rex data using the developed methods

Classification and Denoising of Cosmic-Ray Radio Signals using Deep Learning

The radio detection technique, with advantages like inexpensive detector hardware and full year duty cycle, can prove to be a vital player in cosmic-ray detection at the highest energies and can lead us to the discovery of high energy particle accelerators in the universe. However, radio detection has to deal with continuous, irreducible background. The Galactic and thermal backgrounds, which contaminate the radio signal from air showers, lead to a relatively high detection threshold compared to other techniques. For the purpose of reducing the background, we employ a deep learning technique namely, convolutional neural networks (CNN). This technique has already proven to be efficient for radio pulse recognition e.g., in the Tunka-Rex experiment. We train CNNs on the radio signal and background to separate both from each other. The goal is to improve the radio detection threshold on the one hand, and on the other hand, increase the accuracy of the arrival time and amplitude of the radio pulses and consequently improve the reconstruction of the primary cosmic-ray properties. Here we present two different networks: a Classifier, which can be used to distinguish the radio signals from the pure background waveforms, and a Denoiser, which allows us to mitigate the background from the noisy traces and hence recover the underlying radio signal

Development of Self-Trigger Algorithms for Radio Detection of Air-Showers

The detection of extensive air-showers with radio method isa relatively young. But promising branch in experimental astrophysics ofultrahigh energies. This method allows one to carry out observations re-gardless of weather conditions and time of day, and the precision of recon-struction of the properties of primary particles is comparable to the clas-sical methods. The main disadvantage of this method is the complexityof the trigger implementation. Radio signals from extensive air-showershave a duration of few tens nanoseconds and amplitudes comparable tothe surrounding background. Moreover, industrial noise, tele- and radiobroadcasting signals, as well as noise from the electronic equipment ofthe experiment, often interfere with measurements. Most of the setupsfor detecting radio emission from extensive air-showers use an externaltrigger from optical or particle detectors. Despite numerous attemptsto develop autonomous (operating with an internal trigger) cosmic rayradio detectors, there is still no established cost-effective technology forthe sparse radio arrays. In the present work, we give an overview of ourprogress in this direction, particularly, we describe a noise generator andsimulation study using data from the Tunka-Rex Virtual Observatory