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

Efficiency estimation of self-triggered antenna clusters for air-shower detection

Air-shower radio arrays operate in low signal-to-noise ratio conditions, which complicates the autonomous measurement of air-shower signals without using an external trigger from optical or scintillator detectors. A simple threshold trigger for radio detector can be efficiently applied onlyin radio-quiet conditions, because for other cases this trigger detects a high fraction of noise pulses. In the present work, we study aspects of independent air-shower detection by dense antenna clusters with a complex real-time trigger system. For choosing the optimal procedures for the real-time analysis, we study the dependence between trigger efficiency, count rate, detector hardware and geometry. For this study, we develop a framework for testing various methods of signal detection and noise filtration for arrays with various specifications and the hardware implementation of these methods based on field programmable gate arrays. The framework provides flexible settings for the management of station-level and cluster-level steps of detecting the signal, optimized for the hardware implementation for real-time processing. It includes data-processing tools for the initialconfiguration and tests on pre-recorded data, tools for configuring the trigger architecture andtools for preliminary estimates of the trigger efficiency at given thresholds of cosmic-ray energyand air-shower pulse amplitude. We show examples of the trigger pipeline developed with this framework and discuss the results of tests on simulated data

Multi-messenger Astroparticle Physics for the Public via the astroparticle.online Project

Multi-messenger astroparticle physics is still a young field of research and is hardly covered in educational curricula or outreach. The astroparticle.online project, founded in 2018 within the framework of the German-Russian Astroparticle Data Life Cycle Initiative (GRADLCI), encompasses an endeavor to address this issue. Within the project, scientists from Karlsruhe Institute of Technology (KIT), Irkutsk State University (ISU) and Moscow State University (MSU) developed a range of educational materials: articles, video lectures, tests, problems to solve, laboratory works and pre-trained neural networks for particle recognition. The project is supported by the KASCADE Cosmic-ray Data Center (KCDC) and GRADLCI data aggregation platform, where one can retrieve and analyze open scientific data from various experiments. The main audience of the project\u27s activities are high school and undergraduate students. All the educational materials are available online at the project\u27s web portal astroparticle.online, they are used both in online and offline masterclasses organized by the project members, and also as the supplementary content by educational organizations: for example, in the ISU course "Introduction to experimental methods in high energy astrophysics". Over the time the project has been operating, more than 150 students took part in its activities. This contribution will cover the experience gained while running the project for more than 3 years, our challenges and developments

New insights from old cosmic rays: A novel analysis of archival KASCADE data

Cosmic ray data collected by the KASCADE air shower experiment are competitive in terms of quality and statistics with those of modern observatories. We present a novel mass composition analysis based on archival data acquired from 1998 to 2013 provided by the KASCADE Cosmic ray Data Center (KCDC). The analysis is based on modern machine learning techniques trained on simulation data provided by KCDC. We present spectra for individual groups of primary nuclei, the results of a search for anisotropies in the event arrival directions taking mass composition into account, and search for gamma-ray candidates in the PeV energy domain.Comment: Proceedings of the 37th International Cosmic Ray Conference (ICRC2021), 12-23 July 2021, Berlin, Germany - Onlin

Reconstruction of Radio Signals from Air-Showers with Autoencoder

The Tunka Radio Extension (Tunka-Rex) is a digital antennaarray (63 antennas distributed over≈1 km2) co-located with the TAIGAobservatory in Eastern Siberia. Tunka-Rex measures radio emission ofair-showers induced by ultra-high energy cosmic rays in the frequencyband of 30-80 MHz. Air-shower signal is a short (tens of nanoseconds)broadband pulse. Using time positions and amplitudes of these pulses,we reconstruct parameters of air showers and primary cosmic rays. Theamplitudes of low-energy event (E<1017eV) cannot be used for suc-cesful reconstruction due to the domination of background. To lower theenergy threshold of the detection and increase the efficiency, we use au-toencoder neural network which removes noise from the measured data.This work describes our approach to denoising raw data and furtherreconstruction of air-shower parameters. We also present results of thelow-energy events reconstruction with autoencoder