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

Background identification algorithm for future self-triggered air-shower radio arrays

The study of the ultra-high energy cosmic rays, neutrinos and gamma rays is one of the most important challenges in astrophysics. The low fluxes of these particles do not allow one to detect them directly. The detection is performed by the measuring of the air-showers produced by the primary particles in the Earth's atmosphere. A radio detection of ultra-high energy air-showers is a cost-effective technique that provides a precise reconstruction of the parameters of primary particle and almost full duty cycle in comparison with other methods. The main challenge of the modern radio detectors is the development of efficient self-trigger technology, resistant to high-level background and radio frequency interference. Most of the modern radio detectors receive trigger generated by either particle or optical detectors. The development of the self trigger for the radio detector will significantly simplify the operation of existing instruments and allow one to access the main advantages of the radio method as well as open the way to the construction of the next generation of large-scale radio detectors. In the present work we discuss our progress in the solution of this problem, particularly the classification of broadband pulses.Comment: 6 pages, 1 figur

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

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

Tunka-Rex: the Cost-Effective Radio Extension of the Tunka Air-Shower Observatory

Tunka-Rex is the radio extension of the Tunka cosmic-ray observatory in Siberia close to Lake Baikal. Since October 2012 Tunka-Rex measures the radio signal of air-showers in coincidence with the non-imaging air-Cherenkov array Tunka-133. Furthermore, this year additional antennas will go into operation triggered by the new scintillator array Tunka-Grande measuring the secondary electrons and muons of air showers. Tunka-Rex is a demonstrator for how economic an antenna array can be without losing significant performance: we have decided for simple and robust SALLA antennas, and we share the existing DAQ running in slave mode with the PMT detectors and the scintillators, respectively. This means that Tunka-Rex is triggered externally, and does not need its own infrastructure and DAQ for hybrid measurements. By this, the performance and the added value of the supplementary radio measurements can be studied, in particular, the precision for the reconstructed energy and the shower maximum in the energy range of approximately $10^{17}-10^{18}\,$eV. Here we show first results on the energy reconstruction indicating that radio measurements can compete with air-Cherenkov measurements in precision. Moreover, we discuss future plans for Tunka-Rex.Comment: Proceeding of UHECR 2014, Springdale, Utah, USA, accepted by JPS Conference Proceeding

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

Improved measurements of the energy and shower maximum of cosmic rays with Tunka-Rex

The Tunka Radio Extension (Tunka-Rex) is an array of 63 antennas located in the Tunka Valley, Siberia. It detects radio pulses in the 30-80 MHz band produced during the air-shower development. As shown by Tunka-Rex, a sparse radio array with about 200 m spacing is able to reconstruct the energy and the depth of the shower maximum with satisfactory precision using simple methods based on parameters of the lateral distribution of amplitudes. The LOFAR experiment has shown that a sophisticated treatment of all individually measured amplitudes of a dense antenna array can make the precision comparable with the resolution of existing optical techniques. We develop these ideas further and present a method based on the treatment of time series of measured signals, i.e. each antenna station provides several points (trace) instead of a single one (amplitude or power). We use the measured shower axis and energy as input for CoREAS simulations: for each measured event we simulate a set of air-showers with proton, helium, nitrogen and iron as primary particle (each primary is simulated about ten times to cover fluctuations in the shower maximum due to the first interaction). Simulated radio pulses are processed with the Tunka-Rex detector response and convoluted with the measured signals. A likelihood fit determines how well the simulated event fits to the measured one. The positions of the shower maxima are defined from the distribution of chi-square values of these fits. When using this improved method instead of the standard one, firstly, the shower maximum of more events can be reconstructed, secondly, the resolution is increased. The performance of the method is demonstrated on the data acquired by the Tunka-Rex detector in 2012-2014.Comment: Proceedings of the 35th ICRC 2017, Busan, Kore

Signal recognition and background suppression by matched filters and neural networks for Tunka-Rex

The Tunka Radio Extension (Tunka-Rex) is a digital antenna array, which measures the radio emission of the cosmic-ray air-showers in the frequency band of 30-80 MHz. Tunka-Rex is co-located with TAIGA experiment in Siberia and consists of 63 antennas, 57 of them are in a densely instrumented area of about 1 km\textsuperscript{2}. In the present work we discuss the improvements of the signal reconstruction applied for the Tunka-Rex. At the first stage we implemented matched filtering using averaged signals as template. The simulation study has shown that matched filtering allows one to decrease the threshold of signal detection and increase its purity. However, the maximum performance of matched filtering is achievable only in case of white noise, while in reality the noise is not fully random due to different reasons. To recognize hidden features of the noise and treat them, we decided to use convolutional neural network with autoencoder architecture. Taking the recorded trace as an input, the autoencoder returns denoised trace, i.e. removes all signal-unrelated amplitudes. We present the comparison between standard method of signal reconstruction, matched filtering and autoencoder, and discuss the prospects of application of neural networks for lowering the threshold of digital antenna arrays for cosmic-ray detection.Comment: ARENA2018 proceeding