Towards a coherent Data Life Cycle in Astroparticle Physics

The German-Russian Astroparticle Data Life Cycle Initiative (GRADLCI) aims to develop a data life cycle (DLC), namely a clearly defined and maximally automated data processing pipeline for a combined analysis of data from the experiment KASCADE-Grande (Karlsruhe, Germany) and experiments installed at the Tunka Valley in Russia (TAIGA). The important features of such an astroparticle DLC include scalability for handling large amounts of data, heterogeneous data integration, and exploiting parallel and distributed computing at every possible stage of the data processing. In this work we provide an overview of the technical challenges and solutions worked out so far by the GRADLCI group in the framework of a far-reaching analysis and data center. We will touch the peculiarities of data management in astroparticle physics and employing distributed computing for simulations and physics analyses in this field

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

The decays ρ−→ηπ−\rho^{-}\to\eta\pi^{-} and τ−→η(η′)π−ν\tau^{-}\to\eta(\eta')\pi^{-}\nu in the NJL model

The widths of the decays $\rho^{-}\to\eta\pi^{-}$ and $\tau^{-}\to\eta(\eta')\pi^{-}\nu$ are calculated in the framework of the NJL model. It is shown that these decays are defined by the $u$ and $d$ quark mass difference. It leads to the suppression of these decays in comparison with the main decay modes. In the process $\rho^{-}\to\eta\pi^{-}$ the intermediate scalar $a_0^{-}$ state is taken into account. For the $\tau$ decays the intermediate states with $a_0^{-}$, $\rho^{-}(770)$ and $\rho^{-}(1450)$ mesons are used. Our estimates are compared with the results obtained in other works.Comment: 6 pages, 5 figures, 1 tabl

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