Search

The recent progress in the radio detection technique for air showers paves the path to future cosmic-ray radio detectors. Digital radio arrays allow for a measurement of the air-shower energy and depth of its maximum with a resolution comparable to those of the leading optical detection methods. One of the remaining challenges regarding cosmic-ray radio instrumentation is an accurate estimation of their efficiency and aperture. We present a probabilistic model to address this challenge. We use the model to estimate the efficiency and aperture of the Tunka-Rex radio array. The basis of the model is a parametrization of the radio footprint and a probabilistic treatment of the detection process on both the antenna and array levels. In this way, we can estimate the detection efficiency for air showers as function of their arrival direction, energy, and impact point on the ground. In addition, the transparent internal relationships between the different stages of the air-shower detection process in our probabilistic approach enable to estimate the uncertainty of the efficiency and, consequently, of the aperture of radio arrays. The details of the model will be presented in the contribution., Comment: Proceedings of the 37th International Cosmic Ray Conference (ICRC2021), 12-23 July 2021, Berlin, Germany - Online

Radio detection of air showers produced by ultra-high energy cosmic rays is a cost-effective technique for the next generation of sparse arrays. The performance of this technique strongly depends on the environmental background, which has different constituents, namely anthropogenic radio frequency interference, synchrotron galactic radiation and others. These components have recognizable features, which can help for background suppression. A powerful method for handling this is the application of convolution neural networks with a specific architecture called autoencoder. By suppressing unwanted signatures, the autoencoder keeps the signal-like ones. We have successfully developed and trained an autoencoder, which is now applied to the data from Tunka-Rex. We show the procedures of the training and optimization of the network including benchmarks of different architectures. Using the autoencoder, we improved the standard analysis of Tunka-Rex in order to lower the threshold of the detection. This enables the reconstructing of sub-threshold events with energies lower than 0.1 EeV with satisfactory angular and energy resolutions., Comment: Proceedings of the 37th International Cosmic Ray Conference (ICRC2021), 12-23 July 2021, Berlin, Germany - Online

Tunka-Rex (Tunka Radio Extension) was a detector for ultra-high energy cosmic rays measuring radio emission for air showers in the frequency band of 30-80 MHz, operating in 2010s. It provided an experimental proof that sparse radio arrays can be a cost-effective technique to measure the depth of shower maximum with resolutions competitive to optical detectors. After the decommissioning of Tunka-Rex, as last phase of its lifecycle and following the FAIR (Findability - Accessibility - Interoperability - Reuse) principles, we publish the data and software under free licenses in the frame of the TRVO (Tunka-Rex Virtual Observatory), which is hosted at KIT under the partnership with the KCDC and GRADLCI projects. We present the main features of TRVO, its interface and give an overview of projects, which benefit from its open software and data., Comment: Proceedings of the 37th International Cosmic Ray Conference (ICRC2021), 12-23 July 2021, Berlin, Germany - Online. arXiv admin note: substantial text overlap with arXiv:1906.10425

Sparse digital antenna arrays constitute a promising detection technique for future large-scale cosmic-ray observatories. It has recently been shown that this kind of instrumentation can provide a resolution of the energy and of the shower maximum on the level of other cosmic-ray detection methods. Due to the dominant geomagnetic nature of the air-shower radio emission in the traditional frequency band of 30 to 80 MHz, the amplitude and polarization of the radio signal strongly depend on the azimuth and zenith angle of the arrival direction. Thus, the estimation of the efficiency and subsequently of the aperture of an antenna array is more complex than for particle or Cherenkov-light detectors. We have built a new efficiency model based on utilizing a lateral distribution function as a shower model, and a probabilistic treatment of the detection process. The model is compared to the data measured by the Tunka Radio Extension (Tunka-Rex), a digital antenna array with an area of about 1 km$^2$ located in Siberia at the Tunka Advanced Instrument for Cosmic rays and Gamma Ray Astronomy (TAIGA). Tunka-Rex detects radio emission of air showers using trigger from air-Cherenkov and particle detectors. The present study is an essential step towards the measurement of the cosmic-ray flux with Tunka-Rex, and is important for radio measurements of air showers in general., Comment: Contribution to the 36th International Cosmic Ray Conference

The Tunka Radio Extension (Tunka-Rex) is a digital antenna array located in the Tunka Valley in Siberia, which measures the radio emission of cosmic-ray air-showers with energies up to EeV. Tunka-Rex is externally triggered by the Tunka-133 air-Cherenkov timing array (during nights) and by the Tunka-Grande array of particle detectors (remaining time). These three arrays comprise the cosmic-ray extension of the Tunka Advanced Instrument for cosmic rays and Gamma Astronomy (TAIGA). The configuration and analysis pipeline of Tunka-Rex have significantly changed over its runtime. Density of the antennas was tripled and the pipeline has become more developed forming now sophisticated piece of reconstruction software. During its lifecycle Tunka-Rex has demonstrated that a cost-effective and full duty-cycle radio detector can reconstruct the energy and shower maximum with a precision comparable to optical detectors. Moreover, it was shown that cosmic-ray instruments, that use different detection techniques and are placed in different locations, can be cross-calibrated via their radio extensions. These results show the prospects of application of the radio technique for future large-scale experiments for cosmic-ray and neutrino detection. For the time being Tunka-Rex has ceased active measurements and focuses on the data analysis and publication of corresponding software and data in an open-access data center with online analysis features. In this report we present the current status of the array and give an overview of the results achieved during these years as well as discuss upcoming improvements in instrumentation and data analysis, which can be applied for the future radio arrays., Comment: Presented at the 36th International Cosmic Ray Conference (ICRC 2019)

The Tunka Radio Extension (Tunka-Rex) is a digital antenna array operating in the frequency band of 30-80 MHz, measuring the radio emission of air-showers induced by ultra-high energy cosmic rays. Tunka-Rex is co-located with the TAIGA experiment in Siberia and consists of 63 antennas, 57 of them in a densely instrumented area of about 1km2. The signals from the air showers are short pulses, which have a duration of tens of nanoseconds and are recorded in traces of about 5{\mu}s length. The Tunka-Rex analysis of cosmic-ray events is based on the reconstruction of these signals, in particular, their positions in the traces and amplitudes. This reconstruction suffers at low signal-to-noise ratios, i.e. when the recorded traces are dominated by background. To lower the threshold of the detection and increase the efficiency, we apply advanced methods of signal reconstruction, namely matched filtering and deep neural networks with autoencoder architecture. In the present work we show the comparison between the signal reconstructions obtained with these techniques, and give an example of the first reconstruction of the Tunka-Rex signals obtained with a deep neural networks., Comment: Proceedings of the 3rd International Workshop on Data Life Cycle in Physics, Irkutsk, Russia, April 2-7, 2019

The Tunka Radio Extension (Tunka-Rex) is a cosmic-ray detector operating since 2012. The detection principle of Tunka-Rex is based on the radio technique, which impacts data acquisition and storage. In this paper we give a first detailed overview of the concept of the Tunka-Rex Virtual Observatory (TRVO), a framework for open access to the Tunka-Rex data, which currently is under active development and testing. We describe the structure of the data, main features of the interface and possible applications of the TRVO., Comment: Proceedings of the 3rd International Workshop on Data Life Cycle in Physics, Irkutsk, Russia, April 2-7, 2019

The Tunka Radio Extension (Tunka-Rex) is an antenna array spread over an area of about 1~km$^2$. The array is placed at the Tunka Advanced Instrument for cosmic rays and Gamma Astronomy (TAIGA) and detects the radio emission of air showers in the band of 30 to 80~MHz. During the last years it was shown that a sparse array such as Tunka-Rex is capable of reconstructing the parameters of the primary particle as accurate as the modern instruments. Based on these results we continue developing our data analysis. Our next goal is the reconstruction of cosmic-ray energy spectrum observed only by a radio instrument. Taking a step towards it, we develop a model of aperture of our instrument and test it against hybrid TAIGA observations and Monte-Carlo simulations. In the present work we give an overview of the current status and results for the last five years of operation of Tunka-Rex and discuss prospects of the cosmic-ray energy estimation with sparse radio arrays., Comment: Proceedings of E+CRS 2018

The Tunka Radio Extension (Tunka-Rex) is a digital antenna array for the detection of radio emission from cosmic-ray air showers in the frequency band of 30 to 80 MHz and for primary energies above 100 PeV. The standard analysis of Tunka-Rex includes events with zenith angle of up to 50$^\circ$. This cut is determined by the efficiency of the external trigger. However, due to the air-shower footprint increasing with zenith angle and due to the more efficient generation of radio emission (the magnetic field in the Tunka valley is almost vertical), there are a number of ultra-high-energy inclined events detected by Tunka-Rex. In this work we present a first analysis of a subset of inclined events detected by Tunka-Rex. We estimate the energies of the selected events and test the efficiency of Tunka-Rex antennas for detection of inclined air showers., Comment: ARENA2018 proceedings

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 proceedings

The Tunka Radio Extension (Tunka-Rex) is a digital radio array operating in the frequency band of 30-80 MHz and detecting radio emission from air-showers produced by cosmic rays with energies above 100 PeV. The experiment is installed at the site of the TAIGA (Tunka Advanced Instrument for cosmic rays and Gamma Astronomy) observatory and performs joint measurements with the co-located particle and air-Cherenkov detectors in passive mode receiving a trigger from the latter. Tunka-Rex collects data since 2012, and during the last five years went through several upgrades. As a result the density of the antenna field was increased by three times since its commission. In this contribution we present the latest results of Tunka-Rex experiment, particularly an updated analysis and efficiency study, which have been applied to the measurement of the mean shower maximum as a function of energy for cosmic rays of energies up to EeV. The future plans are also discussed: investigations towards an energy spectrum of cosmic rays with Tunka-Rex and their mass composition using a combination of Tunka-Rex data with muon measurements by the particle detector Tunka-Grande., Comment: ARENA2018 proceedings

Modern detectors of cosmic gamma-rays are a special type of imaging telescopes (air Cherenkov telescopes) supplied with cameras with a relatively large number of photomultiplier-based pixels. For example, the camera of the TAIGA-IACT telescope has 560 pixels of hexagonal structure. Images in such cameras can be analysed by deep learning techniques to extract numerous physical and geometrical parameters and/or for incoming particle identification. The most powerful deep learning technique for image analysis, the so-called convolutional neural network (CNN), was implemented in this study. Two open source libraries for machine learning, PyTorch and TensorFlow, were tested as possible software platforms for particle identification in imaging air Cherenkov telescopes. Monte Carlo simulation was performed to analyse images of gamma-rays and background particles (protons) as well as estimate identification accuracy. Further steps of implementation and improvement of this technique are discussed., Comment: 5 pages, 2 figures. Submitted to CEUR Workshop Proceedings, 8th International Conference "Distributed Computing and Grid-technologies in Science and Education" GRID 2018, 10 - 14 September 2018, Dubna, Russia

The paper is devoted to the issues of raw binary data documenting, parsing and verifying in astroparticle data lifecycle. The long-term preservation of raw data of astroparticle experiments as originally generated is essential for re-running analyses and reproducing research results. The selected high-quality raw data should have detailed documentation and accompanied by open software tools for access to them. We consider applicability of binary file format description languages to specify, parse and verify raw data of the Tunka Advanced Instrument for cosmic rays and Gamma Astronomy (TAIGA) experiment. The formal specifications are implemented for five data formats of the experiment and provide automatic generation of source code for data reading libraries in target programming languages (e.g. C++, Java, and Python). These libraries were tested on TAIGA data. They showed a good performance and help us to locate the parts with corrupted data. The format specifications can be used as metadata for exchanging of astroparticle raw data. They can also simplify software development for data aggregation from various sources for the multi-messenger analysis.

In this work we compare two open source machine learning libraries, PyTorch and TensorFlow, as software platforms for rejecting hadron background events detected by imaging air Cherenkov telescopes (IACTs). Monte Carlo simulation for the TAIGA-IACT telescope is used to estimate background rejection quality. A wide variety of neural network algorithms provided by both libraries can easily be tested on various types of data, which is useful for various imaging air Cherenkov experiments. The work is a component of the Astroparticle.online project, which collaborates with the TAIGA and KASCADE experiments and welcomes any astroparticle experiment to join., Comment: 6 pages, 2 figures. Submitted to JPCS, 26th Extended European Cosmic Ray Symposium and 35th Russian Cosmic Ray Conference (E+CRS 2018 / RCRC 2018), Barnaul - Belokurikha, July 6 - 10, 2018

We present an improved method for the precise reconstruction of cosmic ray air showers above $10^{17}$ eV with sparse radio arrays. The method is based on the comparison of predictions for radio pulse shapes by CoREAS simulations to measured pulses. We applied our method to the data of Tunka-Rex, a 1 km$^2$ radio array in Siberia operating in the frequency band of 30-80 MHz. Tunka-Rex is triggered by the air-Cherenkov detector Tunka-133 and by scintillators (Tunka-Grande). The instrument collects air-shower data since 2012. The present paper describes updated data and signal analyses of Tunka-Rex and details of a new method applied. After efficiency cuts, when Tunka-Rex reaches its full efficiency, the energy resolution of about 10% given by the new method has reached the limit of systematic uncertainties due to the calibration uncertainty and shower-to-shower fluctuations. At the same time the shower maximum reconstruction is significantly improved up to an accuracy of 35 g/cm$^2$ compared to the previous method based on the slope of the lateral distribution. We also define and now achieved conditions of the measurements, at which the shower maximum resolution of Tunka-Rex reaches a value of 25 g/cm$^2$ and becomes competitive to optical detectors. To check and validate our reconstruction and efficiency cuts we compare individual events to the reconstruction of Tunka-133. Furthermore, we compare the mean of shower maximum as a function of primary energy to the measurements of other experiments., Comment: published version