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Direct Evidence of Two Superconducting Gaps in FeSe0.5_{0.5}Te0.5_{0.5}: SnS-Andreev Spectroscopy and Lower Critical Field

We present direct measurements of the superconducting order parameter in nearly optimal FeSe$_{0.5}$Te$_{0.5}$ single crystals with critical temperature $T_C \approx 14$ K. Using intrinsic multiple Andreev reflection effect (IMARE) spectroscopy and measurements of lower critical field, we directly determined two superconducting gaps, $\Delta_L \approx 3.3 - 3.4$ meV and $\Delta_S \approx 1$ meV, and their temperature dependences. We show that a two-band model fits well the experimental data. The estimated electron-boson coupling constants indicate a strong intraband and a moderate interband interaction

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

Current Status and New Challenges of The Tunka Radio Extension

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 201

First analysis of inclined air showers detected by Tunka-Rex

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 proceeding

Organ–preserving operations for the gun–shot thoraco–abdominal and abdominal woundings with splenic injuries

Objective.To determine the expediency of the spleen–preserving operations performance for its injury due to gun–shot thoraco–abdominal and abdominal woundings. Materials and methods. Of 51wounded persons with the gun–shot penetrating abdominal woundings with splenic injuries 8 (15.7%) were operated on the II level of the medical help delivery, while on the ІІІ level – 43 (84.3%). Of 43 wounded persons, operated on the ІІІ level of the medical help delivery, in 35 (81.4%) splenectomy was performed, while in 8 (18.6%) – the organ–preserving operations. All the splenic injuries have been characterized as a shrapnel woundings. The splenic damage degree was estimated in accordance to Moore, taking into account its structure. In 5 (62.5%) wounded persons the open laparotomy organ–preserving operations were performed, and while using video–laparoscopic system– in 3 (37.5%). Results. Ultrasonographic investigation in accordance to the FAST–protocol was done in all the wounded persons, what have permitted to determine the blood abdominal outpouring volume as well as the splenic injury presence. The affection volume and further tactics were determined using video–laparoscopy. The Stage I splenic affection in accordance to Moore was diagnosed in 4 (50%) wounded persons, the Stage ІІ – in 3 (37.5%), and the Stage ІІІ – in 1 (12.5%). In all the patients, in whom organ–preserving interventions were performed, the surgical help volume consisted of coagulation, using the radiofrequency therapy apparatus MEDTRONICAEX and the hemostatic sponge application with drainage of the left subdiaphragmatic space, using silicone tube. Conclusion. Organ–preserving operations in splenic injury are expedient to be performed on ІІІ level of the medical help delivery, while presence of local conditions, such as the organ injury degrees І and ІІ,determined in accordance to Moore, as well as adequate time and equipment

Refining of metal melts by filtration method

The article describes the prerequisites of filtration refining of metal melts. When filtering the liquid metals the refining effect is caused by the deposition on the filter surface of suspended non-metallic particles in the melt, and by the release of the non-metallic phase directly from the melt. Along with this the mechanism of melt refining from a superequilibrium dissolved impurity is realized when filtering as a result of the reaction course of chemical binding of the impurity element

Direct evidence of two superconducting gaps in FeSe<inf>0.5</inf>Te<inf>0.5</inf>: SnS-Andreev spectroscopy and the lower critical field

© 2016, Pleiades Publishing, Inc.We present direct measurements of the superconducting order parameter in nearly optimal FeSe Te single crystals with the critical temperature TC ≈ 14 K. Using the intrinsic multiple Andreev reflection effect (IMARE) spectroscopy and measurements of the lower critical field, we directly determined two superconducting gaps, ΔL ≈ 3.3−3.4 meV and ΔS ≈ 1 meV, and their temperature dependences. We show that a twoband model fits well the experimental data. The estimated electron–boson coupling constants indicate a strong intraband and a moderate interband interaction

Reconstruction of sub-threshold events of cosmic-ray radio detectors using an autoencoder

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 interferences, 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