Radio emission from the extensive air showers (EASs), initiated by ultrahigh-energy cosmic rays, was theoretically suggested over 50 years ago. However, due to technical limitations, successful ...collection of sufficient statistics can take several years. Nowadays, this detection technique is used in many experiments consisting in studying EAS. One of them is the Auger Engineering Radio Array (AERA), located within the Pierre Auger Observatory. AERA focuses on the radio emission, generated by the electromagnetic part of the shower, mainly in geomagnetic and charge excess processes. The frequency band observed by AERA radio stations is 30-80 MHz. Thus, the frequency range is contaminated by human-made and narrow-band radio frequency interferences (RFIs). Suppression of contaminations is very important to lower the rate of spurious triggers. There are two kinds of digital filters used in AERA radio stations to suppress these contaminations: the fast Fourier transform median filter and four narrow-band IIR-notch filters. Both filters have worked successfully in the field for many years. An adaptive filter based on a least mean squares (LMS) algorithm is a relatively simple finite impulse response (FIR) filter, which can be an alternative for currently used filters. Simulations in MATLAB are very promising and show that the LMS filter can be very efficient in suppressing RFI and only slightly distorts radio signals. The LMS algorithm was implemented into a Cyclone V field programmable gate array for testing the stability, RFI suppression efficiency, and adaptation time to new conditions. First results show that the FIR filter based on the LMS algorithm can be successfully implemented and used in real AERA radio stations.
Neutrinos play a fundamental role in the understanding of the origin of ultrahigh-energy cosmic rays. They interact through charged and neutral currents in the atmosphere generating extensive air ...showers. However, the very low rate of events potentially generated by neutrinos is a significant challenge for detection techniques and requires both sophisticated algorithms and high-resolution hardware. Air showers initiated by protons and muon neutrinos at various altitudes, angles, and energies were simulated in CORSIKA and the Auger OffLine event reconstruction platforms, giving analog-to-digital convertor (ADC) patterns in Auger water Cherenkov detectors on the ground. The proton interaction cross section is high, so proton "old" showers start their development early in the atmosphere. In contrast to this, neutrinos can generate "young" showers deeply in the atmosphere relatively close to the detectors. Differences between "old" proton and "young" neutrino showers are visible in attenuation factors of ADC waveforms. For the separation of "old" proton and "young" neutrino ADC traces, many three-layer artificial neural networks (ANNs) were tested. They were trained in MATLAB (in a dedicated way -only "old" proton and "young" neutrino showers as patterns) by simulated ADC traces according to the Levenberg-Marquardt algorithm. Unexpectedly, the recognition efficiency is found to be almost independent of the size of the networks. The ANN trigger based on a selected 8-6-1 network was tested in the Cyclone V E FPGA 5CEFA9F31I7, the heart of prototype front-end boards developed for testing new algorithms in the Pierre Auger surface detectors.
The Linear Predictor (LP) is a finite impulse response adaptive filter using linear prediction to suppress radio frequency interferences (RFI) in the Auger Engineering Radio Array (AERA). AERA focus ...on the electromagnetic part of the Extensive Air Showers. The electromagnetic part of the shower produces radio signals in geomagnetic radiation and charge excess processes. Due to the reflection of the atmosphere AERA radio stations can observe these signals in the frequency band 30 - 80 MHz. This frequency range is contaminated by narrow-band and other human-made RFI. To suppress these contaminations AERA uses two kind of filters: the Median filter and the infinite impulse response - notch filter, however both of them have disadvantages. LP is a new approach in real-time signal filtering. Laboratory and pampas tests show fast adaptation, acceptable power consumption and very good efficiency of the LP filter.
We are presenting a new approach to a filtering of radio frequency interference (RFI) in the Auger Engineering Radio Array (AERA), which studies the electromagnetic part of the extensive air showers. ...Radio stations can observe radio signals caused by coherent emissions due to geomagnetic radiation and charge excess processes. AERA observes the frequency band from 30 to 80 MHz. This range is highly contaminated by human-made RFI. In order to improve the signal to noise ratio RFI filters are used in AERA to suppress this contamination. The filter has already been tested with real AERA radio stations in the Argentinean Pampas with very successful results. The linear equations were solved either in the virtual soft-core NIOS® processor (implemented in the FPGA chip as a net of logic elements) or in the external Voipac PXA270M ARM processor. The NIOS® processor is relatively slow (50 MHz internal clock), and the calculations performed in an external processor consume a significant amount of time for data exchange between the FPGA and the processor. Tests showed very good efficiency of the RFI suppression for stationary (long-term) contaminations. However, we observed short-time contaminations, which could not be suppressed either by the IIR-notch filter or by the FIR filter based on the linear predictions. For the LP FIR filter, the refresh time of the filter coefficients was too long and the filter did not keep up with the changes in the contamination structure, mainly due to a long calculation time in a slow processors. We propose to use the Cyclone® V SE chip with an embedded micro-controller operating with a 925 MHz internal clock to significantly reduce the refreshment time of the FIR coefficients. First results in the laboratory are very promising.
Observations of ultra-high energy neutrinos became a priority in experimental astroparticle physics. Up to now, the Pierre Auger Observatory did not find any candidate on a neutrino event. This ...imposes competitive limits to the diffuse flux of ultra-high energy neutrinos in the EeV range and above. The prototype Front-End boards for Auger-Beyond-2015 with Cyclone ® V E can test the neural network algorithm in real pampas conditions in 2015. Showers for muon and tau neutrino initiating particles on various altitudes, angles and energies were simulated in CORSIKA and OffLine platforms giving pattern of ADC traces in Auger water Cherenkov detectors. The 3-layer 12-10-1 neural network was taught in MATLAB by simulated ADC traces according the Levenberg - Marquardt algorithm. New sophisticated trigger implemented in Cyclone ® V E FPGAs with large amount of DSP blocks, embedded memory running with 120 - 160 MHz sampling may support to discover neutrino events in the Pierre Auger Observatory.
The electromagnetic part of an extensive air shower developing in the atmosphere provides significant information complementary to that obtained by water Cherenkov detectors which are predominantly ...sensitive to the muonic content of an air shower at ground. The emissions can be observed in the frequency band between 10 and 100MHz. However, this frequency range is significantly contaminated by narrow-band RFI and other human-made distortions. The Auger Engineering Radio Array currently suppresses the RFI by multiple time-to-frequency domain conversions using an FFT procedure as well as by a set of manually chosen IIR notch filters in the time-domain. An alternative approach developed in this paper is an adaptive FIR filter based on linear prediction (LP). The coefficients for the linear predictor are dynamically refreshed and calculated in the virtual NIOS processor. The radio detector is an autonomous system installed on the Argentinean pampas and supplied from a solar panel. Powerful calculation capacity inside the FPGA is a factor. Power consumption versus the degree of effectiveness of the calculation inside the FPGA is a figure of merit to be minimized. Results show that the RFI contamination can be significantly suppressed by the LP FIR filter for 64 or less stages.
•We propose an adaptive method using linear prediction for periodic RFI suppression.•Requirements are the detection of short transient signals powered by solar panels.•The RFI is significantly suppressed by ∼70%, even in a very contaminated environment.•This method consumes less energy than the current method based on FFT used in AERA.•Distortion of the short transient signals is negligible.
The origin of ultra-high-energy cosmic rays (UHECRs) is still a mystery. Neutrinos are a fundamental element in an astrophysics puzzle. Extensive air showers generated by UHECRs contain neutrinos ...interacting with other particles via charged and neutral currents. Due to a very low cross-section of neutrinos and in consequence due to a very low rate of neutrino-induced events their detection is a significant challenge requiring sophisticated algorithms and high-resolution hardware.
One of the greatest challenges for nowadays astrophysics is to understand the origin of the ultrahigh-energy cosmic rays (UHECR). Possibility of detection of air showers initiated by neutrinos can ...significantly help to find sources of the UHECR. Detection technique, however, requires very sophisticated algorithm due to very low cross section of neutrinos. Our algorithm is based on a shape recognition by artificial neural networks (ANN). It can efficiently separate air showers initiated very deep in the atmosphere ("young" showers - which can be potentially induced by neutrinos) from air showers which started at the edge of the atmosphere ("old" showers). The algorithm uses a significant amount of resources, so it has been implemented in the largest Cyclone ® V E FPGA with many Digital Signal Processing blocks. MATLAB tests shows that size of the ANN can be decreased, which saves not negligible amount of FPGA resources.
Neutrinos play a fundamental role in the understanding of the origin of ultra-high-energy cosmic rays. They interact through charged and neutral currents in the atmosphere generating extensive air ...showers. However, their a very low rate of events potentially generated by neutrinos is a significant challenge for a detection technique and requires both sophisticated algorithms and high-resolution hardware. A trigger based on a artificial neural network was implemented into the Cyclone® V E FPGA 5CEFA9F31I7 - the heart of the prototype Front-End boards developed for tests of new algorithms in the Pierre Auger surface detectors. Showers for muon and tau neutrino initiating particles on various altitudes, angles and energies were simulated in CORSICA and OffLine platforms giving pattern of ADC traces in Auger water Cherenkov detectors. The 3-layer 12-8-1 neural network was taught in MATLAB by simulated ADC traces according the Levenberg-Marquardt algorithm. Results show that a probability of a ADC traces generation is very low due to a small neutrino cross-section. Nevertheless, ADC traces, if occur, for 1-10 EeV showers are relatively short and can be analyzed by 16-point input algorithm. We optimized the coefficients from MATLAB to get a maximal range of potentially registered events and for fixed-point FPGA processing to minimize calculation errors. New sophisticated triggers implemented in Cyclone® V E FPGAs with large amount of DSP blocks, embedded memory running with 120-160 MHz sampling may support a discovery of neutrino events in the Pierre Auger Observatory.
Neutrinos play a fundamental role in the understanding of the origin of ultrahigh-energy cosmic rays (UHECR). They interact through charged and neutral currents in the atmosphere generating extensive ...air showers. However, the very low rate of events potentially generated by neutrinos is a significant challenge for detection techniques and requires both sophisticated algorithms and high-resolution hardware. We developed the FPGA trigger which is generated by a neural network. The algorithm can recognize various waveform types. It has been developed and tested on ADC traces of the Pierre Auger surface detectors. We developed the algorithm of artificial neural network on a MATLAB platform. Trained network that we implemented into the largest Cyclone V E FPGA was used for the prototype of the front-end board for the AugerPrime. We tested several variants, and the Levenberg–Marquardt algorithm (trainlm) was the most efficient. The network was trained: (a) to recognize ‘old’ very inclined showers (real Auger data were used as patterns for both positive and negative markers: for reconstructed inclined showers and for triggered by time over threshold (ToT), respectively, (b) to recognize ‘neutrino-induced showers’. Here, we used simulated data for positive markers and vertical real showers for negative ones.
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