High-resolution Hardware Research Articles

Optimization of an FPGA Trigger Based on an Artificial Neural Network for the Detection of Neutrino-Induced Air Showers

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.

Artificial Neural Network as a FPGA Trigger for a Detection of Very Inclined Air Showers

Neutrinos can interact in the atmosphere (downward-going {\nu}) or in the Earth crust (Earth-skimming {\nu}), producing air showers that can be observed with arrays of detectors at the ground. The surface detector array of the Pierre Auger Observatory can detect these types of cascades. The distinguishing signature for neutrino events is the presence of very inclined showers produced close to the ground (i.e., after having traversed a large amount of atmosphere). Up to now, the Pierre Auger Observatory did not find any candidate for a neutrino event. 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. We present a trigger based on a pipeline artificial neural network (ANN) implemented in a large FPGA which after learning can recognize traces corresponding to special types of events. The structure of an ANN algorithm being developed on the MATLAB platform has been implemented into the fast logic of the biggest Cyclone V E FPGA used for the prototype of the Front-End Board for the Auger-Beyond-2015 effort. Several algorithms were tested, however, the Levenberg-Marquardt one seems to be the most efficient. The network was taught: a) to recognize "old" showers (learning on a basis of real very inclined Auger showers (positive markers) and real standard showers especially triggered by Time over Threshold (negative marker), b) to recognize "young" showers (on the basis of simulated "young" events (positive markers) and standard Auger events as a negative reference). A three-layer neural network being taught by real very inclined Auger showers shows a good efficiency in pattern recognition of 16-point traces with profiles characteristic of "old" showers.

A Study of High-Resolution Timing

This correspondence describes an experimental comparison of timing information provided by a large multiprogramming operating system (OS/360 MVT) with timing information derived directly from a high-resolution hardware clock. The hardware clock was found to be a superior source of timing information.

A note on high resolution timing

The ability to accurately time the execution of sequences of machine instructions is an important tool in the tuning and evaluation of computer hardware and software. The complexity of modern hardware and software systems often makes accurate timing information difficult to obtain [1]. This note describes an experimental comparison of timing information provided by a large multiprogramming operating system (OS/360 MVT) with timing information derived directly from a high resolution hardware clock. The hardware clock was found to be a superior source of timing information.