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

We present a trigger based on a pipelined artificial neural network implemented in a large FPGA which after learning can recognize different types of waveforms from the Pierre Auger surface detectors. The structure of an artificial neural network algorithm being developed on a MATLAB platform has been implemented into the fast logic of the largest Cyclone V E FPGA used for the prototype of the Front-End Board for the Auger-Beyond-2015. Several algorithms were tested, from which the Levenberg-Marquardt one (trainlm) seems to be the most efficient. The network was taught: a) to recognize ”old” showers learning from real Auger very inclined showers (positive markers) and real standard showers especially triggered by Time over Threshold (negative marker), b) to recognize ”young” showers from 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 for ”old” showers. Nevertheless, preliminary simulations of showers with CORSIKA and the response of the water Cherenkov tanks with OffLine suggest that for neutrino showers starting a development deeply in the atmosphere and with relatively small initial energy ~ 1018 eV, signal waveforms are not to long and a 16-point analysis should be sufficient for recognition of ”young” showers. The neural network algorithm can significantly support detection at small energies, where a denser neutrino stream is expected. For higher energies traces are longer, however, the detector response is strong enough for the showers to be detected by standard amplitude-based triggers.

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