http://arxiv.org/abs/1406.1903
The observation of ultra-high energy neutrinos has become a priority in experimental astroparticle physics. Neutrinos can interact in the atmosphere (downward-going) or in the Earth crust (Earth-skimming), 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 on a neutrino event. This imposes competitive limits to the diffuse flux of neutrinos. 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 implemented in a large FPGA which after learning can recognize traces corresponding to special types of events. The structure of an artificial neural network algorithm being developed on the MATLAB platform has been implemented into the fast logic of the biggest CycloneV E FPGA used for the prototype of the Front-End Board for the Auger-Beyond-2015. 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 Auger very inclined showers (positive markers) and real standard showers (negative marker)), b) to recognize “young” showers (on the basis of simulated “young” events (positive markers). A 3-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.
Z. Szadkowski and Auger. Collaboration-K%2E-Pytel-for-the-Pierre
Tue, 10 Jun 14
42/60
Comments: 8 pages, 14 figures, IEEE Real Time Conference, Nara (Japan) May 25-30, 2014
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