Regular Article - Experimental Physics
Signal recognition efficiencies of artificial neural-network pulse-shape discrimination in HPGe -decay searches
Max-Planck-Institut für Physik, Föhringer Ring 6, 80805, Munich, Germany
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Accepted: 20 July 2015
Published online: 30 July 2015
A pulse-shape discrimination method based on artificial neural networks was applied to pulses simulated for different background, signal and signal-like interactions inside a germanium detector. The simulated pulses were used to investigate variations of efficiencies as a function of used training set. It is verified that neural networks are well-suited to identify background pulses in true-coaxial high-purity germanium detectors. The systematic uncertainty on the signal recognition efficiency derived using signal-like evaluation samples from calibration measurements is estimated to be 5 %. This uncertainty is due to differences between signal and calibration samples.
© SIF and Springer-Verlag Berlin Heidelberg, 2015