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arXiv · 1412.0895

Signal recognition efficiencies of artificial neural-network pulse-shape discrimination in HPGe $0νββ$-decay searches

A. Caldwell·F. Cossavella·B. Majorovits·D. Palioselitis·O. Volynets
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Abstract

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.

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A. Caldwell, F. Cossavella, B. Majorovits, D. Palioselitis, O. Volynets. 2015-07-21. Signal recognition efficiencies of artificial neural-network pulse-shape discrimination in HPGe $0νββ$-decay searches. https://doi.org/10.1140/epjc%2Fs10052-015-3573-8

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