Signal recognition efficiencies of artificial neural-network pulse-shape   discrimination in HPGe $0\nu\beta\beta$-decay searches
  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. 
A. Caldwell, F. Cossavella, B. Majorovits, D. Palioselitis, O.   Volynets
physics.ins-det nucl-ex