Gd-containing scintillators for thermal neutron detection via graph-based particle discrimination.

Neutron-gamma discrimination (NGD) for gadolinium-containing scintillators is a challenging issue which prevents them from being used in highly efficient neutron detectors with high signal-to-noise ratios. Pulse-shape based digital-signal processing has been selected to tackle this issue, with an emphasis on the dimensionality reduction of the raw data and capability to mitigate the influence of noise, unwanted variations, and outliers on the accuracy of classification. We present two graph-embedded non-negative matrix factorization digital classifiers in which an event graph (or a network) is introduced to find a low-dimensional structure hidden in the high-dimensional experimental data. Utilizing energy-independent normalized features of waveforms and a graph composed of mixed-source reference events, the smooth or sparse low-dimensional representation of detector signals from a Ce:GdAlGaO scintillator leads to a high thermal neutron detection efficiency (77%-80%) and a high NGD ratio (neutron-gamma efficiency ratio, ∼10) simultaneously. Moreover, excellent discrimination between neutron and ambient background events has been achieved. The proposed graph-embedded algorithms may be applied not only for thermal-neutron position-sensitive detectors and single-channel detectors but also for other radiation detectors that need excellent particle (or crystal) discrimination capabilities and high detection efficiencies in many applications, including medical imaging, nondestructive testing, and security scans.