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Register a new userPulse Pileup Rejection Methods Using a Two-Component Gaussian Mixture Model for Fast Neutron Detection with Pulse Shape Discriminating Scintillator
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Pulse shape discriminating scintillator materials in many cases allow the user to identify two basic kinds of pulses arising from two kinds of particles: neutrons and gammas. An uncomplicated solution for building a classifier consists of a two-component mixture model learned from a collection of pulses from neutrons and gammas at a range of energies. Depending on the conditions of data gathered to be classified, multiple classes of events besides neutrons and gammas may occur, most notably pileup events. All these kinds of events are anomalous and, in cases where the class of the particle is in doubt, it is preferable to remove them from the analysis. This study compares the performance of several machine learning and analytical methods for using the scores from the two-component model to identify anomalous events and in particular to remove pileup events. A specific outcome of this study is to propose a novel anomaly score, denoted G, from an unsupervised two-component model that is conveniently distributed on the interval [-1,1].
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Digitization of detector signals enables analysis of the original waveform to extract timing, particle identification, and energy deposition information. Here we present the use of analytical functions based on sigmoids to model and fit such pulse shapes from liquid organic scintillators, though the method should also be applicable to other detector systems. Neutron and gamma interactions in NE213 detectors were digitized from the phototube anode and fit using a sigmoid-based function. The acuity of the fit in extracting timing information and performing neutron-gamma pulse-shape discrimination are presented and discussed.
Pulse shape discrimination plays a key role in improving the signal-to-background ratio in NEOS analysis by removing fast neutrons. Identifying particles by looking at the tail of the waveform has been an effective and plausible approach for pulse shape discrimination, but has the limitation in sorting low energy particles. As a good alternative, the convolutional neural network can scan the entire waveform as they are to recognize the characteristics of the pulse and perform shape classification of NEOS data. This network provides a powerful identification tool for all energy ranges and helps to search unprecedented phenomena of low-energy, a few MeV or less, neutrinos.
A comparative study of the neutron-$gamma$ Pulse Shape Discrimination (PSD) with seven organic scintillators is performed using an identical setup and digital electronics. The scintillators include plastics (EJ-299-33 and a plastic prototype), single crystals (stilbene and the recent doped $p$-terphenyl) and liquids (BC501A, NE213 and the deuterated liquid BC537). First, the overall PSD performance of the different scintillators is compared and threshold neutron energies for a given discrimination quality are determined. Then, using statistical arguments, two intrinsic contributions to the PSD capability of the scintillating materials are disentangled: the light yield and the specific pulse shapes induced by neutrons and $gamma$-rays. This separation provides additional insight into the behaviour of organic scintillators and allows a detailed comparison of the discrimination performance of the various materials. On the basis of this analysis, limitations of current organic scintillators and of recently proposed alternative scintillators are discussed.
Fast neutrons are a large background to measurements of gamma-rays emitted from excited nuclei, such that detectors which can efficiently distinguish between the two are essential. In this paper we describe the separation of gamma-rays from neutrons with the pulse shape information of the CsI(Tl) scintillator, using a fast neutron beam and several gamma-ray sources. We find that a figure of merit optimized for this separation takes on large and stable values (nearly 4) between 5 and 10 MeV of electron equivalent deposited energy, the region of most interest to the study of nuclear de-excitation gamma-rays. Accordingly this work demonstrates the ability of CsI(Tl) scintillators to reject neutron backgrounds to gamma-ray measurements at these energies.
We present a characterization of a small (9-liter) and mobile 0.1% 6Li-doped pulse-shape-sensitive plastic scintillator antineutrino detector called SANDD (Segmented AntiNeutrino Directional Detector), constructed for the purpose of near-field reactor monitoring with sensitivity to antineutrino direction. SANDD comprises three different types of module. A detailed Monte Carlo simulation code was developed to match and validate the performance of each of the three modules. The combined model was then used to produce a prediction of the performance of the entire detector. Analysis cuts were established to isolate antineutrino inverse beta decay events while rejecting large fraction of backgrounds. The neutron and positron detection efficiencies are estimated to be 34.8% and 80.2%, respectively, while the coincidence detection efficiency is estimated to be 71.7%, resulting in inverse beta decay detection efficiency of 20.05 +/- 0.2%(stat.) +/- 2.1%(syst.). The predicted directional sensitivity of SANDD produces an uncertainty of 20 degree in the azimuthal direction per 100 detected antineutrino events.
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