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Machine learning for digital pulse shape discrimination
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1016/0029-554x(68)90035-9openalex-snapshot
An accurate pulse-shape discriminator for a wide range of energies
inferredcites →https://doi.org/10.1016/0029-554x(70)90507-0openalex-snapshot
Pulse shape discrimination: An investigation of n-γ discrimination with respect to size of liquid scintillator
inferredcites →https://doi.org/10.1016/j.nima.2003.11.103openalex-snapshot
Digital pulse shape discrimination in organic scintillators for fusion applications
inferredcites →https://doi.org/10.1016/j.nima.2008.06.004openalex-snapshot
Digital pulse-shape discrimination of fast neutrons and γ rays
inferredcites →https://doi.org/10.1109/tsp.2009.2036071openalex-snapshot
Nested Support Vector Machines
inferredcites →https://doi.org/10.20347/wias.preprint.1471openalex-snapshot
Semi-supervised novelty detection
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Comparative analysis of pulse shape discrimination methods in a 6Li loaded plastic scintillator
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Methodology and performance comparison of statistical learning pulse shape classifiers as demonstrated with organic liquid scintillator
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Online neutron/gamma discrimination and source detection using CLYC(Ce) scintillator: A sequential approach
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Pulse shape discrimination and exploration of scintillation signals using convolutional neural networks
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Neutron-Gamma Classification by Evolutionary Fuzzy Rules and Support Vector Machines
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A General-Purpose Digital Pulse Shape Discrimination Algorithm
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Signal Separation from Thermal Neutrons in Electron–Neutron Detectors Using Convolutional Neural Nets in the ENDA Experiment
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Ratio-Based Pulse Shape Discrimination: Analytic Results for Gaussian and Poisson Noise Models
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