Search results for "n- γ discrimination"

showing 2 items of 2 documents

Pulse pile-up identification and reconstruction for liquid scintillator based neutron detectors

2018

WOS: 000433206800010 The issue of pulse pile-up is frequently encountered in nuclear experiments involving high counting rates, which will distort the pulse shapes and the energy spectra. A digital method of off-line processing of pile-up pulses is presented. The pile-up pulses were firstly identified by detecting the downward-going zero-crossings in the first-order derivative of the original signal, and then the constituent pulses were reconstructed based on comparing the pile-up pulse with four models that are generated by combining pairs of neutron and.. standard pulses together with a controllable time interval. The accuracy of this method in resolving the pile-up events was investigate…

Nuclear and High Energy PhysicsLiquid scintillatorFirst-order derivativeNeutron-γ discrimination3106020209 energy310502 engineering and technologyDerivativeScintillatorDigital7. Clean energy01 natural sciencesSignalSpectral lineNeutron-[formula omitted] discriminationOptics0103 physical sciences0202 electrical engineering electronic engineering information engineeringNeutron detectionNeutron[PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det]InstrumentationPile-upPhysicsNeutron-gamma discrimination010308 nuclear & particles physicsbusiness.industryPulse (physics)Neutron- γ discriminationbusinessEnergy (signal processing)
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Artificial neural networks for neutron/ γ discrimination in the neutron detectors of NEDA

2020

Three different Artificial Neural Network architectures have been applied to perform neutron/? discrimination in NEDA based on waveform and time-of-flight information. Using the coincident ?-rays from AGATA, we have been able to measure and compare on real data the performances of the Artificial Neural Networks as classifiers. While the general performances are quite similar for the data set we used, differences, in particular related to the computing times, have been highlighted. One of the Artificial Neural Network architecture has also been found more robust to time misalignment of the waveforms. Such a feature is of great interest for online processing of waveforms. Narodowe Centrum Nau…

Nuclear and High Energy Physics[formula omitted]-ray spectroscopyNeutron detectorComputer Science::Neural and Evolutionary Computationγ -ray spectroscopy[PHYS.NEXP]Physics [physics]/Nuclear Experiment [nucl-ex]01 natural sciences030218 nuclear medicine & medical imaging03 medical and health sciences0302 clinical medicineCoincident0103 physical sciencesMachine learningNeutron detectionWaveformNeutron[PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det]InstrumentationComputingMilieux_MISCELLANEOUSPhysicsArtificial neural networkArtificial neural networksPulse-shape discriminationn- γ discrimination010308 nuclear & particles physicsbusiness.industryPattern recognitionData setn-[formula omitted] discriminationFeature (computer vision)n-? discriminationAGATAArtificial intelligencey-ray spectroscopybusiness
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