0000000000965636

AUTHOR

S. Coudert

showing 2 related works from this author

Performance of the Advanced GAmma Tracking Array at GANIL

2020

The performance of the Advanced GAmma Tracking Array (AGATA) at GANIL is discussed, on the basis of the analysis of source and in-beam data taken with up to 30 segmented crystals. Data processing is described in detail. The performance of individual detectors are shown. The efficiency of the individual detectors as well as the efficiency after $\gamma$-ray tracking are discussed. Recent developments of $\gamma$-ray tracking are also presented. The experimentally achieved peak-to-total is compared with simulations showing the impact of back-scattered $\gamma$ rays on the peak-to-total in a $\gamma$-ray tracking array. An estimate of the achieved position resolution using the Doppler broadeni…

Normalization (statistics)Nuclear and High Energy PhysicsPhysics - Instrumentation and DetectorsAstrophysics::High Energy Astrophysical PhenomenaFOS: Physical sciencesTracking (particle physics)01 natural sciencesOptics0103 physical sciencesNeutron detectionγ -ray trackingGamma spectroscopyNuclear structure[PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det]Nuclear Experiment (nucl-ex)AGATA spectrometer010306 general physicsNuclear ExperimentInstrumentationPhysicsData processing010308 nuclear & particles physicsbusiness.industryHPGe detectorsDetectorInstrumentation and Detectors (physics.ins-det)[formula omitted]-ray trackingAGATAbusinessGANIL facilityDoppler broadeningNuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
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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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