6533b85cfe1ef96bd12bd3e1
RESEARCH PRODUCT
Artificial neural networks for neutron/ γ discrimination in the neutron detectors of NEDA
X. FabianG. BaulieuL. DucrouxO. StézowskiA. BoujradE. ClémentS. CoudertG. De FranceN. ErduranS. ErtürkV. GonzálezG. JaworskiJ. NybergD. RaletE. SanchisR. Wadsworthsubject
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 spectroscopybusinessdescription
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 Nauki: 2017/25/B/ST2/01569 Narodowym Centrum Nauki One of the author acknowledges support of the National Science Centre, Poland (NCN) (grant no. 2017/25/B/ST2/01569 ).
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2020-12-20 |