NEDA—NEutron Detector Array

Valiente-Dobón, J. J.; Jaworski, G.; Goasduff, A.; Egea, Francisco J.; Modamio, V.; Hüyük, T.; Triossi, A.; Jastrzab, M.; Söderström, P.-A.; Nitto, A. Di; Angelis, G. de; France, G. de; Erduran, N.; Gadea, A.; Moszyński, M.; Nyberg, J.; Palacz, M.; Wadsworth, R.; Aliaga, Ramón J.; Aufranc, C.; Bézard, M.; Baulieu, G.; Bissiato, E.; Boujrad, A.; Burrows, I.; Carturan, S.; Cocconi, P.; Colucci, G.; Conventi, D.; Cordwell, M.; Coudert, Sébastien; Deltoro, J. M.; Ducroux, L.; Dupasquier, T.; Ertürk, S.; Fabian, X.; González, V.; Grant, A.; Hadyńska-Klęk, K.; Illana, A.; Jurado-Gomez, M.L.; Kogimtzis, M.; Lazarus, I.; Legeard, L.; Ljungvall, J.; Pasqualato, G.; Pérez-Vidal, R. M.; Raggio, A.; Ralet, D.; Redon, N.; Saillant, F.; Sayğı, B.; Sanchís, E.; Scarcioffolo, M.; Siciliano, M.; Testov, D.; Stézowski, O.; Tripon, M.; Zanon, I.

doi:10.1016/j.nima.2019.02.021

Cited by 38 publications

(17 citation statements)

References 22 publications

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“…Scintillators designed to detect neutrons in in-beam experiments, such as the NEDA array [189], must present the largest possible detection efficiency for fast neutrons. NEDA consists of detection modules of organic liquid scintillator (EJ-301).…”

Section: Gammamentioning

confidence: 99%

Trends in particle and nuclei identification techniques in nuclear physics experiments

et al. 2022

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Particle identification techniques are fundamental tools in nuclear physics experiments. Discriminating particles or nuclei produced in nuclear interactions allows to better understand the underlying physics mechanisms. The energy interval of these reactions is very broad, from sub-eV up to TeV. For this reason, many different identification approaches have been developed, often combining two or more observables. This paper reviews several of these techniques with emphasis on the expertise gained within the current nuclear physics scientific program of the Italian Istituto Nazionale di Fisica Nucleare (INFN).

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Section: Gammamentioning

confidence: 99%

Trends in particle and nuclei identification techniques in nuclear physics experiments

et al. 2022

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“…Neutron detectors are widely used for a variety of applications, for example neutron crystallography, 97 nuclear reactor instrumentation, 98 nuclear material detection, 99 and nuclear physics. 100,101 Unlike γ-rays or charged particles, neutrons interact weakly with electrons. Therefore, the detection of neutrons relies on their interaction with nuclei, releasing charged particles that can be converted into electrical signals.…”

Section: Halide Perovskites For Neutron Detectormentioning

confidence: 99%

Halide perovskites and perovskite related materials for particle radiation detection

Liu

Zeng

et al. 2022

Nanoscale

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“…Nuclear safeguards require state-of-the-art technology to characterize a nuclear material. NEutron Detector Array (NEDA) is one such state-of-the-art detection system built for multiple applications [15]. NEDA is a versatile device with 331 EJ-301 liquid scintillators that has high detection efficiency, excellent particle discrimination and high-count rate capabilities.…”

Section: Artificial Neural Network In Nuclear Safeguards and Non-proliferationmentioning

confidence: 99%

An Artificial Neural Network System for Photon-Based Active Interrogation Applications

Jinia¹,

Maurer

Meert

et al. 2021

IEEE Access

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Active interrogation (AI) is a promising technique to detect shielded special nuclear materials (SNMs). At the University of Michigan, we are developing a photon-based AI system that uses bremsstrahlung radiation from an electron linear accelerator (linac) as an ionizing source and trans-stilbene organic scintillating detectors for neutron detection. Stilbene scintillators are sensitive to fast neutrons and photons and have excellent pulse shape discrimination (PSD) capabilities. The traditional charge integration (CI) method commonly used for PSD analysis eliminates piled-up pulses and relies on a particle discrimination line to separate neutrons and photons. The presence of the intense photon flux during AI creates a significant number of piled-up events in the stilbene scintillator, thereby posing a great challenge to the traditional CI method. Identifying true single neutron pulses becomes challenging due to the presence of a pile-up cloud and overlapping neutron, photon and pile-up clouds in the PSD analysis. To mitigate the effect of pulse pile-up and identify true single neutron pulses from stilbene scintillators, an artificial neural network (ANN) system is developed. The developed ANN system identifies single neutron pulses and neutron-photon combinations from piled-up events. The results obtained from a 252 Cf measurement in the presence of the intense photon flux show that the developed ANN system outperforms the traditional CI method. Since many piled-up events lie above the particle discrimination line, they get misclassified as neutrons by the traditional CI method resulting in 27% overestimation of the net neutron count rate during the linac pulse. The overall net neutron count rate (single and restored neutrons) during the linac pulse, estimated by the ANN system is 62.32% of the ground truth. Energy spectroscopy of the ANN attributed single neutron pulses further provides evidence on the detection of prompt fission neutrons from the 252 Cf fission source.INDEX TERMS artificial neural network, high photon flux, linac, pile-up recovery, trans-stilbene.

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NEDA—NEutron Detector Array

Cited by 38 publications

References 22 publications

Trends in particle and nuclei identification techniques in nuclear physics experiments

Trends in particle and nuclei identification techniques in nuclear physics experiments

Halide perovskites and perovskite related materials for particle radiation detection

An Artificial Neural Network System for Photon-Based Active Interrogation Applications

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