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An artificial neural network based neutron-gamma discrimination and pile-up rejection framework for the BC-501 liquid scintillation detector
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy.
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy.
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy.
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy.
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2009 (English)In: Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, ISSN 0168-9002, E-ISSN 1872-9576, Vol. 610, no 2, 534-539 p.Article in journal (Refereed) Published
Abstract [en]

BC-501 is a liquid scintillation detector sensitive to both neutrons and gamma rays. As these produce slightly different signals in the detector, they can be discriminated based on their pulse shape (Pulse Shape Discrimination, PSD). This paper reports on results obtained with several PSD techniques and compares them with a method based on artificial neural networks (NN) developed for this application. Results indicated a large performance advantage of NN especially in the region of small deposited energy which typically contains the majority of the events. NN were also applied for discrimination of pile-up events with good results. This framework can be implemented on some of the most recent programmable data acquisition cards and it is suitable for real-time application.

Place, publisher, year, edition, pages
2009. Vol. 610, no 2, 534-539 p.
Keyword [en]
BC-501, NE213, BC-501A, Neural networks, PSD, Pulse shape discrimination, Neutron gamma discrimination, Liquid scintillator, Cf-252, Time of flight
National Category
Physical Sciences
Identifiers
URN: urn:nbn:se:uu:diva-148206DOI: 10.1016/j.nima.2009.08.064ISI: 000273240800008OAI: oai:DiVA.org:uu-148206DiVA: diva2:401665
Available from: 2011-03-03 Created: 2011-03-03 Last updated: 2017-12-11Bibliographically approved
In thesis
1. Collective Structure of Neutron-Rich Rare-Earth Nuclei and Development of Instrumentation for Gamma-Ray Spectroscopy
Open this publication in new window or tab >>Collective Structure of Neutron-Rich Rare-Earth Nuclei and Development of Instrumentation for Gamma-Ray Spectroscopy
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Neutron-rich rare-earth nuclei are among the most collective nuclei that can be found in nature. In particular, the doubly mid-shell nucleus 170Dy is expected to be the nucleus where the collective structure is maximized. This has implications for the astrophysical r-process, since it has been suggested that the collectivity maximum plays an important role in the abundances of the rare-earth elements that are created in supernova explosions. In this work, the collective structure of the five nuclei 168,170Dy and 167,168,169Ho are studied and different theoretical models are used to interpret the evolution of collectivity around the mid-shell. In order to produce and study even more neutron-rich nuclei in this mass region, new radioactive ion beam facilities will be a valuable tool. These facilities, however, require advanced instruments to study the weak signals of exotic nuclei in a high background environment. Two of these instruments are the γ-ray tracking spectrometer AGATA and the neutron detector array NEDA. For AGATA to work satisfactorily, the interaction position of the gamma rays must be determined with an accuracy of at least five millimetres. The position resolution is measured in this work using a model independent method based on the Doppler correction capabilities of the detector at two different distances between the detector and the source. For NEDA, one of the critical parameters is its ability to discriminate between neutrons and γ rays. By using digital electronics it is possible to employ advanced and efficient algorithms for pulse-shape discrimination. In this work, digital versions of the common analogue methods are shownto give as good, or better, results compared to the ones obtained using analogue electronics. Another method which effectively distinguishes between neutrons and γ rays is based on artificial neural networks. This method is also investigated in this work and is shown to yield even better results.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2011. 122 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 818
Keyword
nuclear structure, collective models, 150 < A < 189, AGATA, HPGe detectors, gamma-ray tracking, NEDA, pulse-shape discrimination, neutrons, neural network
National Category
Subatomic Physics
Research subject
Physics with specialization in Nuclear Physics
Identifiers
urn:nbn:se:uu:diva-149772 (URN)978-91-554-8056-1 (ISBN)
Public defence
2011-05-20, Polhemsalen, Ångströmlaboratoriet, Lägerhyddsvägen 1, Uppsala, 13:15 (English)
Opponent
Supervisors
Available from: 2011-04-29 Created: 2011-03-22 Last updated: 2011-05-05Bibliographically approved

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