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A neural network pulse shape discrimination and pile-up rejection framework for the BC501 neutron/gamma liquid scintillator
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Fysiska sektionen, Institutionen för fysik och astronomi.
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Fysiska sektionen, Institutionen för fysik och astronomi, Kärnfysik.
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Fysiska sektionen, Institutionen för fysik och astronomi. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Fysiska sektionen, Institutionen för fysik och astronomi, Kärnfysik. (Nuclear Structure)ORCID-id: 0000-0001-6996-7605
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Fysiska sektionen, Institutionen för fysik och astronomi.
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2009 (Engelska)Ingår i: Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, ISSN 0168-9002, E-ISSN 1872-9576, Vol. 610, nr 2, s. 534-539Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
2009. Vol. 610, nr 2, s. 534-539
Nationell ämneskategori
Naturvetenskap
Identifikatorer
URN: urn:nbn:se:uu:diva-109469DOI: 10.1016/j.nima.2009.08.064OAI: oai:DiVA.org:uu-109469DiVA, id: diva2:272542
Tillgänglig från: 2009-10-15 Skapad: 2009-10-15 Senast uppdaterad: 2017-12-12
Ingår i avhandling
1. Neural Networks Applications and Electronics Development for Nuclear Fusion Neutron Diagnostics
Öppna denna publikation i ny flik eller fönster >>Neural Networks Applications and Electronics Development for Nuclear Fusion Neutron Diagnostics
2009 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

This thesis describes the development of electronic modules for fusion neutron spectroscopy as well as several implementations of artificial neural networks (NN) for neutron diagnostics for the Joint European Torus (JET) experimental reactor in England.

The electronics projects include the development of two fast light pulser modules based on Light Emitting Diodes (LEDs) for the calibration and stability monitoring of two neutron spectrometers (MPRu and TOFOR) at JET. The particular electronic implementation of the pulsers allowed for operation of the LEDs in the nanosecond time scale, which is typically not well accessible with simpler circuits. Another electronic project consisted of the the development and implementation at JET of 32 high frequency analog signal amplifiers for MPRu. The circuit board layout adopted and the choice of components permitted to achieve bandwidth above 0.5 GHz and low distortion for a wide range of input signals. The successful and continued use of all electronic modules since 2005 until the present day is an indication of their good performance and reliability.

The NN applications include pulse shape discrimination (PSD), deconvolution of experimental data and tomographic reconstruction of neutron emissivity profiles for JET. The first study showed that NN can perform neutron/gamma PSD in liquid scintillators significantly better than other conventional techniques, especially for low deposited energy in the detector. The second study demonstrated that NN can be used for statistically efficient deconvolution of neutron energy spectra, with and without parametric neutron spectroscopic models, especially in the region of low counts in the data. The work on tomography provided a simple but effective parametric model for describing neutron emissivity at JET. This was then successfully implemented with NN for fast and automatic tomographic reconstruction of the JET camera data.

The fast execution time of NN, i.e. usually in the microsecond time scale, makes the NN applications presented here suitable for real-time data analysis and typically orders of magnitudes faster than other commonly used codes. The results and numerical methods described in this thesis can be applied to other diagnostic instruments and are of relevance for future fusion reactors such as ITER, currently under construction in Cadarache, France.

Ort, förlag, år, upplaga, sidor
Uppsala: Acta Universitatis Upsaliensis, 2009. s. 126
Serie
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 673
Nyckelord
Neural networks, tomography, unfolding, real time, pulse shape discrimination, PSD, neutron spectroscopy, MPRu, TOFOR, KN3, neutron camera, LED, summing amplifiers, electronics, JET
Nationell ämneskategori
Fusion, plasma och rymdfysik Annan fysik
Forskningsämne
Tillämpad kärnfysik; Elektronik
Identifikatorer
urn:nbn:se:uu:diva-108583 (URN)978-91-554-7613-7 (ISBN)
Disputation
2009-11-06, Häggsalen, Ångströmlaboratoriet, Lägerhyddsvägen 1 Polacksbacken, Uppsala, 10:00 (Engelska)
Opponent
Handledare
Tillgänglig från: 2009-11-02 Skapad: 2009-09-23 Senast uppdaterad: 2013-08-01Bibliografiskt granskad

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Söderström, Pär-AndersNyberg, JohanConroy, SeanEricsson, GöranHellesen, CarlWeiszflog, Matthias

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Institutionen för fysik och astronomiKärnfysikKärnfysikTillämpad kärnfysik
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Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
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