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A Neural Networks Framework for Real-Time Unfolding of Neutron Spectroscopic Data at JET
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
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2008 (English)In: Review of Scientific Instruments, ISSN 0034-6748, E-ISSN 1089-7623, Vol. 79, no 10, 10E513- p.Article in journal (Refereed) Published
Abstract [en]

A determination of fast ion population parameters such as intensity and kinetic temperature is important for fusion reactors. This becomes more challenging with finer time resolution of the measurements, since the limited data in each time slice cause increasing statistical variations in the data. This paper describes a framework using Bayesian-regularized neural networks (NNs) designed for such a task. The method is applied to the TOFOR 2.5 MeV fusion neutron spectrometer at JET. NN training data are generated by random sampling of variables in neutron spectroscopy models. Ranges and probability distributions of the parameters are chosen to match the experimental data. Results have shown good performance both on synthetic and experimental data. The latter was assessed by statistical considerations and by examining the robustness and time consistency of the results. The regularization of the training algorithm allowed for higher time resolutions than simple forward methods. The fast execution time makes this approach suitable for real-time analysis with a time resolution limit in the microsecond time scale.

Place, publisher, year, edition, pages
2008. Vol. 79, no 10, 10E513- p.
National Category
Physical Sciences
Identifiers
URN: urn:nbn:se:uu:diva-16763DOI: 10.1063/1.2953492ISI: 000260573500095OAI: oai:DiVA.org:uu-16763DiVA: diva2:44534
Note
JET-EFDA Contributors. Conference information: 17th Topical Conference High Temperature Plasma Diagnostics, May 2008, Albaquerque, New Mexico, USAAvailable from: 2008-06-05 Created: 2008-06-05 Last updated: 2017-12-08Bibliographically approved
In thesis
1. Neural Networks Applications and Electronics Development for Nuclear Fusion Neutron Diagnostics
Open this publication in new window or tab >>Neural Networks Applications and Electronics Development for Nuclear Fusion Neutron Diagnostics
2009 (English)Doctoral thesis, comprehensive summary (Other academic)
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.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2009. 126 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 673
Keyword
Neural networks, tomography, unfolding, real time, pulse shape discrimination, PSD, neutron spectroscopy, MPRu, TOFOR, KN3, neutron camera, LED, summing amplifiers, electronics, JET
National Category
Fusion, Plasma and Space Physics Other Physics Topics
Research subject
Applied Nuclear Physics; Electronics
Identifiers
urn:nbn:se:uu:diva-108583 (URN)978-91-554-7613-7 (ISBN)
Public defence
2009-11-06, Häggsalen, Ångströmlaboratoriet, Lägerhyddsvägen 1 Polacksbacken, Uppsala, 10:00 (English)
Opponent
Supervisors
Available from: 2009-11-02 Created: 2009-09-23 Last updated: 2013-08-01Bibliographically approved

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Ronchi, EConroy, SeanAndersson Sundén, EEricsson, GöranHellesen, CarlGatu Johnson, MariaSjöstrand, HenrikWeiszflog, Matthias

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