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Division Spotlight
Mathematics & Computation
Division members promote the advancement of mathematical and computational methods for solving problems arising in all disciplines encompassed by the Society. They place particular emphasis on numerical techniques for efficient computer applications to aid in the dissemination, integration, and proper use of computer codes, including preparation of computational benchmark and development of standards for computing practices, and to encourage the development on new computer codes and broaden their use.
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ANS Student Conference 2025
April 3–5, 2025
Albuquerque, NM|The University of New Mexico
Standards Program
The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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Latest News
Norway’s Halden reactor takes first step toward decommissioning
The government of Norway has granted the transfer of the Halden research reactor from the Institute for Energy Technology (IFE) to the state agency Norwegian Nuclear Decommissioning (NND). The 25-MWt Halden boiling water reactor operated from 1958 to 2018 and was used in the research of nuclear fuel, reactor internals, plant procedures and monitoring, and human factors.
Patrick Maedgen, Benjamin Wellons, Shikha Prasad, Jian Tao
Nuclear Technology | Volume 208 | Number 10 | October 2022 | Pages 1522-1539
Technical Paper | doi.org/10.1080/00295450.2022.2045533
Articles are hosted by Taylor and Francis Online.
Various machine learning techniques have been implemented to assist in neutron-gamma discrimination with great success compared to traditional methods. Despite this, the fundamental structure of a pulse shape as it relates to machine learning has not yet been explored in detail, and the optimal number of pulse vector features needed for training is still unknown. In this study, support vector machines (SVMs) using linear, radial basis, and exponential kernel functions are fitted on data of two different forms: waveforms that partially cover the original pulses and principal components extracted from those pulses. The described methods correctly classified 98.02% for neutrons and 97.84% for gamma rays. The efficiency of the SVM was improved by extracting principal components from the waveforms. That is, fewer features were needed to discriminate between neutrons and gamma rays without negatively impacting the classification accuracy. This study also shows that utilizing a nonlinear kernel significantly reduces the number of features required to reach high classification accuracy. SVMs that did this could make accurate classifications 97% of the time with data that had fewer than 50 features.