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Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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The Frisch-Peierls memorandum: A seminal document of nuclear history
The Manhattan Project is usually considered to have been initiated with Albert Einstein’s letter to President Franklin Roosevelt in October 1939. However, a lesser-known document that was just as impactful on wartime nuclear history was the so-called Frisch-Peierls memorandum. Prepared by two refugee physicists at the University of Birmingham in Britain in early 1940, this manuscript was the first technical description of nuclear weapons and their military, strategic, and ethical implications to reach high-level government officials on either side of the Atlantic. The memorandum triggered the initiation of the British wartime nuclear program, which later merged with the Manhattan Engineer District.
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.