ANS is committed to advancing, fostering, and promoting the development and application of nuclear sciences and technologies to benefit society.
Explore the many uses for nuclear science and its impact on energy, the environment, healthcare, food, and more.
Explore membership for yourself or for your organization.
Conference Spotlight
2026 ANS Annual Conference
May 31–June 3, 2026
Denver, CO|Sheraton Denver
Latest Magazine Issues
Feb 2026
Jul 2025
Latest Journal Issues
Nuclear Science and Engineering
February 2026
Nuclear Technology
January 2026
Fusion Science and Technology
Latest News
DOE, General Matter team up for new fuel mission at Hanford
The Department of Energy's Office of Environmental Management (EM) on Tuesday announced a partnership with California-based nuclear fuel company General Matter for the potential use of the long-idle Fuels and Materials Examination Facility (FMEF) at the Hanford Site in Washington state.
According to the announcement, the DOE and General Matter have signed a lease to explore the FMEF's potential to be used for advanced nuclear fuel cycle technologies and materials, in part to help satisfy the predicted future requirements of artificial intelligence.
Andreas Szeless, Lawrence Ruby
Nuclear Science and Engineering | Volume 45 | Number 1 | July 1971 | Pages 7-13
Technical Paper | doi.org/10.13182/NSE71-A20340
Articles are hosted by Taylor and Francis Online.
A method has been devised to calculate exactly the probability distribution of reactor neutron noise. The distribution is calculated from a complicated generating function which has been known for some time. The method depends on the success achieved in obtaining a closed-form expression for the n'th derivative of a differentiable r-fold composite function. As an application of the technique, exact probability distributions are calculated for a variety of parameters. The resultant distributions are compared with the approximative negative binomial distribution. In some cases, rather similar variances are found, where the negative binomial is not expected to be a good approximation to the exact distribution. The explanation lies in an interlacing of the exact and approximative distributions. A procedure is described for fitting an experimental distribution to the exact distribution, thereby obtaining the best values of the parameters α1 and Y1 ∞. When the negative binomial is a good approximation to the exact distribution, only the product α1 Y1 ∞ can be obtained by the fitting procedure. In such cases, a Feynman-variance experiment can be performed to determine the parameters separately.