ANS is committed to advancing, fostering, and promoting the development and application of nuclear sciences and technologies to benefit society.
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Division Spotlight
Education, Training & Workforce Development
The Education, Training & Workforce Development Division provides communication among the academic, industrial, and governmental communities through the exchange of views and information on matters related to education, training and workforce development in nuclear and radiological science, engineering, and technology. Industry leaders, education and training professionals, and interested students work together through Society-sponsored meetings and publications, to enrich their professional development, to educate the general public, and to advance nuclear and radiological science and engineering.
Meeting Spotlight
2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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
Proving DRACO will deliver
The United States is now closer than it has been in over five decades to launching the first nuclear thermal rocket into space, thanks to DRACO—the Demonstration Rocket for Agile Cislunar Orbit.
Denise Neudecker, Rudolf Frühwirth, Helmut Leeb
Nuclear Science and Engineering | Volume 170 | Number 1 | January 2012 | Pages 54-60
Technical Paper | doi.org/10.13182/NSE11-20
Articles are hosted by Taylor and Francis Online.
The occurrence of unexpected mean values in statistical analyses of experimental data, known as Peelle's pertinent puzzle in nuclear data evaluation, is revisited. It is shown in terms of Bayesian statistics, it is not caused exclusively by nonlinearities but is due to improper estimates of covariance matrices of experiments. Applying the correct covariance matrix leads to the exact posterior expectation value and variance for an arbitrary number of uncorrelated measurement points that are normalized with the same quantity.