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
Jan 2026
Jul 2025
Latest Journal Issues
Nuclear Science and Engineering
January 2026
Nuclear Technology
December 2025
Fusion Science and Technology
November 2025
Latest News
Restart progress and a new task force in Iowa
This week, Iowa Gov. Kim Reynolds signed an executive order to form the Iowa Nuclear Energy Task Force, the purpose of which will be to “advise her, the General Assembly, and relevant state agencies on the development and advancement of nuclear energy technologies and infrastructure in the state.”
Keisuke Fujii, Motoshi Goto, Shigeru Morita, Masahiro Hasuo
Fusion Science and Technology | Volume 69 | Number 2 | April 2016 | Pages 514-525
Technical Paper | doi.org/10.13182/FST15-168
Articles are hosted by Taylor and Francis Online.
The Balmer-α line profile observed from high-temperature magnetized plasmas can be interpreted as the sum of narrow and broad components corresponding to the emission from atoms generated in edge and core regions, respectively. The inversion of this line profile reveals the atom density distribution in the plasma. The inversion method we reported in previous studies [Nucl. Fusion, 55, 063029 (2015) and Rev. Sci. Instrum., 85, 023502 (2014)] requires a regularization parameter that must be manually tuned to avoid overfitting. Therefore, it has been difficult to evaluate the uncertainty of the results. Here, we report an improved method based on Bayesian statistics in which the regularization parameter is interpreted as an adjustable parameter, which is then marginalized for the uncertainty evaluation. Two types of prior distributions were examined. The first is an empirical prior that assumes the smoothness of a solution, and the second is based on a diffusion model of hydrogen atoms. We found the use of the diffusion model as prior information to have an advantage with respect to the accuracy of the core region atom density.