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
Accelerator Applications
The division was organized to promote the advancement of knowledge of the use of particle accelerator technologies for nuclear and other applications. It focuses on production of neutrons and other particles, utilization of these particles for scientific or industrial purposes, such as the production or destruction of radionuclides significant to energy, medicine, defense or other endeavors, as well as imaging and diagnostics.
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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The Nuclear Family: Empowering parents and caregivers
The Diversity and Inclusion in ANS Committee is hosting a webinar today to celebrate the contributions of parents in the nuclear industry while fostering diversity and inclusion within the community.
Register now: The webinar, from 1:00-2:00 pm ET, will highlight how the nuclear industry supports caregivers, new parents, and new mothers, and will focus on life transitions and parental responsibilities.
Carlos X. Soto, Odera Dim, Yonggang Cui, Warren Stern
Nuclear Technology | Volume 209 | Number 9 | September 2023 | Pages 1282-1294
Research Article | doi.org/10.1080/00295450.2023.2200573
Articles are hosted by Taylor and Francis Online.
Burnup measurement is an important step in material control and accountancy at nuclear reactors and may be done by examining gamma spectra of fuel samples. Traditional approaches rely on known correlations to specific photopeaks (e.g., Cs) and operate via a standard linear regression method. However, the quality of these regression methods is limited even in the best case and is significantly poorer at short fuel cooldown times, due to the elevated radiation background by short-lifetime isotopes and self-shielding effect of the fuel. For practical operation of pebble bed reactors (PBRs), quick measurements (in minutes) and short cooling times (in hours) are required from a safety and security perspective. We investigated the efficacy and performance of machine learning (ML) methods to predict the burnup of the pebble fuel from full gamma spectra (rather than specific discrete photopeaks) and found a full-spectrum ML approach to far outperform baseline regression predictions in all measurement and cooling conditions, including in operational-like measurement conditions. We also performed model and data ablation experiments to determine the relative performance impact of our ML methods’ capacity to model data nonlinearities and the inherent additional information in full spectra. Applying our ML methods, we found a number of surprising results, including improved accuracy at shorter fuel cooling times (the opposite of the norm), remarkable robustness to spectrum compression (via rebinning), and competitive burnup predictions even when using a background signal only (i.e., explicitly omitting known isotope photopeaks).