| Issue |
EPJ Web Conf.
Volume 302, 2024
Joint International Conference on Supercomputing in Nuclear Applications + Monte Carlo (SNA + MC 2024)
|
|
|---|---|---|
| Article Number | 16004 | |
| Number of page(s) | 10 | |
| Section | Monte Carlo Simulation: Applications / Radiation Shielding, Fusion & Medical | |
| DOI | https://doi.org/10.1051/epjconf/202430216004 | |
| Published online | 15 October 2024 | |
https://doi.org/10.1051/epjconf/202430216004
Selection method for observation points using Bayesian LASSO at estimating radiation source distribution from air dose rates
1 Center for Computation Science & e-Systems, Japan Atomic Energy Agency, Japan
2 Mizuho Research & Technologies, Ltd.
* Corresponding author: yamada.susumu@jaea.go.jp
Published online: 15 October 2024
When we decommission a reactor building, it is essential to identify the radiation source distribution for safety. It has been reported that the source distribution can be predicted from the measured air dose rates at appropriate observation points by minimizing an evaluation function using the Least Absolute Shrinkage and Selection Operator (LASSO). However, it is challenging to decide on suitable points in advance. Therefore, we estimate the posterior distribution from the prior distribution of the source amounts, calculated by the standard LASSO, using the Bayesian LASSO. We then assess the predictive distribution of the air dose rates at the candidate observation points from the posterior distribution. We select the additional observation points based on the variances of the predictive distributions. We confirmed that the method can estimate the source distribution with fewer additional observation points than when adding them randomly in most cases.
© The Authors, published by EDP Sciences, 2024
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