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DOE OSTI2024

Recommendations for harmonized reporting of radiation Dosimetry by adoption of Compatibility in Irradiation Research Protocols Expert Roundtable (CIRPER)

Here, in radiobiology, radiation physics, and related areas, there is growing interest in transitioning from cesium-based irradiators to X-ray technologies. Therefore, it makes it imperative for reproducibility purposes, for researchers to provide specifications regarding device parameters, machine calibration details, and experimental setup when innovative X-ray technology is being employed. Several groups have already identified a glaring lack in completeness of critical information on experimental setups, equipment, and physical parameters in published radiation biology studies. The Alternative Technologies Meta Study Report by Brookhaven National Laboratory (BNL) examined 456 manuscripts published between 2017-2021. Only 64 of the reviewed studies (14.0%) consistently reported methods that could allow for replication of their studies. However, 85% provided information on the manufacturer, the irradiator model and the absorbed radiation dose. Approximately 66% provided information on the energy spectra and the dose rate, but only one third disclosed the filter used. Less than 15% of the reviewed studies reported information on calibration and field geometry, specifically field size and distance from source, which are critical parameters in determining the absorbed radiation dose.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2023

A Better Method to Calculate Fuel Burnup in Pebble Bed Reactors Using Machine Learning

Burnup measurement is an important step in material control and accountancy (MC&A) 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. 137 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 cool-down times, due to the elevated radiation background by short life-time 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 background signal only (i.e. explicitly omitting known isotope photopeaks).

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗