Development of LASSO based optimized scheme for reconstructing radioactive source distributions using monitoring air dose rates

Clarifying the distribution of radioactive sources within nuclear facilities is crucial for ensuring worker safety during decommissioning and accident response. However, obtaining comprehensive air dose rate measurements in restricted areas is often difficult due to complex structures and high radiation levels in contaminated rooms. To address this challenge, LASSO regression approach has been proposed to reconstruct radioactive source distributions in simplified room models. This method demonstrates high accuracy in reconstructing sources inside these simple environments. However, obstacles present in more complex settings can degrade reconstruction accuracy. To overcome these limitations, an optimized scheme is developed based on the LASSO method to improve inverse estimation in complex rooms. In this scheme, the impact of shielding structures is mitigated by normalizing the radioactive contributions from sources. A series of numerical simulations demonstrate that the optimized approach outperforms the non-optimized version in accurately reconstructing source distributions. Furthermore, experiments in a room with complex structures validate the effectiveness of the optimized method. The inverse estimations performed on experimental data confirm that the use of a normalized contribution matrix significantly improves accuracy by reducing the influence of shielding. It is confirmed that optimized LASSO scheme holds significant promise for future monitoring and decommissioning projects in both operational and damaged nuclear facilities. However, current optimization does not yet perfectly reconstruct experimental source distributions, owing to several reasons such as model–reality mismatch, measurement errors, etc. In the future, integrating refined models and embedding uncertainty quantification will further improve inversion accuracy under realistic conditions. © 2025 Elsevier Ltd