arXiv subjects

Cheick Thiam

Publications and source records attributed to Cheick Thiam.

2 recordsLinked to original sources

arXiv2026

Bayesian approach for uncertainty quantification of hybrid spectral unmixing in $\gamma$-ray spectrometry

Identifying and quantifying $\gamma$-emitting radionuclides, considering spectral deformation from $\gamma$-interactions in radioactive source surroundings, present a significant challenge in $\gamma$-ray spectrometry. In that context, a hybrid machine learning method has been previously proposed to jointly estimate the counting and spectral signatures of $\gamma$-emitters under conditions of spectral variability. This paper addresses the uncertainty quantification of the estimators (i.e., the counting and the variable $\lambda$ which characterizes the spectral signatures) obtained by this spectral unmixing algorithm. The focus is on the coverage interval, as defined by the GUM, which corresponds closely to a credible interval in the Bayesian framework. Given the inverse problem and the constraints associated with spectral deformation, two Bayesian methods - Laplace approximation and Markov Chain Monte Carlo - have been developed for uncertainty quantification to ensure robust decision-making. The Laplace approximation technique approximates the posterior distribution by a Gaussian distribution, while the Markov Chain Monte Carlo technique samples the posterior distribution. This study evaluates these two methods in terms of precision of coverage interval based on repeated Monte Carlo samples using the long-run success rate. Numerical experiments show that both methods yield similar results close to the expected success rate of 95.4$\%$ when constraints related to spectral signatures deformation and counting are inactive. However, when constraints are active or the background counting significantly dominates other radionuclides, the Laplace approximation method deviates from the expected long-run success rate due to the non-Gaussian posterior distribution. In such cases, the Markov Chain Monte Carlo method still provides robust results.

physics.data-an↗

arXiv2025

Comparative study of machine learning and statistical methods for automatic identification and quantification in {\gamma}-ray spectrometry

During the last decade, a large number of different numerical methods have been proposed to tackle the automatic identification and quantification in {\gamma}-ray spectrometry. However, the lack of common benchmarks, including datasets, code and comparison metrics, makes their evaluation and comparison hard. In that context, we propose an open-source benchmark that comprises simulated datasets of various {\gamma}-spectrometry settings, codes of different analysis approaches and evaluation metrics. This allows us to compare the state-of-the-art end-to-end machine learning with a statistical unmixing approach using the full spectrum. Three scenarios have been investigated: (1) spectral signatures are assumed to be known; (2) spectral signatures are deformed due to physical phenomena such as Compton scattering and attenuation; and (3) spectral signatures are shifted (e.g., due to temperature variation). A large dataset of 200000 simulated spectra containing nine radionuclides with an experimental natural background is used for each scenario with multiple radionuclides present in the spectrum. Regarding identification performance, the statistical approach consistently outperforms the machine learning approaches across all three scenarios for all comparison metrics. However, the performance of the statistical approach can be significantly impacted when spectral signatures are not modeled correctly. Consequently, the full-spectrum statistical approach is most effective with known or well-modeled spectral signatures, while end-to-end machine learning is a good alternative when measurement conditions are uncertain for radionuclide identification. Concerning the quantification task, the statistical approach provides accurate estimates of radionuclide counting, while the machine learning methods deliver less satisfactory results.

cs.LG↗