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Unsupervised domain adaptation for radioisotope identification in gamma spectroscopy

arXiv:2603.05719v1 Announce Type: new Abstract: Training machine learning models for radioisotope identification using gamma spectroscopy remains an elusive challenge for many practical applications, largely stemming from the difficulty of acquiring and labeling large, diverse experimental datasets. Simulations can mitigate this challenge, but the accuracy of models trained on simulated data can deteriorate substantially when deployed to an out-of-distribution operational environment. In this study, we demonstrate that unsupervised domain adaptation (UDA) can improve the ability of a model trained on synthetic data to generalize to a new testing domain, provided unlabeled data from the target domain are available. Conventional supervised techniques are unable to utilize this data because the absence of isotope labels precludes defining a supervised classification loss. Instead, we first pretrain a spectral classifier using labeled synthetic data and subsequently leverage unlabeled tar

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Peter Lalor, Ayush Panigrahy, Alex Hagen · · 1 min read · 9 views
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arXiv:2603.05719v1 Announce Type: new Abstract: Training machine learning models for radioisotope identification using gamma spectroscopy remains an elusive challenge for many practical applications, largely stemming from the difficulty of acquiring and labeling large, diverse experimental datasets. Simulations can mitigate this challenge, but the accuracy of models trained on simulated data can deteriorate substantially when deployed to an out-of-distribution operational environment. In this study, we demonstrate that unsupervised domain adaptation (UDA) can improve the ability of a model trained on synthetic data to generalize to a new testing domain, provided unlabeled data from the target domain are available. Conventional supervised techniques are unable to utilize this data because the absence of isotope labels precludes defining a supervised classification loss. Instead, we first pretrain a spectral classifier using labeled synthetic data and subsequently leverage unlabeled target data to align the learned feature representations between the source and target domains. We compare a range of different UDA techniques, finding that minimizing the maximum mean discrepancy (MMD) between source and target feature vectors yields the most consistent improvement to testing scores. For instance, using a custom transformer-based neural network, we achieved a testing accuracy of $0.904 \pm 0.022$ on an experimental LaBr$_3$ test set after performing unsupervised feature alignment via MMD minimization, compared to $0.754 \pm 0.014$ before alignment. Overall, our results highlight the potential of using UDA to adapt a radioisotope classifier trained on synthetic data for real-world deployment.

Executive Summary

This article presents an innovative application of unsupervised domain adaptation (UDA) to improve the performance of machine learning models for radioisotope identification in gamma spectroscopy. By leveraging unlabeled data from the target domain, the authors demonstrate that UDA can enhance the generalizability of models trained on synthetic data. The study highlights the potential of using MMD minimization for feature alignment and achieves significant improvements in testing accuracy. The results have promising implications for real-world deployment of radioisotope classifiers. However, the study's limitations and potential applications warrant further exploration. The findings contribute to the growing body of research on domain adaptation and its applications in machine learning.

Key Points

  • ▸ Unsupervised domain adaptation (UDA) is applied to improve the generalizability of machine learning models for radioisotope identification.
  • ▸ MMD minimization is used for feature alignment, achieving significant improvements in testing accuracy.
  • ▸ The study demonstrates the potential of UDA for real-world deployment of radioisotope classifiers.

Merits

Strength in Methodology

The authors employ a rigorous approach, utilizing a range of UDA techniques and comparing their performance to demonstrate the effectiveness of MMD minimization.

Significant Improvement in Performance

The study achieves substantial improvements in testing accuracy, highlighting the potential of UDA for enhancing model generalizability.

Demerits

Limited Experimental Dataset

The study's reliance on a limited experimental dataset may limit the generalizability of the findings to other applications and environments.

Potential Overfitting to Synthetic Data

The pretraining of the model on synthetic data may lead to overfitting, which could be mitigated by incorporating more diverse and representative data in the training set.

Expert Commentary

The study presents a significant contribution to the field of machine learning, particularly in the area of domain adaptation. The authors' application of UDA to improve the generalizability of radioisotope classifiers is innovative and demonstrates the potential of this approach for enhancing model performance. However, the study's limitations and potential applications warrant further exploration. The findings have promising implications for real-world deployment of radioisotope classifiers and contribute to the growing body of research on domain adaptation and its applications in machine learning.

Recommendations

  • ✓ Future studies should seek to replicate the findings using more diverse and representative datasets to improve the generalizability of the results.
  • ✓ The authors should investigate the potential of incorporating additional data sources, such as expert annotations or user feedback, to further enhance model performance and generalizability.

Sources

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