Unsupervised domain adaptation for radioisotope identification in gamma spectroscopy
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 is available. Conventional supervise...
| Authors | Peter Lalor, Ayush Panigrahy, Alex Hagen |
|---|---|
| Primary category | cs.LG |
| Published | 5 March 2026 |
| Last updated at source | 17 April 2026 |
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