Machine learning models trained on simulated gamma peak ratios achieve over 95% accuracy classifying yields near a 1 kg TNT threshold and 12.4% mean absolute relative error in yield regression for measurements taken months after the test.
Explaining machine- learning models for gamma-ray detection and identification.PLOS ONE18(6)
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Unsupervised domain adaptation via feature alignment raises radioisotope identification accuracy on real LaBr3 gamma spectra from 0.754 to 0.904 for models trained only on synthetic data.
citing papers explorer
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Machine learning inference of fission yields from gamma spectroscopy for very low-yield nuclear test verification
Machine learning models trained on simulated gamma peak ratios achieve over 95% accuracy classifying yields near a 1 kg TNT threshold and 12.4% mean absolute relative error in yield regression for measurements taken months after the test.
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Unsupervised domain adaptation for radioisotope identification in gamma spectroscopy
Unsupervised domain adaptation via feature alignment raises radioisotope identification accuracy on real LaBr3 gamma spectra from 0.754 to 0.904 for models trained only on synthetic data.