Summary of Enhancing Radioisotope Identification in Gamma Spectra with Transfer Learning, by Peter Lalor
Enhancing radioisotope identification in gamma spectra with transfer learning
by Peter Lalor
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Nuclear Theory (nucl-th)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning methods have the potential to accurately classify unknown radioactive samples in gamma spectroscopy. However, obtaining sufficient training data can be expensive and time-consuming. This paper explores a novel approach that combines pretraining with physically derived synthetic data and transfer learning techniques. The method enables models to learn physical principles during the pretraining step, requiring less target-domain data than traditional machine learning methods. Results show that fine-tuned models significantly outperform those trained solely on synthetic or target-domain data, particularly in the intermediate data regime. This study demonstrates the effectiveness of transfer learning for scenarios where experimental data is limited. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Gamma spectroscopy aims to classify unknown radioactive samples quickly and accurately. Currently, it’s hard to get enough training data because it costs time and money. To solve this problem, researchers combined pretraining with synthetic data and special techniques called transfer learning. This approach helps models learn important principles before they see real-world data. The results show that these fine-tuned models do much better than ones trained only on fake or real data, especially when there’s not a lot of training data available. |
Keywords
» Artificial intelligence » Machine learning » Pretraining » Synthetic data » Transfer learning
