Applicability Evaluation of Selected xAI Methods for Machine Learning Algorithms for Signal Parameters Extraction
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918039769841664 |
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| author | Dimitrova, Kalina Kozhuharov, Venelin Petkov, Peicho |
| author_facet | Dimitrova, Kalina Kozhuharov, Venelin Petkov, Peicho |
| contents | Machine learning methods find growing application in the reconstruction and analysis of data in high energy physics experiments. A modified convolutional autoencoder model was employed to identify and reconstruct the pulses from scintillating crystals. The model was further investigated using four xAI methods for deeper understanding of the underlying reconstruction mechanism. The results are discussed in detail, underlining the importance of xAI for knowledge gain and further improvement of the algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_15670 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Applicability Evaluation of Selected xAI Methods for Machine Learning Algorithms for Signal Parameters Extraction Dimitrova, Kalina Kozhuharov, Venelin Petkov, Peicho Computational Physics High Energy Physics - Experiment Instrumentation and Detectors Machine learning methods find growing application in the reconstruction and analysis of data in high energy physics experiments. A modified convolutional autoencoder model was employed to identify and reconstruct the pulses from scintillating crystals. The model was further investigated using four xAI methods for deeper understanding of the underlying reconstruction mechanism. The results are discussed in detail, underlining the importance of xAI for knowledge gain and further improvement of the algorithms. |
| title | Applicability Evaluation of Selected xAI Methods for Machine Learning Algorithms for Signal Parameters Extraction |
| topic | Computational Physics High Energy Physics - Experiment Instrumentation and Detectors |
| url | https://arxiv.org/abs/2504.15670 |