Development and Explainability of Models for Machine-Learning-Based Reconstruction of Signals in Particle Detectors
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909591743234048 |
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| author | Dimitrova, Kalina Kozhuharov, Venelin Petkov, Peicho |
| author_facet | Dimitrova, Kalina Kozhuharov, Venelin Petkov, Peicho |
| contents | Machine learning methods are being introduced at all stages of data reconstruction and analysis in various high-energy physics experiments. We present the development and application of convolutional neural networks with modified autoencoder architecture for the reconstruction of the pulse arrival time and amplitude in individual scintillating crystals in electromagnetic calorimeters and other detectors. The network performance is discussed as well as the application of xAI methods for further investigation of the algorithm and improvement of the output accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_17272 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Development and Explainability of Models for Machine-Learning-Based Reconstruction of Signals in Particle Detectors Dimitrova, Kalina Kozhuharov, Venelin Petkov, Peicho Instrumentation and Detectors High Energy Physics - Experiment Computational Physics Machine learning methods are being introduced at all stages of data reconstruction and analysis in various high-energy physics experiments. We present the development and application of convolutional neural networks with modified autoencoder architecture for the reconstruction of the pulse arrival time and amplitude in individual scintillating crystals in electromagnetic calorimeters and other detectors. The network performance is discussed as well as the application of xAI methods for further investigation of the algorithm and improvement of the output accuracy. |
| title | Development and Explainability of Models for Machine-Learning-Based Reconstruction of Signals in Particle Detectors |
| topic | Instrumentation and Detectors High Energy Physics - Experiment Computational Physics |
| url | https://arxiv.org/abs/2504.17272 |