DNN-based Signal Processing for Liquid Argon Time Projection Chambers
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908618375299072 |
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| author | Bhat, Avinay Jung, Mun Jung Putnam, Gray Yu, Haiwang |
| author_facet | Bhat, Avinay Jung, Mun Jung Putnam, Gray Yu, Haiwang |
| contents | We investigate a deep learning-based signal processing for liquid argon time projection chambers (LArTPCs), a leading detector technology in neutrino physics. Identifying regions of interest (ROIs) in LArTPCs is challenging due to signal cancellation from bipolar responses and various detector effects observed in real data. We approach ROI identification as an image segmentation task, and employ a U-ResNet architecture. The network is trained on samples that incorporate detector geometry information and include a range of detector variations. Our approach significantly outperforms traditional methods while maintaining robustness across diverse detector conditions. This method has been adopted for signal processing in the Short-Baseline Neutrino program and provides a valuable foundation for future experiments such as the Deep Underground Neutrino Experiment. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_24786 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DNN-based Signal Processing for Liquid Argon Time Projection Chambers Bhat, Avinay Jung, Mun Jung Putnam, Gray Yu, Haiwang Instrumentation and Detectors High Energy Physics - Experiment We investigate a deep learning-based signal processing for liquid argon time projection chambers (LArTPCs), a leading detector technology in neutrino physics. Identifying regions of interest (ROIs) in LArTPCs is challenging due to signal cancellation from bipolar responses and various detector effects observed in real data. We approach ROI identification as an image segmentation task, and employ a U-ResNet architecture. The network is trained on samples that incorporate detector geometry information and include a range of detector variations. Our approach significantly outperforms traditional methods while maintaining robustness across diverse detector conditions. This method has been adopted for signal processing in the Short-Baseline Neutrino program and provides a valuable foundation for future experiments such as the Deep Underground Neutrino Experiment. |
| title | DNN-based Signal Processing for Liquid Argon Time Projection Chambers |
| topic | Instrumentation and Detectors High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2510.24786 |