Physics-informed continuous normalizing flows to learn the electric field within a time-projection chamber
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912686595375104 |
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| author | Li, Ivy Gaemers, Peter Qin, Juehang Bruckner, Naija Arthurs, Maris Monzani, Maria Elena Tunnell, Christopher |
| author_facet | Li, Ivy Gaemers, Peter Qin, Juehang Bruckner, Naija Arthurs, Maris Monzani, Maria Elena Tunnell, Christopher |
| contents | Accurate position reconstruction in noble-element time-projection chambers (TPCs) is critical for rare-event searches in astroparticle physics, yet is systematically limited by electric field distortions arising from charge accumulation on detector surfaces. Conventional data-driven field corrections suffer from three fundamental limitations: discretization artifacts that break smoothness and differentiability, lack of guaranteed consistency with Maxwell's equations, and statistical requirements of $\mathcal{O}(10^7)$ calibration events. We introduce a physics-informed continuous normalizing flow architecture that learns the electric field transformation directly from calibration data while enforcing the constraint of field conservativity through the model structure itself. Applied to simulated $^{83\mathrm{m}}$Kr calibration data in an XLZD-like dual-phase xenon TPC, our method achieves superior reconstruction accuracy compared to histogram-based corrections when trained on identical datasets, demonstrating viable performance with only $6\times10^5$ events$\unicode{x2013}$an order of magnitude reduction in calibration requirements. This approach enables practical monthly field monitoring campaigns, propagation of position uncertainties through differentiable transformations, and enhanced background discrimination in next-generation rare-event searches. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_01897 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Physics-informed continuous normalizing flows to learn the electric field within a time-projection chamber Li, Ivy Gaemers, Peter Qin, Juehang Bruckner, Naija Arthurs, Maris Monzani, Maria Elena Tunnell, Christopher Instrumentation and Detectors Data Analysis, Statistics and Probability Accurate position reconstruction in noble-element time-projection chambers (TPCs) is critical for rare-event searches in astroparticle physics, yet is systematically limited by electric field distortions arising from charge accumulation on detector surfaces. Conventional data-driven field corrections suffer from three fundamental limitations: discretization artifacts that break smoothness and differentiability, lack of guaranteed consistency with Maxwell's equations, and statistical requirements of $\mathcal{O}(10^7)$ calibration events. We introduce a physics-informed continuous normalizing flow architecture that learns the electric field transformation directly from calibration data while enforcing the constraint of field conservativity through the model structure itself. Applied to simulated $^{83\mathrm{m}}$Kr calibration data in an XLZD-like dual-phase xenon TPC, our method achieves superior reconstruction accuracy compared to histogram-based corrections when trained on identical datasets, demonstrating viable performance with only $6\times10^5$ events$\unicode{x2013}$an order of magnitude reduction in calibration requirements. This approach enables practical monthly field monitoring campaigns, propagation of position uncertainties through differentiable transformations, and enhanced background discrimination in next-generation rare-event searches. |
| title | Physics-informed continuous normalizing flows to learn the electric field within a time-projection chamber |
| topic | Instrumentation and Detectors Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2511.01897 |