Speaker
Description
Accurate position reconstruction in noble-element time-projection chambers 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 detector, our method achieves superior reconstruction accuracy compared to histogram-based corrections when trained on identical datasets, demonstrating viable performance with approximately $8.9 \times 10^4$ events---a \textbf{50 times reduction} in calibration data requirements for a comparable performance to a field distortion correction map generated from approximately $4.5 \times 10^6$ events. This approach can enable practical monthly field monitoring campaigns, propagation of position uncertainties through differentiable transformations, and enhanced background discrimination in next-generation rare-event searches.
| Contribution types | Standard talk (20min + 5min Q/A) |
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