发表机构
GANIL, CEA/DRF - CNRS/IN2P3（GANIL，CEA/DRF - CNRS/IN2P3）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出物理信息自监督学习框架，联合校准多丝平行板雪崩计数器丝线增益并重建相互作用位置，无需标签数据，实验验证提升均匀性与分辨率。
AI 中文摘要
科学仪器需要精确校准，以将探测器信号转换为可靠的物理可观测值。传统校准程序通常依赖于专用的校准测量、解析响应模型或带标签的参考数据，这限制了其适应变化的工作条件和探测器老化的能力。我们提出了一种物理信息自监督学习框架，该框架在多丝平行板雪崩计数器（MWPPACs）中联合执行丝线校准和相互作用位置重建，而无需带标签的位置测量或专用校准运行。该方法将探测器校准表述为一个潜在优化问题，其中全局丝线增益和事件级相互作用位置通过仅从探测器几何和电荷-能量一致性约束中获得的监督信号同时估计。一个与探测器无关的神经网络从局部电荷分布中重建亚丝级相互作用位置，通过直接从实验数据中学习探测器响应，消除了假设解析感应分布的必要。端到端可微框架实现了连续的探测器自校准，同时提高了位置重建的均匀性和准确性。在VAMOS++磁谱仪的入口MWPPAC跟踪探测器上的实验评估表明，该方法具有稳定的收敛性、改善的空间均匀性和增强的位置分辨率。除了所研究的探测器外，该方法为科学仪器的物理信息自监督校准建立了一个通用框架，并向能够运行期间持续适应的自主智能仪器迈出了一步。在这种范式中，探测器校准不再是实验的先决条件，而是测量过程本身的一个组成部分。
英文摘要
Scientific instruments require accurate calibration to convert detector signals into reliable physical observables. Conventional calibration procedures typically rely on dedicated calibration measurements, analytical response models or labelled reference data, limiting their ability to adapt to changing operating conditions and detector aging. We present a physics-informed self-supervised learning framework that jointly performs wire calibration and interaction position reconstruction in Multi-Wire Parallel Plate Avalanche Counters (MWPPACs) without requiring labelled position measurements or dedicated calibration runs. The method formulates detector calibration as a latent optimization problem in which global wire gains and event-wise interaction positions are estimated simultaneously using supervision derived exclusively from detector geometry and charge-energy consistency constraints. A detector-independent neural network reconstructs sub-wire interaction positions from local charge distributions, eliminating the need to assume analytical induction profiles by learning the detector response directly from experimental data. The end-to-end differentiable framework enables continuous detector self-calibration while improving the uniformity and accuracy of position reconstruction. Experimental evaluation on the entrance MWPPAC tracking detectors of the VAMOS++ magnetic spectrometer demonstrates stable convergence, improved spatial homogeneity and enhanced position resolution. Beyond the detector studied, the method establishes a general framework for physics-informed self-supervised calibration of scientific instruments and is a step toward autonomous intelligent instrumentation capable of continuous adaptation during operation. In this paradigm, detector calibration is no longer a prerequisite for an experiment but an integral part of the measurement process itself.
