AI 中文总结
该研究针对电子成像中Timepix4亚像素质心定位的重建偏差问题，通过分析质心重建的歧义性，提出统一解释并经模拟与实测数据验证，指出质心优化需兼顾相位偏差、重分组性与定位精度。
AI 中文摘要
亚像素质心定位被广泛应用于混合像素探测器，通过估算入射像素内的真实相互作用位置，提升电子成像的空间分辨率。现有质心定位策略通常以定位精度为优化目标，但观测结果显示，定位精度的提升未必能转化为调制传递函数（MTF）的改善或可靠的虚拟像素重分组。本研究基于质心重建问题的歧义性，对这些观测结果提出了统一解释。研究表明，基于可观测变量的质心估计器采用确定性重建规则求解本质上非唯一的逆问题，可能引入与入射相位相关的重建偏差，该偏差决定了重建亚像素坐标的空间分布。此框架解释了为何仅靠定位精度不足以预测成像性能，并确定近似的像素内平移对称性是实现可靠虚拟像素成像的前提。该解释通过模拟200 keV和300 keV电子数据，以及使用Timepix4探测器获取的实测200 keV数据进行评估。对电荷加权、基于时序、基于形态的质心定位策略的对比显示，定位性能相近的估计器可表现出显著不同的MTF和重分组平场行为。结果表明，未来的质心优化应同时考虑亚像素相位偏差、重分组性与定位精度。
英文摘要
Subpixel centroiding is widely used to improve the spatial resolution of hybrid pixel detectors for electron imaging by estimating the true interaction position within an entry pixel. Existing centroiding strategies are typically optimised using localisation accuracy. However, observations show that improved localisation does not necessarily translate into improved modulation transfer function (MTF) or reliable virtual-pixel rebinning. This work proposes a unified interpretation of these observations based on the ambiguity of the centroid reconstruction problem. We show that observable-based centroid estimators solve an intrinsically non-unique inverse problem using deterministic reconstruction rules, which can introduce entry-phase-dependent reconstruction bias that governs the spatial distribution of reconstructed subpixel coordinates. This framework explains why localisation accuracy alone is insufficient to predict imaging performance and identifies approximate intra-pixel translational symmetry as a prerequisite for faithful virtual-pixel imaging. The proposed interpretation is evaluated using simulated 200 keV and 300 keV electron data together with measured 200 keV data acquired with a Timepix4 detector. Comparisons of charge-weighted, timing-based, and morphology-dependent centroiding strategies demonstrate that estimators with similar localisation performance can exhibit markedly different MTFs and rebinned flat-field behaviour. The results suggest that future centroid optimisation should consider subpixel phase bias and rebinnability alongside localisation accuracy.
