发表机构
ASTRON, Netherlands Institute for Radio Astronomy（荷兰射电天文研究所（ASTRON））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对弱信号被强干扰掩盖的问题，提出利用归一化流模型，基于统计不变性和最小初始抑制假设，有效恢复校准过程中丢失的信号成分，并通过模拟数据验证了其有效性。
AI 中文摘要
在许多科学领域中，感兴趣的弱信号往往被强度高出几个数量级的主导干扰信号所掩盖。恢复这些弱信号需要减去主导信号；然而，这一校准过程本质上会扭曲或部分抑制目标信号。为解决此问题，我们提出使用归一化流模型来重建受校准影响的弱信号。通过利用目标信号的统计不变性并假设初始抑制最小，我们的框架能够有效恢复丢失的信号成分。我们提供了该基于归一化流的恢复方法的全面理论概述，并使用模拟数据证明了其有效性。
英文摘要
In many scientific disciplines, weak signals of interest are obscured by dominant nuisance signals that are several orders of magnitude stronger. Recovering these weak signals requires subtracting the dominant ones; however, this calibration process inherently distorts or partially suppresses the underlying signal of interest. To address this problem, we propose the use of normalizing flow models to reconstruct calibration-affected weak signals. By leveraging the statistical invariance of the target signals and assuming minimal initial suppression, our framework effectively recovers the lost signal components. We provide a comprehensive theoretical overview of this normalizing flow-based recovery method and demonstrate its efficacy using simulated data.
