改进的ACS/WFC成像中卫星轨迹识别方法：基于改进的Radon变换
Improved Identification of Satellite Trails in ACS/WFC Imaging Using a Modified Radon Transform
AI总结：
本文提出改进的Radon变换方法，用于识别ACS/WFC成像中的卫星轨迹，提高了识别准确率并降低了误检率。
摘要：
我们提出了一种新的方法，利用改进的Radon变换来识别ACS/WFC成像数据中的卫星轨迹（或其他线性伪影）。我们证明该方法对具有平均亮度显著低于背景噪声水平的特征敏感，并且在大多数情况下对明亮的天体源（如恒星、星系）的影响具有抗性。与通过肉眼识别的一组卫星轨迹相比，我们发现轨迹恢复率为85%，在去除易被过滤的衍射尖峰后，误检率为2.5%。通过使用一个更大的ACS/WFC数据集进行分析，其中误判的轨迹通过在相同场的多张图像中持续存在来识别，我们确定了Radon变换参数空间和图像属性中我们的算法不可靠的区域，并估计其他区域的误检率为约10%。我们应用该方法分析了2002年至2022年间ACS/WFC数据，以确定科学数据中卫星轨迹污染的频率以及轨迹亮度随时间的变化。我们发现过去二十年中卫星轨迹污染率增加了约两倍，但轨迹亮度的典型值没有明显的系统性演变。我们的卫星轨迹识别程序作为\texttt{acstools}包的一部分提供。
英文摘要：
We present a new approach to identify satellite trails (or other linear artifacts) in ACS/WFC imaging data using a modified Radon Transform. We demonstrate that this approach is sensitive to features with mean brightness significantly below the background noise level, and it is resistant to the influence of bright astronomical sources (e.g., stars, galaxies) in most cases. Comparing with a set of satellite trails identified by eye, we find a trail recovery rate of 85\% and a false detection rate (after removing diffraction spikes that are easily filtered) of 2.5\%. By performing an analysis using a much larger ACS/WFC data set where false trails are identified by their persistence across multiple images of the same field, we identify the Radon Transform parameter space and image properties where our algorithm is unreliable, and estimate a false detection rate of $\sim10\%$ elsewhere. We apply our method to ACS/WFC data taken between 2002 and 2022 to determine both the frequency of satellite trail contamination in science data and also the typical trail brightness as a function of time. We find the rate of satellite trail contamination has increased by approximately a factor of two in the last two decades, but there is no clear systematic evolution in the typical trail brightness. Our satellite trail identification program is available as part of the \texttt{acstools} package.
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