[论文解读] Metadetection: Mitigating Shear-dependent Object Detection Biases with Metacalibration

E. Sheldon, M. R. Becker+2 more|arXiv (Cornell University)|Nov 6, 2019

Adaptive optics and wavefront sensing参考文献 1被引用 7

一句话总结

本文提出元检测（metadetection）方法，将目标检测整合进元校准（metacalibration）以消除弱引力透镜测量中与剪切相关的偏差。通过在图像区域施加人工剪切，并对剪切后的图像执行检测与测量，该技术可校正因重叠导致的混合偏差，在高度混合的场景中仍能达到亚百分之一的精度。

ABSTRACT

Metacalibration is a new technique for measuring weak gravitational lensing shear that is unbiased for isolated galaxy images. In this work we test metacalibration with overlapping, or galaxy images. Using standard metacalibration, we find a few percent bias for galaxy densities relevant for current surveys, and that this bias increases with increasing galaxy number density. We show that this bias is not due to blending itself, but rather to shear-dependent object detection. If object detection is shear independent, no deblending of images is needed, in principle. We demonstrate that detection biases are accurately removed when including object detection in the metacalibration process, a technique we call metadetection. This process involves applying an artificial shear to images of small regions of sky, and performing detection and measurement on the sheared images in order to calculate a shear response. We show that the method works up to second-order shear effects even in highly blended scenes. However, because the space between objects is sheared coherently in metadetection, the accuracy is ultimately limited by how closely this process matches real data, in which some, but not all, galaxies images are sheared coherently. We find that even for the worst case scenario, in which the space between objects is completely unsheared, the bias is at most a few tenths of a percent for future surveys. We show that the primary technical challenge for metadetection, deconvolution using a spatially varying point-spread-function, does not result in a significant bias for typical imaging surveys. Finally, we discuss additional technical challenges that must be met in order to implement metadetection for real surveys.

研究动机与目标

• 解决因重叠星系图像中目标检测导致的弱引力透镜测量中与剪切相关的偏差。
• 开发一种将目标检测整合进元校准过程的方法，以消除系统性误差。
• 在高星系密度和空间变化的PSF等真实巡天条件下，评估该方法的性能。
• 识别并缓解在未来的弱引力透镜巡天中实施元检测时面临的技术挑战。

提出的方法

• 对天空图像的小区域施加人工剪切，以模拟引力透镜效应。
• 在剪切后的图像上执行目标检测与测量，以计算剪切响应。
• 将检测与测量整合进元校准框架，形成‘元检测’（metadetection）。
• 使用空间变化的点扩散函数（PSFs）来模拟真实的观测条件。
• 在高度混合的场景中考虑二阶剪切效应。
• 在不同星系密度和图像区域间剪切相干性变化的条件下验证该方法。

实验结果

研究问题

• RQ1 在重叠星系的情况下，与剪切相关的检测如何在标准元校准中引入偏差？
• RQ2 将检测整合进元校准是否能消除高密度星场中与剪切相关的偏差？
• RQ3 空间变化的PSF对典型成像巡天中元检测精度有何影响？
• RQ4 物体间剪切的相干性如何影响元检测性能？
• RQ5 在实际弱引力透镜巡天中部署元检测的主要技术挑战是什么？

主要发现

• 在当前巡天典型的星系密度下，标准元校准表现出百分之几的偏差，且随星系数量密度增加而增大。
• 偏差并非源于混合本身，而是源于与剪切相关的检测行为，而非去混合过程。
• 元检测通过将检测纳入元校准过程，消除了这种由检测引起的偏差。
• 即使在最坏情况下（即物体间区域未被剪切），未来巡天中的偏差仍低于0.3%。
• 使用空间变化的PSF进行去卷积在典型成像巡天中不会引入显著偏差。
• 主要技术挑战在于准确建模PSF变化，但实践中这并不会导致显著偏差。