论文标题
深田元校准
Deep-field Metacalibration
论文作者
论文摘要
我们引入了深层元校准，这是一种新技术，可通过使用较深的成像调查进行校准，从而降低弱透镜剪切信号的元素映射估计器中的像素噪声。在标准的元素映射中，当估计对象的剪切响应时，添加额外的噪声以纠正剪切图像中的噪声的效果，从而使剪切估计值的不确定性增加了约20％。我们的新的深层元元技术利用了一个单独的，更深的成像调查来计算校准图像噪声的降低较少。我们证明，弱透镜剪切测量具有深田元元能力是公正的，直到二阶剪切效应。我们提供算法将此技术应用于成像调查，并描述如何将其推广到明确依赖对象检测的剪切估计器（例如，元映射）。对于Vera C. Rubin天文台的遗产调查（LSST），弱透镜精确度的改善将取决于在面积和深度，DDF和Main LSST Spers Surveys shoppers soumple soumple soumple soumple shoppers soumple sypers sypers nistin sypers of Pixel噪声中的LSST深钻孔（DDF）观察的某种未知细节（DDF）观察结果。我们保守地估计，精度的降解从20％降低到深田元校准的〜5％或更少，我们主要归因于增加的源密度增加，并降低了像素噪声对整体形状噪声的贡献。最后，我们表明该技术对于LSST DDFS的样品方差很强，由于其较大面积，等效校准误差为〜0.1％。深田元校准技术提供了更高的信噪弱透镜测量值，同时仍满足未来调查的严格系统错误要求。
We introduce deep-field metacalibration, a new technique that reduces the pixel noise in metacalibration estimators of weak lensing shear signals by using a deeper imaging survey for calibration. In standard metacalibration, when estimating the object's shear response, extra noise is added to correct the effect of shearing the noise in the image, increasing the uncertainty on shear estimates by ~ 20%. Our new deep-field metacalibration technique leverages a separate, deeper imaging survey to calculate calibrations with less degradation in image noise. We demonstrate that weak lensing shear measurement with deep-field metacalibration is unbiased up to second-order shear effects. We provide algorithms to apply this technique to imaging surveys and describe how to generalize it to shear estimators that rely explicitly on object detection (e.g., metacalibration). For the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), the improvement in weak lensing precision will depend on the somewhat unknown details of the LSST Deep Drilling Field (DDF) observations in terms of area and depth, the relative point-spread function properties of the DDF and main LSST surveys, and the relative contribution of pixel noise vs. intrinsic shape noise to the total shape noise in the survey. We conservatively estimate that the degradation in precision is reduced from 20% for metacalibration to ~ 5% or less for deep-field metacalibration, which we attribute primarily to the increased source density and reduced pixel noise contributions to the overall shape noise. Finally, we show that the technique is robust to sample variance in the LSST DDFs due to their large area, with the equivalent calibration error being ~ 0.1%. The deep-field metacalibration technique provides higher signal-to-noise weak lensing measurements while still meeting the stringent systematic error requirements of future surveys.
