论文标题
减轻剪切测量中的噪声源耦合效果
Mitigating the Noise-Source Coupling Effect in Shear Measurement
论文作者
论文摘要
IV期弱透镜调查需要剪切测量中的次级水平准确性。一个重要的挑战是抑制低信噪比的源图像（SNR $ \ lyssim10 $）的剪切偏差。以前，已经证明，在Fourier_quad（FQ）方法中定义的剪切估计器可以通过集合平均在非常微弱的端（SNR $ \ Lessim5 $）上实现次级精度。稍后，发现我们可以通过对称FQ剪切估计器的完整PDF（PDF_SYM方法）来处理最小统计误差（CRAMER-RAO BOND），而不是进行集合平均值。最近，有了大量模拟星系图像，我们能够在微弱的末端识别pdf_sym方法中的少量剪切偏见。在snr $ \ Lessim 10 $上，乘法偏差达到$ 1-2 \ times10^{ - 2} $，并且点差函数（PSF）的各向异性会导致添加偏置，该添加偏置可以达到几次$ 10^{ - 4} $。我们发现这些偏见源于星系功率谱中的噪声源耦合。事实证明，通过在FQ剪切估计器中添加其他术语可以在很大程度上解决此问题。所得的乘法和添加偏见可以分别大大抑制至$ 10^{ - 3} $和$ 10^{ - 5} $的水平。这些校正基本上扩展了可用的SNR范围，以使用PDF_SYM方法进行准确的剪切测量。
Sub-percent level accuracy in shear measurement is required by the Stage-IV weak lensing surveys. One important challenge is about suppressing the shear bias on source images of low signal-to-noise ratios (SNR$\lesssim10$). Previously, it has been demonstrated that the shear estimators defined in the Fourier_Quad (FQ) method can achieve sub-percent accuracy at the very faint end (SNR$\lesssim5$) through ensemble averaging. Later, it is found that we can approach the minimum statistical error (the Cramer-Rao Bound) by symmetrizing the full PDF of the FQ shear estimators (the PDF_SYM approach), instead of taking ensemble averages. Recently, with a large amount of mock galaxy images, we are able to identify some small amount of shear biases in the PDF_SYM approach at the faint end. The multiplicative bias goes up to $1-2\times10^{-2}$ at SNR $\lesssim 10$, and the anisotropy of the point spread function (PSF) causes an additive bias that can reach a few times $10^{-4}$. We find that these biases originate from the noise-source coupling in the galaxy power spectrum. It turns out that this problem can be largely fixed by adding additional terms to the FQ shear estimators. The resulting multiplicative and additive biases can be significantly suppressed to the level of $10^{-3}$ and $10^{-5}$ respectively. These corrections substantially extend the available SNR range for accurate shear measurement with the PDF_SYM approach.
