The impact of signal-to-noise, redshift, and angular range on the bias of weak lensing 2-point functions
The impact of signal-to-noise, redshift, and angular range on the bias of weak lensing 2-point functions
复制标题
信噪比、红移和角度范围对弱透镜 2 点函数偏差的影响
DOI：
10.21105/astro.2007.07253
复制
发表时间：
2020
影响因子：
--
通讯作者：
E. Sellentin
中科院分区：
文献类型：
--
作者：
A. Louca;E. Sellentin
弱透镜数据遵循自然倾斜的分布，这意味着从调查中产生的数据向量最有可能系统性地低于其平均值。尽管从CMB分析中可以定性地知道这一影响，但在弱透镜中正确地解释它是具有挑战性的，因为直接转移CMB结果在数量上是不正确的。而之前的一项研究(Sellentin等人)2018)集中于这种偏差的大小，我们在这里重点关注这种偏差的频率、其与红移的比例，以及它对调查的信噪比的影响。用COSEBIS过滤弱透镜数据，我们发现弱透镜似然在$\ell\约100$处倾斜，而CMB-似然已经在$\ell\约20$处高斯化。虽然COSEBI压缩的儿童数据-以及DES-类似红移-和角度范围服从高斯分布，但我们检测到欧几里德或LSST类数据集的一半存在6$\sigma$显著的偏度，这是由于这些调查的覆盖范围更广和覆盖范围更深造成的。计算每个数据点的信噪比，我们精确地表明，信噪比最高的数据点是最有偏差的。在所有红移中，这种偏差至少影响调查总信噪比的10%，高红移时高达25%。因此，这种偏差预计会影响参数推断。这种偏向可以通过发展非高斯概率来处理。否则，可以通过去除信噪比最高的数据点来降低它。
Weak lensing data follow a naturally skewed distribution, implying the data vector most likely yielded from a survey will systematically fall below its mean. Although this effect is qualitatively known from CMB-analyses, correctly accounting for it in weak lensing is challenging, as a direct transfer of the CMB results is quantitatively incorrect. While a previous study (Sellentin et al. 2018) focused on the magnitude of this bias, we here focus on the frequency of this bias, its scaling with redshift, and its impact on the signal-to-noise of a survey. Filtering weak lensing data with COSEBIs, we show that weak lensing likelihoods are skewed up until $\ell \approx 100$, whereas CMB-likelihoods Gaussianize already at $\ell \approx 20$. While COSEBI-compressed data on KiDS- and DES-like redshift- and angular ranges follow Gaussian distributions, we detect skewness at 6$\sigma$ significance for half of a Euclid- or LSST-like data set, caused by the wider coverage and deeper reach of these surveys. Computing the signal-to-noise ratio per data point, we show that precisely the data points of highest signal-to-noise are the most biased. Over all redshifts, this bias affects at least 10% of a survey's total signal-to-noise, at high redshifts up to 25%. The bias is accordingly expected to impact parameter inference. The bias can be handled by developing non-Gaussian likelihoods. Otherwise, it could be reduced by removing the data points of highest signal-to-noise.