An empirical method for mitigating an excess up-scattering mass bias on the weak lensing mass estimates for shear-selected cluster samples
An empirical method for mitigating an excess up-scattering mass bias on the weak lensing mass estimates for shear-selected cluster samples
复制标题
用于减轻剪切选择簇样本的弱透镜质量估计上的过度上散射质量偏差的经验方法
DOI：
10.1093/pasj/psac085
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发表时间：
2022
影响因子：
2.3
通讯作者：
Hamana Takashi
中科院分区：
文献类型：
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作者：
Yasuki Nagai;Masako Kawabata;Shintaro Hashimoto;Kazuaki Tsukada;Kazuyuki Hashimoto;Shoji Motoishi;Hideya Saeki;Arata Motomur;Futoshi Minato;Masatoshi Itoh;S. Okukawa et al.;川田和正;川島輝能;榊原陽平;M. Anzorena;﨏隆志;片寄祐作;M. Anzorena;M. Anzorena;川島輝能;Y. Yokoe;Y. Yokoe;Hamana Takashi
弱透镜星系团质量估计上的过量上散射质量偏差是一种统计偏差，即星系团中观测到的弱透镜质量（Mobs）在统计意义上大于其真实质量（Mtrue），因为弱透镜星系团剪切曲线中随机噪声导致的上散射比下散射的机会更高。这种非对称散射概率是由团质量函数随质量的增加而单调减小引起的。我们研究了弱透镜剪切选择簇中的这种偏差（定义为b=Mobs/Mtrue），并提出了一种减轻这种偏差的经验方法。在此过程中，我们对现实模拟簇进行了标准的弱透镜质量估计，并发现基于标准χ2分析的弱透镜质量估计给出了统计上正确的置信区间，但最终的最佳拟合质量平均偏高。我们的修正方法使用标准贝叶斯统计的框架，并使用最近经验模型中聚类质量和浓度参数的概率分布的先验。我们使用模拟弱透镜簇测试了我们的校正方法，并发现该方法在校正后的mobs -bin平均质量偏差接近统一的情况下效果很好。我们将校正方法应用于Hamana， Shirasaki, and Lin （2020， PASJ, 72, 78）的弱透镜剪切选择簇样本，并给出了偏差校正的弱透镜团质量。
An excess up-scattering mass bias on a weak lensing cluster mass estimate is a statistical bias that an observed weak lensing mass (Mobs) of a cluster of galaxies is, in a statistical sense, larger than its true mass (Mtrue) because of a higher chance of up-scattering than that of down-scattering due to random noises in a weak lensing cluster shear profile. This non-symmetric scattering probability is caused by a monotonically decreasing cluster mass function with increasing mass. We examine this bias (defined byb=Mobs/Mtrue) in weak lensing shear-selected clusters, and present an empirical method for mitigating it. In so doing, we perform the standard weak lensing mass estimate of realistic mock clusters, and find that the weak lensing mass estimate based on the standard χ2analysis gives a statistically correct confidence intervals, but resulting best-fitting masses are biased high on average. Our correction method uses the framework of the standard Bayesian statistics with the prior of the probability distribution of the cluster mass and concentration parameter from recent empirical models. We test our correction method using mock weak lensing clusters, and find that the method works well with resulting correctedMobs-bin averaged mass biases being close to unity within. We applied the correction method to weak lensing shear-selected cluster sample of Hamana, Shirasaki, and Lin (2020, PASJ, 72, 78), and present bias-corrected weak lensing cluster masses.