Testing the lognormality of the galaxy and weak lensing convergence distributions from Dark Energy Survey maps
Testing the lognormality of the galaxy and weak lensing convergence distributions from Dark Energy Survey maps
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DOI：
10.1093/mnras/stw2106
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发表时间：
2017-04-01
影响因子：
4.8
通讯作者：
Walker, A. R.
中科院分区：
文献类型：
--
作者：
Clerkin, L.;Kirk, D.;Walker, A. R.
众所周知，星系密度对比度的概率分布函数（PDF）近似为对数正态分布;而来自弱透镜收敛的质量涨落（kappa（WL））的概率分布函数是否为对数正态分布则不太确定。我们通过细胞计数（CiC）方法推导出星系和投影物质密度分布的PDF。我们使用的星系图和弱透镜收敛产生的暗能量调查科学验证数据超过139度（2）。我们测试是否是一个对数正态分布的星系，收敛和他们的联合PDF的基本密度对比度很好地描述。我们确认，星系密度对比度分布是很好的模拟对数正态PDF卷积泊松噪声在角尺度从10到40弧分（对应于3-10兆秒差距的物理尺度）。我们注意到，由于kappa（WL）是沿视线沿着的质量波动的加权和，其PDF预计仅近似对数正态。我们发现，kappa（WL）分布是很好地模拟对数正态PDF卷积高斯形状噪声在10和20弧分之间的尺度，与最佳拟合卡方（2）/dof为1.11相比，1.84高斯模型，对应的p值分别为0.35和0.07，在10弧分的尺度。在20弧分以上，简单的高斯模型就足够了。联合PDF也合理地拟合了一个二元对数正态分布。作为一致性检查，我们比较了对数正态建模的方差与通过CiC直接测量的方差。我们的方法进行了验证，对地图的MICE大挑战N体模拟。
It is well known that the probability distribution function (PDF) of galaxy density contrast is approximately lognormal; whether the PDF of mass fluctuations derived from weak lensing convergence (kappa(WL)) is lognormal is less well established. We derive PDFs of the galaxy and projected matter density distributions via the counts-in-cells (CiC) method. We use maps of galaxies and weak lensing convergence produced from the Dark Energy Survey Science Verification data over 139 deg(2). We test whether the underlying density contrast is well described by a lognormal distribution for the galaxies, the convergence and their joint PDF. We confirm that the galaxy density contrast distribution is well modelled by a lognormal PDF convolved with Poisson noise at angular scales from 10 to 40 arcmin (corresponding to physical scales of 3-10 Mpc). We note that as kappa(WL) is a weighted sum of the mass fluctuations along the line of sight, its PDF is expected to be only approximately lognormal. We find that the kappa(WL) distribution is well modelled by a lognormal PDF convolved with Gaussian shape noise at scales between 10 and 20 arcmin, with a best-fitting chi(2)/dof of 1.11 compared to 1.84 for a Gaussian model, corresponding to p-values 0.35 and 0.07, respectively, at a scale of 10 arcmin. Above 20 arcmin a simple Gaussian model is sufficient. The joint PDF is also reasonably fitted by a bivariate lognormal. As a consistency check, we compare the variances derived from the lognormal modelling with those directly measured via CiC. Our methods are validated against maps from the MICE Grand Challenge N-body simulation.