arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1911.03474astro-ph.COgr-qc
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用内部误差估计技术从5年SNLS数据估计Ia型超新星的弱透镜收敛关联

Estimating weak lensing convergence correlation of Type-Ia supernovae from 5-year SNLS data by internal error estimate technique

Ayan Mitra, Arnab Paul, Barun Pal, Supratik Pal

摘要:

我们报道了5年超新星遗产巡天数据中Ia型超新星的非零弱透镜收敛相关信号。对于我们的分析,我们利用296超新星放大数据从5年SNLS在弱透镜极限。我们使用的数据由四个不同的补丁组成,每个补丁覆盖1平方度的天空,合并在一起。我们证明,它是可能有一个很好的估计两点关联函数从这个数据使用内部误差估计技术。为了有一个很好的估计相应的协方差矩阵,我们应用自举空间重采样技术,我们重新洗牌的原始数据组成的296个数据点100 - 10000倍,并比较从原始数据点获得的结果。我们表明,这种技术可以帮助我们得出一个可靠的结论弱透镜收敛,即使原始数据集包括少量的数据点。这也允许我们以很高的精度计算相应的协方差矩阵。

英文摘要:

We report non-zero weak lensing convergence correlation signal of Type-Ia supernovae from 5-year Supernovae Legacy Survey data. For our analysis we utilize 296 supernovae magnification data from 5-year SNLS in the weak lensing limit. The data we use consists of measurements from four different patches, each covering 1 square degree of the sky, merged together. We demonstrate that it is possible to have a very good estimate of the two point correlation function from this data using internal error estimate technique. In order to have a good estimate of the corresponding covariance matrix we apply bootstrap spatial re-sampling technique where we reshuffle the original data consisting of 296 data points 100-10000 times and compare the results with that obtained from original data points. We show that this technique helps us arrive at a reliable conclusion on weak lensing convergence even though the original dataset comprises of a small number of data points. This also allows us to compute the corresponding covariance matrix with great accuracy.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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