Disentangling magnification in combined shear-clustering analyses
Disentangling magnification in combined shear-clustering analyses
复制标题
组合剪切聚类分析中的解缠结放大倍数
DOI：
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发表时间：
2019
影响因子：
4.8
通讯作者：
D. Alonso
中科院分区：
文献类型：
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作者：
L. Thiele;C. Duncan;D. Alonso
我们调查的灵敏度透镜放大率的影响，结合测光宇宙剪切和星系聚类数据（即现在通常称为“3 × 2点”分析）的大尺度结构分析。使用Fisher矩阵偏差形式主义，我们解开的贡献，宇宙学参数的偏差所造成的忽略放大的影响，在理论拟合从数据向量中的各个元素，第三阶段和第四阶段的调查。我们表明，删除的数据向量的放大率占主导地位的元素，并不能保证减少宇宙学的偏见，由于放大信号，但可以增加放大的灵敏度。我们发现，最敏感的元素的数据向量来自剪切聚类互相关，特别是最高的红移剪切仓和任何较低的红移透镜样本之间，参数ΩM，$S_8=\sigma _8\sqrt{\Omega _\mathrm{ M}/0.3}$，和w 0显示最显着的偏差两个调查模型。我们的预测预测，目前的分析并没有显着的放大偏见，但这种偏见将成为高度显着的统计能力在不久的将来继续增加。因此，我们得出结论，未来的调查应该测量和建模放大率作为他们的旗舰“3 × 2点”分析的一部分。
We investigate the sensitivity to the effects of lensing magnification on large-scale structure analyses combining photometric cosmic shear and galaxy clustering data (i.e. the now commonly called ‘3 × 2-point’ analysis). Using a Fisher matrix bias formalism, we disentangle the contribution to the bias on cosmological parameters caused by ignoring the effects of magnification in a theory fit from individual elements in the data vector, for Stage-III and Stage-IV surveys. We show that the removal of elements of the data vectors that are dominated by magnification does not guarantee a reduction in the cosmological bias due to the magnification signal, but can instead increase the sensitivity to magnification. We find that the most sensitive elements of the data vector come from the shear-clustering cross-correlations, particularly between the highest redshift shear bin and any lower redshift lens sample, and that the parameters ΩM, $S_8=\sigma _8\sqrt{\Omega _\mathrm{ M}/0.3}$, and w0 show the most significant biases for both survey models. Our forecasts predict that current analyses are not significantly biased by magnification, but this bias will become highly significant with the continued increase of statistical power in the near future. We therefore conclude that future surveys should measure and model the magnification as part of their flagship ‘3 × 2-point’ analysis.
影响因子：
4.8
作者：
C. Duncan;C. Heymans;A. Heavens;B. Joachimi
通讯作者：
C. Duncan;C. Heymans;A. Heavens;B. Joachimi
影响因子：
4.8
作者：
C. Duncan;B. Joachimi;A. Heavens;C. Heymans;H. Hildebrandt
通讯作者：
C. Duncan;B. Joachimi;A. Heavens;C. Heymans;H. Hildebrandt