Data compression and covariance matrix inspection: Cosmic shear
Data compression and covariance matrix inspection: Cosmic shear
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数据压缩和协方差矩阵检查：宇宙剪切
DOI：
10.1103/physrevd.103.103535
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发表时间：
2021
影响因子：
5
通讯作者：
Dodelson, Scott
中科院分区：
文献类型：
--
作者：
Ferreira, Tassia;Zhang, Tianqing;Chen, Nianyi;Dodelson, Scott
协方差矩阵是端到端宇宙学分析中最困难的部分。原则上，对于两点函数，每个分量都涉及一个四点函数，所得协方差通常有数十万个元素。我们调查各种压缩机制，能够大大减少宇宙剪切统计的背景下的协方差矩阵的大小。这有助于确定其哪些部分对参数估计最关键。我们从简单的压缩方法开始，通过隔离和“移除”与最低特征值相关的200种模式，然后是那些具有最低信噪比的模式，然后再转向更复杂的方案，如层析成像水平的压缩，最后是大规模优化参数估计和数据压缩（MOPED）。我们发现，虽然大多数这些方法被证明是有用的几个参数的兴趣，像，最简单的产生的内在对齐（IA）参数以及约束力的损失。对于所考虑的情况--来自暗能量巡天第一年数据的宇宙剪切--只有MOPED能够在16参数空间中复制原始约束。最后，我们应用公差测试与MOPED得到的压缩协方差矩阵的元素，并确认IA parameters是最容易受到不准确的协方差矩阵。
Covariance matrices are among the most difficult pieces of end-to-end cosmological analyses. In principle, for two-point functions, each component involves a four-point function, and the resulting covariance often has hundreds of thousands of elements. We investigate various compression mechanisms capable of vastly reducing the size of the covariance matrix in the context of cosmic shear statistics. This helps identify which of its parts are most crucial to parameter estimation. We start with simple compression methods, by isolating and “removing” 200 modes associated with the lowest eigenvalues, then those with the lowest signal-to-noise ratio, before moving on to more sophisticated schemes like compression at the tomographic level and, finally, with the massively optimized parameter estimation and data compression (MOPED). We find that, while most of these approaches prove useful for a few parameters of interest, like, the simplest yield a loss of constraining power on the intrinsic alignment (IA) parameters as well as. For the case considered—cosmic shear from the first year of data from the Dark Energy Survey—only MOPED was able to replicate the original constraints in the 16-parameter space. Finally, we apply a tolerance test to the elements of the compressed covariance matrix obtained with MOPED and confirm that the IA parameteris the most susceptible to inaccuracies in the covariance matrix.
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