减轻LSS调查中的污染:方法的比较

N. Weaverdyck, D. Huterer
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引用次数: 14

摘要

未来的大规模结构调查将测量数十亿星系的位置和形状。这种目录的精确度要求对观测到的场的系统性污染进行细致的处理。我们比较了几种现有的从星系团测量中去除这种系统的方法。我们展示了所有的方法,包括流行的伪模式投影和模板减法方法,如何在一个共同的回归框架下解释,并使用它来建议改进的估计器。我们展示了如何使用旨在减轻功率谱中的系统性的方法来生成干净的地图,这对于功率谱以外的宇宙学分析是必要的,并且我们扩展了当前的方法来处理观测到的地图和功率谱中的下阶乘法污染。提出了两种新的缓解方法,它们结合了当前最先进方法的理想特征,同时更易于实施。研究了暗能量调查第5年的一组常见模拟测量中所有方法的性能,我们测试了它们对各种分析案例的鲁棒性。与现有方法相比,我们提出的方法产生了改进的图和功率谱,而几乎不需要用户调整。最后,我们提出了在今后的调查中减轻系统分类学影响的建议,并指出,所提出的方法一般适用于星系分布以外的任何具有空间系统分类学的领域。
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Mitigating contamination in LSS surveys: a comparison of methods
Future large scale structure surveys will measure the locations and shapes of billions of galaxies. The precision of such catalogs will require meticulous treatment of systematic contamination of the observed fields. We compare several existing methods for removing such systematics from galaxy clustering measurements. We show how all the methods, including the popular pseudo-$C_\ell$ Mode Projection and Template Subtraction methods, can be interpreted under a common regression framework and use this to suggest improved estimators. We show how methods designed to mitigate systematics in the power spectrum can be used to produce clean maps, which are necessary for cosmological analyses beyond the power spectrum, and we extend current methods to treat the next-order multiplicative contamination in observed maps and power spectra. Two new mitigation methods are proposed, which incorporate desirable features of current state-of-the-art methods while being simpler to implement. Investigating the performance of all the methods on a common set of simulated measurements from Year 5 of the Dark Energy Survey, we test their robustness to various analysis cases. Our proposed methods produce improved maps and power spectra when compared to current methods, while requiring almost no user tuning. We end with recommendations for systematics mitigation in future surveys, and note that the methods presented are generally applicable beyond the galaxy distribution to any field with spatial systematics.
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