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arXiv 1502.04491astro-ph.COstat.AP
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来自千年模拟的约束关联函数

Constrained correlation functions from the Millennium Simulation

Philipp Wilking, Randolf Röseler, Peter Schneider

摘要:

上下文在以前的工作中,我们开发了一个准高斯近似的可能性的关联函数,这与通常的高斯方法,结合了基本的数学约束关联函数。这些约束的解析计算仅在一维随机场的关联函数的情况下是可行的。目的。在这项工作中,我们的目标是在高维随机场的情况下获得相应的约束,并在更现实的情况下测试它们。方法.我们开发的数值方法来计算关联函数,这也适用于二维和三维领域的约束。为了测试数值得到的约束的准确性,我们将它们与一维情况下的分析结果进行比较。最后,我们计算关联函数从晕目录的千年模拟,检查它们是否服从约束,并检查性能的转换中使用的建设的准高斯似然。结果我们发现,我们的数值方法计算的约束是强大的,从千年模拟测量的关联函数服从它们。尽管测得的关联函数位于参数空间的允许区域内,即远离由约束定义的允许体积的边界,但我们发现强有力的迹象表明,准高斯似然产生比高斯更准确的描述。

英文摘要:

Context. In previous work, we developed a quasi-Gaussian approximation for the likelihood of correlation functions, which, in contrast to the usual Gaussian approach, incorporates fundamental mathematical constraints on correlation functions. The analytical computation of these constraints is only feasible in the case of correlation functions of one-dimensional random fields. Aims. In this work, we aim to obtain corresponding constraints in the case of higher-dimensional random fields and test them in a more realistic context. Methods. We develop numerical methods to compute the constraints on correlation functions which are also applicable for two- and three-dimensional fields. In order to test the accuracy of the numerically obtained constraints, we compare them to the analytical results for the one-dimensional case. Finally, we compute correlation functions from the halo catalog of the Millennium Simulation, check whether they obey the constraints, and examine the performance of the transformation used in the construction of the quasi-Gaussian likelihood. Results. We find that our numerical methods of computing the constraints are robust and that the correlation functions measured from the Millennium Simulation obey them. Despite the fact that the measured correlation functions lie well inside the allowed region of parameter space, i.e. far away from the boundaries of the allowed volume defined by the constraints, we find strong indications that the quasi-Gaussian likelihood yields a substantially more accurate description than the Gaussian one.

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