Constrained correlation functions from the Millennium Simulation

Constrained correlation functions from the Millennium Simulation
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千年模拟中的约束相关函数

DOI:
10.1051/0004-6361/201525906
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发表时间:
2015
期刊:
arXiv: Cosmology and Nongalactic Astrophysics
影响因子:
--
通讯作者:
Schneider
Schneider
中科院分区:
--
文献类型:
--
作者:
Wilking;Röseler;Schneider

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上下文在以前的工作中,我们开发了一个准高斯近似的相关函数,结合相关函数的基本数学约束的可能性,在通常的高斯方法。这些约束的分析计算是唯一可行的情况下的相关函数的一维randomfields.AimsIn这项工作中,我们的目标是获得相应的约束在高维randomfields.MethodsWe开发的情况下,测试它们在一个更现实的context.MethodsWe数值方法计算的相关函数的约束,也适用于二维和三维的字段。为了测试数值获得的约束的准确性,我们将它们与一维情况下的分析结果进行比较。最后,我们计算相关函数从晕目录的千年模拟,检查他们是否遵守的约束,并检查在建设中使用的准高斯likelihood.ResultsWe发现,我们的数值方法计算的约束是强大的,相关函数测量的千年模拟遵守他们的转换的性能。即使测量的相关函数完全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,我们发现强有力的迹象表明,准高斯似然产生一个更准确的描述比高斯的。
ContextIn previous work, we developed a quasi-Gaussian approximation for the likelihood of correlation functions that incorporates fundamental mathematical constraints on correlation functions, in contrast to the usual Gaussian approach. The analytical computation of these constraints is only feasible in the case of correlation functions of one-dimensional random fields.AimsIn this work, we aim to obtain corresponding constraints in the case of higher dimensional random fields and test them in a more realistic context.MethodsWe develop numerical methods of computing the constraints on correlation functions that are also applicable for two- and three-dimensional fields. 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.ResultsWe find that our numerical methods of computing the constraints are robust and that the correlation functions measured from the Millennium Simulation obey them. Even though 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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