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arXiv 2407.09282astro-ph.CO
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宇宙时间中的二次晕偏置 II:利用聚类信息重建晕的性质

Secondary halo bias through cosmic time II: Reconstructing halo properties using clustering information

Andres Balaguera-Antolinez, Antonio D. Montero-Dorta

AI总结:

本研究探索在校准/学习方法背景下用聚类信息分配暗物质晕性质,通过重建晕性质并约束重现偏置,改进了晕性质分配方法,可复制N体模拟信号,助力模拟目录生成。

摘要:

在基于通过近似、校准或机器学习方法生成的暗物质晕目录集构建星系模拟目录时,此类示踪剂的本征性质分配是至关重要的一步,因为这些性质会影响模拟星系团的丰度和模拟星系的空间分布。我们探索在校准/学习方法的背景下,明确使用聚类信息来分配暗物质晕性质的可能性。目标是仅基于晕分布的相空间性质和暗物质密度场,检索作为重建性质函数的一次和二次大有效偏置的正确信号。该算法重建一组晕性质(如 virial 质量、最大圆周速度、浓度和自旋),并约束其重现一次和二次(或组装)偏置。该算法的关键要素是单独分配的大尺度有效偏置的实现、考虑晕排斥的多尺度方法以及晕性质的分层分配。该方法有助于分配旨在复制大尺度有效偏置(一次和二次)的晕性质。这比文献中先前的方法有所改进,尤其是在高质量群体中。我们设计了一种策略,用于重建通过校准/学习算法获得的暗物质晕的主要性质,其方式是预期的一点和两点统计量(在大尺度上)复制来自详细 N 体模拟的信号。我们鼓励将此策略(或我们算法的实现)应用于基于近似方法的暗物质晕模拟目录的生成。

英文摘要:

When constructing galaxy mock catalogs based on suits of dark matter halo catalogs generated with approximated, calibrated or machine-learning approaches, the assignment of intrinsic properties for such tracers is a step of paramount relevance, given that these can shape the abundance of mock galaxy cluster and the spatial distribution of mock galaxies. We explore the possibility to assign properties of dark matter halos within the context of calibrated/learning approaches, explicitly using clustering information. The goal is to retrieve the correct signal of primary and secondary large-effective bias as a function of properties reconstructed solely based on phase-space properties of the halo distribution and dark matter density field. The algorithm reconstructs a set halo properties (such as virial mass, maximum circular velocity, concentration and spin) constraint to reproduce both primary and secondary (or assembly) bias. The key ingredients of the algorithm are the implementation of individually-assigned large-scale effective bias, a multi-scale approach to account for halo exclusion and a hierarchical assignment of halo properties. The method facilitates the assignment of halo properties aiming at replicating the large-scale effective bias, both primary and secondary. This improves over over previous methods found in the literature, especially at the high mass population. We have designed an strategy to reconstruct the main properties of dark matter halos obtained by calibrated/learning algorithms, in a way that the expected one and two-point statistics (on large scales) replicates the signal from detailed N-body simulations. We encourage the application of this strategy (or the implementation of our algorithm) for the generation of mock catalogs of dark matter halos based on approximated methods.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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