论文标题

流体动力模拟的偏见:将重子物理学映射到暗物质领域

The bias from hydrodynamic simulations: mapping baryon physics onto dark matter fields

论文作者

Sinigaglia, Francesco, Kitaura, Francisco-Shu, Balaguera-Antolínez, Andrés, Nagamine, Kentaro, Ata, Metin, Shimizu, Ikkoh, Sánchez-Benavente, Manuel

论文摘要

本文研究了从最先进的流体动力学模拟获得的Baryon物理组装偏置关系的层次结构,相对于暗物质领域跨越的基本宇宙网络。使用偏置分配方法(BAM),我们发现非本地偏置起着核心作用。我们基于曲率张量的不变性对宇宙网络进行分类,不仅是由重力电位定义的,尤其是由过度密度定义的,因为在这种情况下,小规模的聚类变得很重要。首先,可以将气体密度偏置关系直接映射到暗物质密度场上,以高精度利用它们之间的强相关性。在第二步中,根据暗物质和气体密度场映射中性氢。最后,根据先前的数量映射温度。这使我们能够在两点统计数据中统计地重建BARYON属性,并在三点统计中兼容的结果中,在三点统计中兼容结果,通常在1- $σ$之内,就参考模拟而言(有5至6个量级的计算时间订单订单较少的计算时间)。这为建立模拟的最佳设置铺平了道路,以探测在大型即将执行的任务(例如DESI,Euclid,J-PAS和Weave)的统计分析中,以探测这种关键成分的生成此类关键成分。

This paper investigates the hierarchy of baryon physics assembly bias relations obtained from state-of-the-art hydrodynamic simulations with respect to the underlying cosmic web spanned by the dark matter field. Using the Bias Assignment Method (BAM) we find that non-local bias plays a central role. We classify the cosmic web based on the invariants of the curvature tensor defined not only by the gravitational potential, but especially by the over-density, as small scale clustering becomes important in this context. First, the gas density bias relation can be directly mapped onto the dark matter density field to high precision exploiting the strong correlation between them. In a second step, the neutral hydrogen is mapped based on the dark matter and the gas density fields. Finally, the temperature is mapped based on the previous quantities. This permits us to statistically reconstruct the baryon properties within the same simulated volume finding percent-precision in the two-point statistics and compatible results in the three-point statistics, in general within 1-$σ$, with respect to the reference simulation (with 5 to 6 orders of magnitude less computing time). This paves the path to establish the best set-up for the construction of mocks probing the intergalactic medium for the generation of such key ingredients in the statistical analysis of large forthcoming missions such as DESI, Euclid, J-PAS and WEAVE.

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