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arXiv 2012.06795astro-ph.CO
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流体动力学模拟的偏差:重子物理学到暗物质场的映射

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

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

摘要:

本文研究了从最先进的流体动力学模拟中获得的关于暗物质场所覆盖的宇宙网络的重子物理组装偏置关系的层次结构。使用偏置分配方法(BAM),我们发现,非本地偏置起着核心作用。我们根据曲率张量的不变量对宇宙网进行分类,曲率张量不仅由引力势定义,而且特别是由过密度定义,因为小尺度聚类在这种情况下变得很重要。首先,气体密度偏差关系可以直接映射到暗物质密度场,以高精度利用它们之间的强相关性。在第二步中,基于暗物质和气体密度场绘制中性氢。最后,根据前面的量绘制温度。这使我们能够在相同的模拟体积内统计重建重子性质,在两点统计中找到10 - 10精度,在三点统计中找到兼容的结果,一般在1- $σ$内,相对于参考模拟(计算时间少5到6个数量级)。这为建立最佳设置铺平了道路,用于构建探测星系间介质的模拟,以便在即将进行的大型任务(如DESI,Euclid,J-PAS和WEAVE)的统计分析中生成这些关键成分。

英文摘要:

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.

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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