论文标题

$κ$ TNG:带有Illustristng模拟的重晶过程对弱透镜的影响

$κ$TNG: Effect of Baryonic Processes on Weak Lensing with IllustrisTNG Simulations

论文作者

Osato, Ken, Liu, Jia, Haiman, Zoltán

论文摘要

我们研究了基于宇宙学水动力学模拟Illustristng的$κ$ tng,研究带有模拟WL图的弱透镜(WL)可观察物的影响。我们量化了对WL角功率谱,单点概率分布函数(PDF)以及峰值和最小值的数量计数的重体效应。我们还显示了效应的红移演变,这是将重子与基本物理学(例如暗能量,暗物质和大量中微子)区分开的关键。我们发现,重型过程降低了小规模的功率,抑制PDF的尾巴,峰值和最小计数,并改变峰值和最小值的总数。我们将结果与现有的半分析模型和流体动力模拟进行了比较,并讨论了差异的来源。 $κ$ tng套件包括10,000美元的实现$ 5 \ times 5 \,\ mathrm {deg}^2 $映射40个源红移,最高为$ z_s = 2.6 $,很好地涵盖了现有和即将进行的弱透镜调查的范围。我们还制作了$κ$ tng-dark套件的地图套件,该套件仅根据相应的暗物质模拟而产生。我们的模拟图适用于开发包含重子的效果的分析模型,但对于依靠大规模图的研究,例如非高斯统计和与卷积神经网络的机器学习”特别有用。模拟地图的套件可在哥伦比亚镜头(http://columbialensing.org)上公开使用。

We study the effect of baryonic processes on weak lensing (WL) observables with a suite of mock WL maps, the $κ$TNG, based on the cosmological hydrodynamic simulations IllustrisTNG. We quantify the baryonic effects on the WL angular power spectrum, one-point probability distribution function (PDF), and number counts of peaks and minima. We also show the redshift evolution of the effects, which is a key to distinguish the effect of baryons from fundamental physics such as dark energy, dark matter, and massive neutrinos. We find that baryonic processes reduce the small-scale power, suppress the tails of the PDF, peak and minimum counts, and change the total number of peaks and minima. We compare our results to existing semi-analytic models and hydrodynamic simulations, and discuss the source of discrepancies. The $κ$TNG suite includes 10,000 realisations of $5 \times 5 \, \mathrm{deg}^2$ maps for 40 source redshifts up to $z_s = 2.6$, well covering the range of interest for existing and upcoming weak lensing surveys. We also produce the $κ$TNG-Dark suite of maps, generated based on the corresponding dark matter only IllustrisTNG simulations. Our mock maps are suitable for developing analytic models that incorporate the effect of baryons, but also particularly useful for studies that rely on mass maps, such as non-Gaussian statistics and machine learning with convolutional neural networks. The suite of mock maps is publicly available at Columbia Lensing (http://columbialensing.org).

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