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arXiv 2203.12669astro-ph.CO
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基于机器学习的模拟Halo聚类统计质量分辨效应校正方法

A Machine Learning Approach to Correct for Mass Resolution Effects in Simulated Halo Clustering Statistics

Daniel Forero-Sánchez, Chia-Hsun Chuang, Sergio Rodríguez-Torres, Gustavo Yepes, Stefan Gottlöber, Cheng Zhao

摘要:

宇宙学调查中观测量的增加对模拟准备工作提出了各种挑战。首先,所需的模拟量与观测成比例地增加。然而,大容量模拟很快变得难以计算。其次,正在进行和未来的大规模调查的目标是较小的物体,如发射线星系,与早期的重点,即发光的红色星系相比。它们要求模拟具有更高的质量分辨率。在这项工作中,我们提出了一种机器学习(ML)方法来校准低分辨率(LR)模拟的光环目录,通过使用具有相同背景白噪声的配对高分辨率(HR)模拟进行训练,因此我们可以通过将HR光环与LR光环以一对一的方式匹配来构建训练数据。经过训练后,校准的LR晕目录再现了在尺度$k<1~h\,\rm Mpc^{-1}$的$5~{\rm per~cent}$内质量下降到$2.5\times 10^{11}~h^{-1}M_\odot$的质量-聚类关系。我们验证了不同统计方法的性能,包括晕质量函数、功率谱、两点关联函数和双谱在实空间和红移空间中的性能。我们的方法产生高分辨率的光环目录($>200$粒子每晕)从低分辨率目录($>25$粒子每晕)包含每个对象的校正晕质量。这允许绕过大体积真实高分辨率模拟的计算负担,而不会在结果的质量分辨率上有太大的妥协。与$N$ -身体模拟的成本(例如,数百万CPU-小时)相比,我们的ML方法的成本($\sim 1$ CPU-小时)可以忽略不计,所需的计算时间减少了8倍。

英文摘要:

The increase in the observed volume in cosmological surveys imposes various challenges on simulation preparations. Firstly, the volume of the simulations required increases proportionally to the observations. However, large-volume simulations are quickly becoming computationally intractable. Secondly, on-going and future large-volume survey are targeting smaller objects, e.g. emission line galaxies, compared to the earlier focus, i.e. luminous red galaxies. They require the simulations to have higher mass resolutions. In this work we present a machine learning (ML) approach to calibrate the halo catalogue of a low-resolution (LR) simulation by training with a paired high-resolution (HR) simulation with the same background white noise, thus we can build the training data by matching HR haloes to LR haloes in a one-to-one fashion. After training, the calibrated LR halo catalogue reproduces the mass-clustering relation for mass down to $2.5\times 10^{11}~h^{-1}M_\odot$ within $5~{\rm per~cent}$ at scales $k<1~h\,\rm Mpc^{-1}$. We validate the performance of different statistics including halo mass function, power spectrum, two-point correlation function, and bispectrum in both real and redshift space. Our approach generates high-resolution-like halo catalogues ($>200$ particles per halo) from low-resolution catalogues ($>25$ particles per halo) containing corrected halo masses for each object. This allows to bypass the computational burden of a large-volume real high-resolution simulation without much compromise in the mass resolution of the result. The cost of our ML approach ($\sim 1$ CPU-hour) is negligible compared to the cost of a $N$-body simulation (e.g. millions of CPU-hours), The required computing time is cut a factor of 8.

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