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arXiv 2202.14006astro-ph.GAastro-ph.CO
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星系晕连接中的散射:机器学习分析

The scatter in the galaxy-halo connection: a machine learning analysis

Richard Stiskalek, Deaglan J. Bartlett, Harry Desmond, Dhayaa Anbajagane

摘要:

我们应用机器学习,一个强大的方法来揭示复杂的相关性,在高维数据,宇宙学流体动力学模拟的星系晕连接。在没有完美信息的情况下,星系和光环变量之间的映射是随机的,但传统的机器学习模型是确定性的,因此无法捕捉其内在的散射。为了克服这一限制,我们设计了一个具有高斯损失函数的神经网络集成,该函数预测概率分布,使我们能够对星系-晕连接中的统计不确定性及其最佳拟合趋势进行建模。我们从地平线AGN和IllustrisTNG 100 -1模拟中提取了一些星系和晕变量,并量化了一个子集的知识在多大程度上能够预测另一个。这使我们能够识别星系晕连接的关键特征,并调查其在各种投影中的散射起源。我们发现,虽然晕质量以外的属性占50%的散射晕恒星质量的关系,恒星的半质量半径或总气体质量的预测是没有实质性的改善,通过添加进一步的晕属性。我们还使用这些结果调查半解析模型的星系大小在两个模拟,发现假设星系大小晕大小或自旋是不成功的。

英文摘要:

We apply machine learning, a powerful method for uncovering complex correlations in high-dimensional data, to the galaxy-halo connection of cosmological hydrodynamical simulations. The mapping between galaxy and halo variables is stochastic in the absence of perfect information, but conventional machine learning models are deterministic and hence cannot capture its intrinsic scatter. To overcome this limitation, we design an ensemble of neural networks with a Gaussian loss function that predict probability distributions, allowing us to model statistical uncertainties in the galaxy-halo connection as well as its best-fit trends. We extract a number of galaxy and halo variables from the Horizon-AGN and IllustrisTNG100-1 simulations and quantify the extent to which knowledge of some subset of one enables prediction of the other. This allows us to identify the key features of the galaxy-halo connection and investigate the origin of its scatter in various projections. We find that while halo properties beyond mass account for up to 50 per cent of the scatter in the halo-to-stellar mass relation, the prediction of stellar half-mass radius or total gas mass is not substantially improved by adding further halo properties. We also use these results to investigate semi-analytic models for galaxy size in the two simulations, finding that assumptions relating galaxy size to halo size or spin are not successful.

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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