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arXiv 2510.20751astro-ph.GA
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黑洞-宿主星系关联的机器学习研究：Illustris、TNG 与 EAGLE 的比较分析

Black Hole-Host Galaxy Correlations with Machine Learning: A Comparative Study of Illustris, TNG, and EAGLE

Jacob Reinheimer, Yuan Li, Trung Ha, Melanie Habouzit, Brandon M. Matthews, George Blaney

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13 pages, 6 figures. Comments welcome
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AI总结：

本研究利用机器学习回归器比较 Illustris、TNG 和 EAGLE 模拟中的黑洞-宿主星系标度关系，发现 MLP 预测最优，且关联本质上是多维的。

摘要：

超大质量黑洞（SMBHs）已知与其宿主星系的许多性质相关，但我们尚未完全理解这些关联。这些关联的强度（紧密程度）也一直备受争议。在本工作中，我们探索了三个最先进的宇宙学模拟（Illustris、TNG 和 EAGLE）中的 SMBH-宿主关系。使用多种机器学习回归器，我们测量了黑洞质量（$M_{\rm BH}$）与星系性质（包括恒星速度弥散（$\sigma$）、恒星质量（$M_{\star}$）、暗物质晕质量（$M_{\rm Halo}$）和 Sersic 指数）之间的标度关系。我们发现，在模拟中的许多标度关系中，机器学习回归器提供的预测能力优于线性回归，其中多层感知机（MLP）回归器的性能最强。由于亚网格模型的不同，SMBH-宿主关系在不同模拟中具有不同的强度。与观测类似，$M_{\rm BH}$-$\sigma$ 关系在所有模拟中都是强关联，但在 TNG 中，$M_{\rm BH}$-$M_{\star}$ 关系甚至比 $M_{\rm BH}$-$\sigma$ 更紧密。EAGLE 在所有模拟中产生的 SMBH-宿主关联最弱。低质量 SMBH 往往与其宿主星系关联较差，但包含它们仍有助于机器更好地掌握 Illustris 和 TNG 中的关联。将与 $M_{\rm BH}$ 强相关但彼此相关性较差的星系性质结合起来，可以提高 MLP 的性能。当所有星系性质都包含在训练中时，$M_{\rm BH}$ 的预测最准确，这表明在这些模拟中，SMBH-宿主关联本质上是多维的。

英文摘要：

Supermassive black holes (SMBHs) are known to correlate with many properties of their host galaxies, but we do not fully understand these correlations. The strengths (tightness) of these correlations have also been widely debated. In this work, we explore SMBH-host relations in three state-of-the-art cosmological simulations: Illustris, TNG, and EAGLE. Using a variety of machine learning regressors, we measure the scaling relations between black hole mass ($M_{\rm BH}$) and galaxy properties including stellar velocity dispersion ($σ$), stellar mass ($M_{\star}$), dark matter halo mass ($M_{\rm Halo}$), and the Sersic index. We find that machine learning regressors provide predictive capabilities superior to linear regression in many scaling relations in simulations, and Multi-layer Perceptron (MLP) regressor has the strongest performance. SMBH-host relations have different strengths in different simulations as a result of their sub-grid models. Similar to the observations, the $M_{\rm BH} $-$σ$ relation is a strong correlation in all simulations, but in TNG, the $M_{\rm BH} $-$M_{\star}$ relation is even tighter than $M_{\rm BH} $-$σ$. EAGLE produces the weakest SMBH-host correlations among all simulations. Low mass SMBHs tend to be poorly correlated with their host galaxies, but including them can still help machines better grasp the correlations in Illustris and TNG. Combining galaxy properties that strongly correlate with $M_{\rm BH} $ but poorly correlate with each other can improve MLP's performance. $M_{\rm BH} $ is most accurately predicted when all galaxy properties are included in the training, suggesting that SMBH-host correlations are fundamentally multi-dimensional in these simulations.

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