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arXiv 2603.03439astro-ph.GA
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追溯AGN-合并关系:来自宇宙学模拟和JWST模拟观测的见解

Tracing the AGN-Merger Connection: insights from cosmological simulations and JWST mock observations

University of Seoul(首尔大学);Flatiron Institute(平顿研究所);Colby College(科尔比学院);École Polytechnique Fédérale de Lausanne (EPFL)(洛桑联邦理工学院);University of Copenhagen(哥本哈根大学);Max-Planck-Institut für Astrophysik(马克斯·普朗克天体物理学研究所);University of Florida(佛罗里达大学);Space Telescope Science Institute(太空望远镜科学研究所);Korea Institute for Advanced Study (KIAS)(韩国高等科学研究院)

Hannah Jhee, Ena Choi, Rachel S. Somerville, Dale D. Kocevski, Michaela Hirschmann, Thorsten Naab, Desika Narayanan, Intae Jung, Juhan Kim

AI总结:

本研究通过宇宙学模拟和JWST模拟观测,揭示了在低气体储备环境中合并对AGN触发的显著影响,同时指出仅依赖形态学分类可能弱化AGN-合并关系。

摘要:

星系合并长期以来被认为是一种将气体引导到银河中心的机制,可能触发超大质量黑洞(SMBH)的吸积并点燃活跃星系核(AGN)。尽管模拟通常支持这一情景,但观测研究在AGN-合并关系方面产生了矛盾的结果。在本研究中,我们分析了跨越红移0.5 < z < 3的31个宇宙学放大模拟中的星系。我们利用六维暗物质粒子信息构建详细的合并树来识别合并,并通过SMBH吸积历史来识别AGN活动。为了弥合模拟与观测之间的差距,我们生成了类似JWST的模拟图像,并提取了非参数化的形态学参数。在五维空间(四个形态学参数和红移)中使用k-最近邻(KNN)分类器,我们识别了模拟观测数据集中的合并。我们的分析揭示了在合并系统中AGN活动的统计学显著增强,特别是在较低的红移(0.5 < z < 0.9)中,其中中心气体储备更为耗尽。这支持了在内部气体储备较低的环境中,合并对AGN触发的贡献更为显著,而在气体丰富的系统中其影响可能不那么明显。然而,当仅依赖于模拟观测的形态学分类时,观察到的AGN-合并关系会减弱,尤其是在更高的红移中。这突显了在观测上检测由合并引发的AGN活动的挑战,并强调了结合模拟与现实的模拟观测以充分理解AGN-合并关系的重要性。

英文摘要:

Galaxy mergers have long been proposed as a mechanism for funneling gas toward galactic centres, potentially triggering accretion onto supermassive black holes (SMBHs) and igniting active galactic nuclei (AGN). While simulations often support this scenario, observational studies have yielded conflicting results regarding the AGN-merger connection. In this study, we analyze 31 galaxies from cosmological zoom-in simulations spanning redshifts $0.5 < z < 3$. We identify mergers using detailed merger trees based on six-dimensional dark matter particle information and identify AGN activity through SMBH accretion histories. To bridge the gap between simulations and observations, we generate mock JWST-like images and extract non-parametric morphological parameters. Employing a $k$-nearest neighbours (KNN) classifier in a five-dimensional space (four morphological parameters and redshift), we identify mergers in the mock-observed dataset. Our analysis reveals a statistically significant enhancement of AGN activity in merging systems, particularly at lower redshifts ($0.5 < z < 0.9$), where central gas reservoirs are more depleted. This supports the view that mergers contribute more significantly to AGN triggering in environments with low internal gas reservoirs, while their impact may be less pronounced in gas-rich systems. However, when relying solely on morphological classifications from mock observations, the observed AGN-merger connection weakens, especially at higher redshifts. This underscores the challenges in detecting merger-induced AGN activity observationally and highlights the importance of combining simulations with realistic mock observations to fully understand the AGN-merger relationship.

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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