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arXiv 2507.08074astro-ph.GA
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探索银河系晕中的子结构:应用于Gaia和APOGEE DR 17的神经网络

Exploring substructures in the Milky Way halo Neural networks applied to Gaia and APOGEE DR 17

L. Berni, L. Spina, L. Magrini, D. Massari, J. Schiapppacasse-Ulloa, R. E. Giribaldi

AI总结:

本研究提出一种结合化学与动力学的神经网络聚类方法CREEK,用于识别银河系晕中的吸积结构,成功恢复了APOGEE数据集80%的球状星团并发现潜在新恒星流。

摘要:

识别银河系晕中的恒星结构(包括恒星流和并合遗迹)通常依赖于其组成恒星的动力学。然而,由于这些结构与其环境之间复杂的动力学相互作用,这种方法存在局限性。诸如银河系施加的潮汐力、恒星可能的逃逸,以及穿越银道面等扰动,可能导致这些结构中恒星的动力学相干性丧失。因此,仅依赖动力学可能不足以探测此类被瓦解或分散的遗迹。我们将化学与动力学相结合,通过一个神经网络系统进行整合,开发了一种用于识别银河系晕中吸积结构的聚类方法。我们开发了一种整合了连体神经网络(SNNs)、图神经网络(GNNs)、自编码器和OPTICS算法的综合方法,命名为CREEK。该方法旨在揭示银河系晕中的恒星结构。最初,CREEK在已知的球状星团(GCs)上进行训练,然后应用于数据集以识别恒星流。CREEK成功恢复了APOGEE数据集中80%的球状星团,重新识别了几个已知的恒星流,并识别出了一个潜在的新恒星流。此外,在高度密集的恒星结构中,CREEK能够识别出具有独特化学成分和轨道能量的子结构。这种方法提供了一种客观的数据驱动方法,用于选择与恒星流及一般恒星结构相关的恒星。

英文摘要:

The identification of stellar structures in the Galactic halo, including stellar streams and merger remnants, often relies on the dynamics of their constituent stars. However, this approach has limitations due to the complex dynamical interactions between these structures and their environment. Perturbations such as tidal forces exerted by the Milky Way, the potential escape of stars, and passages through the Galactic plane can result in the loss of dynamical coherence of stars in these structures. Consequently, relying solely on dynamics may be insufficient for detecting such disrupted or dispersed remnants. We combine chemistry and dynamics, integrated through a system of neural networks, to develop a clustering method for identifying accreted structures in the Galactic halo. We developed an integrated approach combining Siamese neural networks (SNNs), graph neural networks (GNNs), autoencoders, and the OPTICS algorithm to create a comprehensive procedure named CREEK. This method is designed to uncover stellar structures in the Galactic halo. Initially, CREEK was trained on known globular clusters (GCs) and then applied to the dataset to identify stellar streams. CREEK successfully recovered 80% of the GCs present in the APOGEE dataset, re-identified several known stellar streams, and identified a potential new stream. Additionally, within highly populated stellar structures, CREEK can identify substructures that exhibit distinct chemical compositions and orbital energies. This approach provides an objective data-driven method for selecting stars associated with streams and stellar structures in general.

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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