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arXiv 2602.22375astro-ph.GA
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通过无监督和监督机器学习识别分子团的演化阶段

Identifying Evolutionary Stages of Molecular Clumps through Unsupervised and Supervised Machine Learning

Institute of Astronomy of the Russian Academy of Sciences(俄罗斯科学院天文研究所);Ural Federal University named after the first President of Russia B.N.Yeltsin(叶利钦总统乌拉尔联邦大学);Space Research Institute of the Russian Academy of Sciences(俄罗斯科学院太空研究所)

K. V. Plakitina, M. S. Kirsanova, A. B. Ostrovskii, A. D. Gimalieva, S. V. Salii, A. V. Meshcheryakov

AI总结:

本研究利用机器学习方法对分子团的演化阶段进行分类,通过无监督和监督学习技术识别出不同演化阶段的团块,并为522个对象提供了分类结果。

摘要:

分子团的演化分类对于理解恒星形成至关重要,通常基于人类分配的类别,来源于红外(IR)发射和已确立的形态学标准。然而,由于模糊的特征、距离不确定性或严重的红外发射遮蔽,大量源往往无法分类。本研究展示了机器学习(ML)作为一种互补的数据驱动方法的能力,利用MALT90调查的数据,结合斯皮策红外光度计数据,自动识别和分类这些团块。我们应用了HDBSCAN的无监督聚类,基于分子线强度,揭示了不同的分组,对应于演化阶段。仅使用五种分子线(HCO+,HNC,N2H+,HCN,C2H),我们识别出稳定的原恒星团和无活跃恒星形成的区域,主要由C2H和N2H+发射驱动。加入H13CO+产生了一个独特的紫外主导团块,追踪了更成熟的区域。红外特性似乎是非显著特征,暗示不同质量团块的包层在全局红外特性上相似。我们随后利用监督学习对之前不确定的类别进行分类,并为522个对象提供了分类,主要是无活跃恒星形成的区域。我们的结果表明,ML技术可以有效地在复杂的天体化学数据中揭示内在的演化结构,并为不确定的源分配类别,为传统方法提供了强大的数据驱动补充。

英文摘要:

The evolutionary classification of molecular clumps, crucial for understanding star formation, is commonly based on human-assigned categories derived from infrared (IR) emission and well-established morphological criteria. However, due to ambiguous signatures, distance uncertainties or heavily obscured IR emission, a significant fraction of sources often remains unclassified. This work demonstrates the capability of machine learning (ML) as a complementary, data-driven approach to automate the identification and classification of these clumps using data from the MALT90 survey, complemented by Spitzer IR photometry. We applied unsupervised clustering with HDBSCAN on molecular line intensities, revealing distinct groupings that correspond to evolutionary stages. Using only five molecular lines (HCO$^+$, HNC, N$_2$H$^+$, HCN, C$_2$H), we identified stable clusters of protostars and regions without active star formation, driven primarily by C$_2$H and N$_2$H$^+$ emission. Incorporating H$^{13}$CO$^+$ gave rise to a distinct UV-dominant cluster, tracing more evolved regions. Infrared properties appeared as non-significant features implying that envelopes of clumps with different masses are similar in their global infrared characteristics. We then employed supervised learning to classify clumps with previously uncertain categories and provided classifications for 522 objects, predominantly as regions without active star formation. Our results show that ML techniques can effectively uncover intrinsic evolutionary structures in complex astrochemical data and assign categories to uncertain sources, providing a powerful, data-driven complement to traditional methods.

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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