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arXiv 2602.01344astro-ph.SR
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利用机器学习理解脉动星I：δ Scuti星的非线性现象

Machine learning for understanding pulsating stars I: the non-linear phenomenon in δ Scuti stars

Instituto de Astrofísica de Andalucía (CSIC)（安达卢西亚天体物理研究所（西班牙科学委员会））；Embry-Riddle Aeronautical University (ERAU)（埃姆布里-里德尔航空大学）；Departament d’Astronomia i Astrofísica, Universitat de Valéncia（瓦伦西亚大学天文与天体物理系）；The Exploration Company（探索公司）

J. R. Rodon, J. Pascual-Granado, M. Lares-Martiz, M. Rodríguez Sánchez, C. Roche

备注：
8 pages, 6 figures
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AI总结：

本研究利用机器学习聚类技术，通过分析频域特征和非线性机制，揭示δ Scuti星的内在子群，挑战基于幅度的传统分类方法。

摘要：

δ Scuti星是表现出径向和非径向脉动的变星，是理解恒星演化和内部结构的关键对象。目前，根据光变曲线的峰值到峰值幅度（>0.3视星等）将δ Scuti星分为高幅δ Scuti（HADS）和低幅δ Scuti（LADS）星。然而，这种分类可能无法完全捕捉其脉动机制和非线性效应的复杂性，导致可能的误分类。本研究旨在挑战基于幅度的现有分类，通过探索频域特征和非线性机制，以识别内在子群。目标是更深入地理解δ Scuti星的性质。我们使用机器学习聚类技术，特别是基于Ward链接的层次聚类（HC），分析由CoRoT、Kepler和TESS等空间望远镜观测的142颗δ Scuti星样本。我们关注频域特征，包括基频和高阶模态，以及非线性特征如谐波、和频和差频，以揭示δ Scuti星中的内在子群。聚类过程的结果表明，现有的基于幅度的分类（HADS/LADS）在某些方面与通过频域特征识别的聚类相一致。然而，研究发现了额外的子群，表明存在未被幅度单独捕捉的更多非线性效应。它突显了非线性特征的重要性，如减法组合的数量，可能指示共振效应或其他内部物理机制。

英文摘要：

$δ$ Scuti stars are pulsating variable stars that exhibit both radial and non-radial pulsations, making them key objects for understanding stellar evolution and internal structures. The current classification of $δ$ Scuti stars into High-Amplitude $δ$ Scuti (HADS) and Low-Amplitude $δ$ Scuti (LADS) stars is based on the peak-to-peak amplitude of their light curves (>0.3 mag). Nevertheless, this classification may not fully capture the complexity of their pulsation mechanisms and non-linear effects, leading to possible misclassifications. This investigation aims to challenge the existing classification of $δ$ Scuti stars according to amplitude, employing the exploration of frequency domain features and non-linear mechanisms in order to identify intrinsic subgroups. The objective is to get a deeper understanding of the properties of $δ$ Scuti stars. We use machine learning clustering techniques, specifically hierarchical clustering (HC) with Ward's linkage, to analyze a sample of 142 $δ$ Scuti stars observed by space telescopes such as CoRoT, Kepler, and TESS. We focus on frequency-domain features, including fundamental and overtone modes, as well as non-linear features such as harmonic, sums, and subtraction frequencies, to uncover intrinsic subgroups within $δ$ Scuti stars. The results of the clustering process indicate that the present amplitude-based classification (HADS/LADS) exhibits partial alignment with the clusters identified by using features from the frequency-domain. However, the study identified additional sub-groups, suggesting a greater variety of nonlinear effects that are not captured by the amplitude alone. It highlights the importance of non-linear features, such as the number of subtraction combinations, which may be indicative of resonance effects or other internal physical mechanisms.

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