Machine learning for understanding pulsating stars I: the non-linear phenomenon in δ Scuti stars
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| Format: | Preprint |
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2026
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| author | Rodon, J. R. Pascual-Granado, J. Lares-Martiz, M. Sánchez, M. Rodríguez Roche, C. |
| author_facet | Rodon, J. R. Pascual-Granado, J. Lares-Martiz, M. Sánchez, M. Rodríguez Roche, C. |
| contents | $δ$ 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_01344 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Machine learning for understanding pulsating stars I: the non-linear phenomenon in δ Scuti stars Rodon, J. R. Pascual-Granado, J. Lares-Martiz, M. Sánchez, M. Rodríguez Roche, C. Solar and Stellar Astrophysics $δ$ 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. |
| title | Machine learning for understanding pulsating stars I: the non-linear phenomenon in δ Scuti stars |
| topic | Solar and Stellar Astrophysics |
| url | https://arxiv.org/abs/2602.01344 |