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arXiv 2409.02540astro-ph.SR
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基于可扩展先进机器学习的恒星耀斑识别方法:应用于TESS短曝光数据及新耀斑星表分析

Scalable, Advanced Machine Learning-based Approaches for Stellar Flare Identification: Application to TESS short-cadence Data and Analysis of a New Flare Catalogue

Chia-Lung Lin, Daniel Apai, Mark S. Giampapa, Wing-Huen Ip

AI总结:

本文利用深度神经网络、随机森林和XGBoost三种机器学习模型处理TESS 130万条光变曲线,构建了包含近18,000颗耀斑星和250,000个耀斑的新星表,并揭示了耀斑能量与自转周期、X射线辐射之间的关系。

摘要:

我们将多算法机器学习模型应用于TESS第1至72扇区的2分钟巡天数据,以识别恒星耀斑。分别采用深度神经网络、随机森林和XGBoost算法训练的模型,利用四个耀斑光变曲线特征作为输入特征。模型性能通过准确率、精确率、召回率和F1分数指标进行评估,所有指标均超过94%。与先前报道的TESS M矮星耀斑识别结果进行验证对比表明,我们的模型成功恢复了超过92%的耀斑,同时额外检测到约2,000个更小的事件,从而扩展了先前工作的探测灵敏度。在处理了130万条光变曲线后,我们的模型识别出近18,000颗耀斑星和250,000个耀斑。我们提供了一个详尽的星表,记录了耀斑和恒星的性质。我们发现总耀斑能量和耀斑振幅与颜色之间存在强相关性,这与先前的研究一致。我们分析了耀斑频率分布,由于低振幅事件的探测不完备性,对耀斑行为的幂律斜率及其频率不确定性进行了修正。我们确定了约120,000颗恒星的自转周期,从而得到了自转周期与耀斑活动之间的关系。我们发现,耀斑能量中饱和与非饱和状态之间的自转周期转变点,与日冕X射线辐射中区分饱和与非饱和水平的自转周期转变点一致。我们发现,在早型星和非饱和恒星中,X射线辐射随耀斑光度的增加更为迅速,表明这些天体中日冕加热效率更高。此外,我们在白矮星和热亚矮星中探测到耀斑,这些耀斑很可能源自未分辨的低质量伴星。

英文摘要:

We apply multi-algorithm machine learning models to TESS 2-minute survey data from Sectors 1-72 to identify stellar flares. Models trained with Deep Neural Network, Random Forest, and XGBoost algorithms, respectively, utilized four flare light curve characteristics as input features. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics, all exceeding 94%. Validation against previously reported TESS M dwarf flare identifications showed that our models successfully recovered over 92% of the flares while detecting $\sim2,000$ more small events, thus extending the detection sensitivity of previous work. After processing 1.3 million light curves, our models identified nearly 18,000 flare stars and 250,000 flares. We present an extensive catalog documenting both flare and stellar properties. We find strong correlations in total flare energy and flare amplitude with color, in agreement with previous studies. Flare frequency distributions are analyzed, refining power-law slopes for flare behavior with the frequency uncertainties due to the detection incompleteness of low-amplitude events. We determine rotation periods for $\sim120,000$ stars thus yielding the relationship between rotation period and flare activity. We find that the transition in rotation period between the saturated and unsaturated regimes in flare energy coincides with the same transition in rotation period separating the saturated and unsaturated levels in coronal X-ray emission. We find that X-ray emission increases more rapidly with flare luminosity in earlier-type and unsaturated stars, indicating more efficient coronal heating in these objects. Additionally, we detect flares in white dwarfs and hot subdwarfs that are likely arising from unresolved low-mass companions.

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