arXivDaily arXiv每日学术速递 周一至周五更新
北美退伍军人节(Veterans Day),12 日无更新
arXiv 2408.05038astro-ph.SRastro-ph.IM
收藏

利用神经网络检测太阳和恒星耀斑中的准周期脉动

Detecting quasi-periodic pulsations in solar and stellar flares with a neural network

Sergey A. Belov, Dmitrii Y. Kolotkov, Valery M. Nakariakov, Anne-Marie Broomhal

AI总结:

本文提出基于FCN的自动化方法,利用9万条合成光变曲线训练,以检测太阳和恒星耀斑中的指数衰减谐波QPP,并在Kepler真实数据中验证有效。

摘要:

准周期脉动(QPP)常在太阳和恒星耀斑光变曲线中被检测到。这些事件可能包含有关潜在基础等离子体动力学的宝贵信息,因为标准耀斑模型无法描述它们。在耀斑光变曲线中检测QPP信号受到其固有的非平稳特性、噪声污染以及耀斑观测数量持续增长的阻碍。因此,建立用于QPP检测的自动化技术势在必行。我们采用全卷积网络(FCN)架构,对耀斑光变曲线是否具有指数衰减谐波QPP进行分类。为训练FCN,生成了90,000条包含和不包含QPP的合成耀斑光变曲线。训练后,该网络在合成测试数据上的准确率达到87.2%,且未出现过拟合。为检验FCN在真实数据上的性能,我们使用了Kepler观测到的恒星光变曲线子集,这些样本迄今已通过其他方法识别出强有力的衰减QPP证据。随后,将FCN应用于由2274个事件组成的更大规模Kepler耀斑星表以寻找QPP,在概率高于95%的条件下获得了7%的QPP检测率。该FCN以Python实现,可通过具有友好图形界面的浏览器应用程序访问,并附有详细的安装和使用指南。所得结果表明,所开发的FCN性能良好,能够成功检测真实耀斑数据中的指数衰减谐波QPP,并可作为未来大规模观测巡天中初步筛选此类QPP事件的工具。

英文摘要:

Quasi-periodic pulsations (QPP) are often detected in solar and stellar flare lightcurves. These events may contain valuable information about the underlying fundamental plasma dynamics as they are not described by the standard flare model. The detection of QPP signals in flare lightcurves is hindered by their intrinsically non-stationary nature, contamination by noise, and the continuously increasing amount of flare observations. Hence, the creation of automated techniques for QPP detection is imperative. We implemented the Fully Convolution Network (FCN) architecture to classify the flare lightcurves whether they have exponentially decaying harmonic QPP or not. To train the FCN, 90,000 synthetic flare lightcurves with and without QPP were generated. After training, it showed an accuracy of 87.2% on the synthetic test data and did not experience overfitting. To test the FCN performance on real data, we used the subset of stellar flare lightcurves observed by Kepler, with strong evidence of decaying QPP identified hitherto with other methods. Then, the FCN was applied to find QPPs in a larger-scale Kepler flare catalogue comprised of 2274 events, resulting in a 7% QPP detection rate with a probability above 95%. The FCN, implemented in Python, is accessible through a browser application with a user-friendly graphical interface and detailed installation and usage guide. The obtained results demonstrate that the developed FCN performs well and successfully detects exponentially decaying harmonic QPP in real flare data, and can be used as a tool for preliminary sifting of the QPP events of this type in future large-scale observational surveys.

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。

↑