使用卷积神经网络从光度时间序列数据中导出恒星性质的数据驱动推导
Data-driven derivation of stellar properties from photometric time series data using convolutional neural networks
摘要：
恒星的变异性是由许多内部物理过程驱动的，这些物理过程取决于恒星的基本性质。这些性质是我们调和恒星观测与恒星物理的桥梁，也是理解星系形成背景下恒星星族分布的桥梁。许多正在进行和即将进行的任务正在绘制恒星随时间的亮度波动，这些信息编码了有关物理过程的信息，如旋转周期，演化状态（如有效温度和表面重力）和质量（通过星震参数）。在这里，我们探讨如何以及我们可以预测这些恒星的属性，在不同的演化状态，只使用光度时间序列数据。为此，我们实现了一个卷积神经网络，并通过数据驱动的建模，我们从各种基线和节奏的光变曲线预测恒星属性。基于一个季度的\textit{Kepler}数据，我们恢复恒星的性质，包括表面重力的红巨星（与$\lesssim$ 0.06 dex的不确定性），和旋转周期的主序星（与$\lesssim$ 5.2天的不确定性，从$\approx$ 5至40天无偏）。缩短\textit{Kepler}数据到27天的TESS样基线，我们恢复恒星的性能，精度略有下降，$\sim$ 0.07 dex的日志$g$和$\sim$ 5.5天的$P_{\rm rot}$，无偏从$\approx$ 5至35天。我们灵活的数据驱动方法利用了数据的全部信息内容，需要最少的特征工程，并且可以推广到其他调查和数据集。这有可能在当前和未来的调查中为数百万颗恒星提供恒星属性估计。
英文摘要：
Stellar variability is driven by a multitude of internal physical processes that depend on fundamental stellar properties. These properties are our bridge to reconciling stellar observations with stellar physics, and for understanding the distribution of stellar populations within the context of galaxy formation. Numerous ongoing and upcoming missions are charting brightness fluctuations of stars over time, which encode information about physical processes such as rotation period, evolutionary state (such as effective temperature and surface gravity), and mass (via asteroseismic parameters). Here, we explore how well we can predict these stellar properties, across different evolutionary states, using only photometric time series data. To do this, we implement a convolutional neural network, and with data-driven modeling we predict stellar properties from light curves of various baselines and cadences. Based on a single quarter of \textit{Kepler} data, we recover stellar properties, including surface gravity for red giant stars (with an uncertainty of $\lesssim$ 0.06 dex), and rotation period for main sequence stars (with an uncertainty of $\lesssim$ 5.2 days, and unbiased from $\approx$5 to 40 days). Shortening the \textit{Kepler} data to a 27-day TESS-like baseline, we recover stellar properties with a small decrease in precision, $\sim$0.07 dex for log $g$ and $\sim$5.5 days for $P_{\rm rot}$, unbiased from $\approx$5 to 35 days. Our flexible data-driven approach leverages the full information content of the data, requires minimal feature engineering, and can be generalized to other surveys and datasets. This has the potential to provide stellar property estimates for many millions of stars in current and future 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。
