论文标题
通过深度学习，近红外搜索基本模式RR Lyrae星空朝向内部凸起
Near-Infrared Search for Fundamental-mode RR Lyrae Stars Toward the Inner Bulge by Deep Learning
论文作者
论文摘要
为了将RR Lyrae恒星的人口普查扩展到中央银河系的高度红纬度区域，我们使用VíaLáctea（VVV）调查中的Vista变量的数据进行了深入的IR可变性搜索，分析了超过一亿点源的光度测定时间序列。为了将基本模式RR Lyre（RRAB）星星与其他定期可变来源分开，我们使用VVV调查数据和通过光学调查发现和分类的RRAB星星的数据和目录进行了深层双向长期记忆复发器（RNN）分类器。我们的分类器获得了约99％的精度和回忆的光曲线，信噪比高于60，并且与经过准确的光学数据训练的表现最好的分类器相当。使用我们的RNN分类器，我们确定了超过4300个迄今未知的善意rrab恒星，朝着内部的凸起。我们提供了它们的光度目录和VVV J，H，KS光度序列。
Aiming to extend the census of RR Lyrae stars to highly reddened low-latitude regions of the central Milky Way, we performed a deep near-IR variability search using data from the VISTA Variables in the Vía Láctea (VVV) survey of the bulge, analyzing the photometric time series of over a hundred million point sources. In order to separate fundamental-mode RR Lyrae (RRab) stars from other periodically variable sources, we trained a deep bidirectional long short-term memory recurrent neural network (RNN) classifier using VVV survey data and catalogs of RRab stars discovered and classified by optical surveys. Our classifier attained a ~99% precision and recall for light curves with signal-to-noise ratio above 60, and is comparable to the best-performing classifiers trained on accurate optical data. Using our RNN classifier, we identified over 4300 hitherto unknown bona fide RRab stars toward the inner bulge. We provide their photometric catalog and VVV J,H,Ks photometric time-series.
