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arXiv 2006.09883astro-ph.GAastro-ph.IM
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基于深度学习的基模RR天琴座恒星内凸近红外搜索

Near-Infrared Search for Fundamental-mode RR Lyrae Stars Toward the Inner Bulge by Deep Learning

István Dékány, Eva K. Grebel

备注：
Accepted for publication in The Astrophysical Journal
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摘要：

为了将天琴座RR型恒星的普查扩展到银河系中央的高度变红的低纬度地区，我们使用Vía Láctea（VVV）调查中的VISTA变星数据进行了深度近红外变率搜索，分析了超过一亿个点源的光度时间序列。为了将基模天琴座RR星（RRab）与其他周期性变量源分离，利用VVV巡天数据和光学巡天发现并分类的RRab星表，训练了一个深度双向长短期记忆递归神经网络（RNN）分类器。对于信噪比超过60的光变曲线，我们的分类器达到了~99%的精确度和召回率，并且与在准确的光学数据上训练的性能最好的分类器相当。使用我们的RNN分类器，我们确定了超过4300颗迄今为止未知的真正的RRab星朝向内部凸起。我们提供他们的测光表和VVV J，H，Ks测光时间序列。

英文摘要：

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.

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