Near-Infrared Search for Fundamental-mode RR Lyrae Stars Toward the
  Inner Bulge by Deep Learning
  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 V\'ia L\'actea (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.
