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

TL;DR

This study employs deep learning to identify fundamental-mode RR Lyrae stars in the near-infrared, significantly expanding the census of these stars in the highly obscured inner Milky Way bulge.

Contribution

It introduces a deep RNN classifier trained on optical data to accurately detect RRab stars in near-IR VVV survey data, discovering over 4300 new RRab stars.

Findings

Achieved ~99% precision and recall in classifying RRab stars.

Identified over 4300 new RRab stars in the inner bulge.

Provided a comprehensive catalog and light curves for the new RRab stars.

Abstract

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.