# Identification of RR Lyrae stars in multiband, sparsely-sampled data from the Dark Energy Survey using template fitting and Random Forest classification

## TL;DR

This paper introduces a new template fitting and Random Forest classification method to identify RR Lyrae stars in the sparse, multiband data from the Dark Energy Survey, enabling the discovery of many new stellar tracers in the Galactic halo.

## Contribution

The paper presents a novel, efficient approach combining template fitting and machine learning to detect RR Lyrae stars in sparsely sampled multiband survey data, outperforming traditional methods.

## Key findings

- Recovered 89% of RR Lyrae periods within 1% accuracy
- Achieved 85% purity and 76% completeness in classification
- Identified 5783 RR Lyrae candidates, including 31% new discoveries

## Abstract

Many studies have shown that RR Lyrae variable stars (RRL) are powerful stellar tracers of Galactic halo structure and satellite galaxies. The Dark Energy Survey (DES), with its deep and wide coverage (g ~ 23.5 mag) in a single exposure; over 5000 deg$^{2}$) provides a rich opportunity to search for substructures out to the edge of the Milky Way halo. However, the sparse and unevenly sampled multiband light curves from the DES wide-field survey (median 4 observations in each of grizY over the first three years) pose a challenge for traditional techniques used to detect RRL. We present an empirically motivated and computationally efficient template fitting method to identify these variable stars using three years of DES data. When tested on DES light curves of previously classified objects in SDSS stripe 82, our algorithm recovers 89% of RRL periods to within 1% of their true value with 85% purity and 76% completeness. Using this method, we identify 5783 RRL candidates, ~31% of which are previously undiscovered. This method will be useful for identifying RRL in other sparse multiband data sets.