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Many studies have shown that RR Lyrae variable stars (RRL) are powerfulstellar tracers of Galactic halo structure and satellite galaxies. The DarkEnergy Survey (DES), with its deep and wide coverage (g ~ 23.5 mag) in a singleexposure; over 5000 deg$^{2}$) provides a rich opportunity to search forsubstructures out to the edge of the Milky Way halo. However, the sparse andunevenly sampled multiband light curves from the DES wide-field survey (median4 observations in each of grizY over the first three years) pose a challengefor traditional techniques used to detect RRL. We present an empiricallymotivated and computationally efficient template fitting method to identifythese variable stars using three years of DES data. When tested on DES lightcurves of previously classified objects in SDSS stripe 82, our algorithmrecovers 89% of RRL periods to within 1% of their true value with 85% purityand 76% completeness. Using this method, we identify 5783 RRL candidates, ~31%of which are previously undiscovered. This method will be useful foridentifying RRL in other sparse multiband data sets.

the astronomical journal

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