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