Dark Energy Survey Year 3 Results: Galaxy Sample for BAO Measurement
  In this paper we present and validate the galaxy sample used for the analysis
of the Baryon Acoustic Oscillation signal (BAO) in the Dark Energy Survey (DES)
Y3 data. The definition is based on a colour and redshift-dependent magnitude
cut optimized to select galaxies at redshifts higher than 0.5, while ensuring a
high quality photometric redshift determination. The sample covers $\approx
4100$ square degrees to a depth of $i = 22.3 \ (AB)$ at $10\sigma$. It contains
7,031,993 galaxies in the redshift range from $z$= 0.6 to 1.1, with a mean
effective redshift of 0.835. Photometric redshifts are estimated with the
machine learning algorithm DNF, and are validated using the VIPERS PDR2 sample.
We find a mean redshift bias of $z_{\mathrm{bias}} \approx 0.01$ and a mean
uncertainty, in units of $1+z$, of $\sigma_{68} \approx 0.03$. We evaluate the
galaxy population of the sample, showing it is mostly built upon Elliptical to
Sbc types. Furthermore, we find a low level of stellar contamination of
$\lesssim 4\%$. We present the method used to mitigate the effect of spurious
clustering coming from observing conditions and other large-scale systematics.
We apply it to the DES Y3 BAO sample and calculate sample weights that are used
to get a robust estimate of the galaxy clustering signal. This paper is one of
a series dedicated to the analysis of the BAO signal in the DES Y3 data. In the
companion papers, Ferrero et al. (2021) and DES Collaboration (2021), we
present the galaxy mock catalogues used to calibrate the analysis and the
angular diameter distance constraints obtained through the fitting to the BAO
scale, respectively. The galaxy sample, masks and additional material will be
released in the public DES data repository upon acceptance.
