No galaxy left behind: accurate measurements with the faintest objects
  in the Dark Energy Survey
  Accurate statistical measurement with large imaging surveys has traditionally
required throwing away a sizable fraction of the data. This is because most
measurements have have relied on selecting nearly complete samples, where
variations in the composition of the galaxy population with seeing, depth, or
other survey characteristics are small.
  We introduce a new measurement method that aims to minimize this wastage,
allowing precision measurement for any class of stars or galaxies detectable in
an imaging survey. We have implemented our proposal in Balrog, a software
package which embeds fake objects in real imaging in order to accurately
characterize measurement biases.
  We demonstrate this technique with an angular clustering measurement using
Dark Energy Survey (DES) data. We first show that recovery of our injected
galaxies depends on a wide variety of survey characteristics in the same way as
the real data. We then construct a flux-limited sample of the faintest galaxies
in DES, chosen specifically for their sensitivity to depth and seeing
variations. Using the synthetic galaxies as randoms in the standard
Landy-Szalay correlation function estimator suppresses the effects of variable
survey selection by at least two orders of magnitude. With this correction, our
measured angular clustering is found to be in excellent agreement with that of
a matched sample drawn from much deeper, higher-resolution space-based
Cosmological Evolution Survey (COSMOS) imaging; over angular scales of
$0.004^{\circ} < \theta < 0.2^{\circ}$, we find a best-fit scaling amplitude
between the DES and COSMOS measurements of $1.00 \pm 0.09$.
  We expect this methodology to be broadly useful for extending the statistical
reach of measurements in a wide variety of coming imaging surveys.
