Unbiased clustering estimation in the presence of missing observations
  In order to be efficient, spectroscopic galaxy redshift surveys do not obtain
redshifts for all galaxies in the population targeted. The missing galaxies are
often clustered, commonly leading to a lower proportion of successful
observations in dense regions. One example is the close-pair issue for SDSS
spectroscopic galaxy surveys, which have a deficit of pairs of observed
galaxies with angular separation closer than the hardware limit on placing
neighbouring fibers. Spatially clustered missing observations will exist in the
next generations of surveys. Various schemes have previously been suggested to
mitigate these effects, but none works for all situations. We argue that the
solution is to link the missing galaxies to those observed with statistically
equivalent clustering properties, and that the best way to do this is to rerun
the targeting algorithm, varying the angular position of the observations.
Provided that every pair has a non-zero probability of being observed in one
realisation of the algorithm, then a pair-upweighting scheme linking targets to
successful observations, can correct these issues. We present such a scheme,
and demonstrate its validity using realisations of an idealised simple survey
strategy.
