# Unbiased clustering estimation in the presence of missing observations

## TL;DR

This paper introduces a novel pair-upweighting scheme to correct for biases caused by missing clustered observations in spectroscopic galaxy surveys, improving the accuracy of clustering estimates.

## Contribution

The paper proposes a rerun-based targeting algorithm combined with a pair-upweighting scheme to unbiasedly estimate galaxy clustering despite missing observations.

## Key findings

- The scheme corrects for clustering biases in simulated surveys.
- It effectively recovers true clustering statistics.
- Applicable to future large-scale galaxy surveys.

## Abstract

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.