Mitigating the impact of fiber assignment on clustering measurements from deep galaxy redshift surveys

TL;DR

This paper investigates how fiber assignment in deep galaxy redshift surveys affects clustering measurements and proposes weighting methods to correct these biases, achieving high accuracy in recovered correlation functions.

Contribution

It introduces new effects specific to deep surveys and develops weighting techniques to mitigate fiber assignment biases in clustering analysis.

Findings

Long-wavelength fluctuations can be recovered with galaxy weighting.

Under-dense region bias can be corrected using inverse probability weighting.

Correlation functions are recovered with better than 1% accuracy on large scales.

Abstract

We examine the impact of fiber assignment on clustering measurements from fiber-fed spectroscopic galaxy surveys. We identify new effects which were absent in previous, relatively shallow galaxy surveys such as Baryon Oscillation Spectroscopic Survey . Specifically, we consider deep surveys covering a wide redshift range from z=0.6 to z=2.4, as in the Subaru Prime Focus Spectrograph survey. Such surveys will have more target galaxies than we can place fibers on. This leads to two effects. First, it eliminates fluctuations with wavelengths longer than the size of the field of view, as the number of observed galaxies per field is nearly fixed to the number of available fibers. We find that we can recover the long-wavelength fluctuation by weighting galaxies in each field by the number of target galaxies. Second, it makes the preferential selection of galaxies in under-dense regions. We mitigate this effect by weighting galaxies using the so-called individual inverse probability. Correcting these two effects, we recover the underlying correlation function at better than 1 percent accuracy on scales greater than 10 Mpc/h.