Galaxy clustering with photometric surveys using PDF redshift information

TL;DR

This paper investigates how using full photometric redshift probability density functions (PDFs) improves galaxy clustering measurements in photometric surveys, reducing bias compared to single-point estimates.

Contribution

It demonstrates that employing the entire photo-z PDF, instead of a single estimate, decreases measurement bias in galaxy clustering analyses.

Findings

Using full PDFs reduces bias from 5% to 3% in narrow redshift bins.

Employing PDFs leads to more accurate galaxy bias measurements.

The approach improves clustering analysis accuracy in simulated data.

Abstract

Photometric surveys produce large-area maps of the galaxy distribution, but with less accurate redshift information than is obtained from spectroscopic methods. Modern photometric redshift (photo-z) algorithms use galaxy magnitudes, or colors, that are obtained through multi-band imaging to produce a probability density function (PDF) for each galaxy in the map. We used simulated data to study the effect of using different photo-z estimators to assign galaxies to redshift bins in order to compare their effects on angular clustering and galaxy bias measurements. We found that if we use the entire PDF, rather than a single-point (mean or mode) estimate, the deviations are less biased, especially when using narrow redshift bins. When the redshift bin widths are $\Delta z=0.1$, the use of the entire PDF reduces the typical measurement bias from 5%, when using single point estimates, to 3%.