Incorporating Photometric Redshift Probability Density Information into Real-Space Clustering Measurements

TL;DR

This paper introduces a new estimator for the 2-point correlation function that leverages the full photometric redshift probability density functions, significantly improving clustering measurements in cosmology.

Contribution

We develop a novel real-space estimator that incorporates full photometric redshift PDFs into clustering measurements, enhancing signal detection without binning by peak redshift.

Findings

Improves clustering signal by a factor of 2-3 over traditional methods.

Pair-weighted estimator enhances clustering detection equivalent to a 4-5 times larger survey.

Applicable to various photometric populations beyond QSOs.

Abstract

The use of photometric redshifts in cosmology is increasing. Often, however these photo-zs are treated like spectroscopic observations, in that the peak of the photometric redshift, rather than the full probability density function (PDF), is used. This overlooks useful information inherent in the full PDF. We introduce a new real-space estimator for one of the most used cosmological statistics, the 2-point correlation function, that weights by the PDF of individual photometric objects in a manner that is optimal when Poisson statistics dominate. As our estimator does not bin based on the PDF peak it substantially enhances the clustering signal by usefully incorporating information from all photometric objects that overlap the redshift bin of interest. As a real-world application, we measure QSO clustering in the Sloan Digital Sky Survey (SDSS). We find that our simplest binned estimator improves the clustering signal by a factor equivalent to increasing the survey size by a factor of 2-3. We also introduce a new implementation that fully weights between pairs of objects in constructing the cross-correlation and find that this pair-weighted estimator improves clustering signal in a manner equivalent to increasing the survey size by a factor of 4-5. Our technique uses spectroscopic data to anchor the distance scale and it will be particularly useful where spectroscopic data (e.g, from BOSS) overlaps deeper photometry (e.g.,from Pan-STARRS, DES or the LSST). We additionally provide simple, informative expressions to determine when our estimator will be competitive with the autocorrelation of spectroscopic objects. Although we use QSOs as an example population, our estimator can and should be applied to any clustering estimate that uses photometric objects.