Clustering-based redshift estimation: method and application to data

TL;DR

This paper introduces a clustering-based method for estimating the redshift distribution of astronomical datasets by leveraging spatial cross-correlations, improving accuracy through optimal sampling, and demonstrating its application across multiple surveys.

Contribution

The paper presents a novel clustering-based approach that utilizes all scales and optimal sampling to infer redshift distributions, advancing previous linear-scale methods.

Findings

Effective redshift distribution estimation for diverse datasets

Consistent results using different reference populations

Enhanced accuracy through optimized sampling strategies

Abstract

We present a data-driven method to infer the redshift distribution of an arbitrary dataset based on spatial cross-correlation with a reference population and we apply it to various datasets across the electromagnetic spectrum to show its potential and limitations. Our approach advocates the use of clustering measurements on all available scales, in contrast to previous works focusing only on linear scales. We also show how its accuracy can be enhanced by optimally sampling a dataset within its photometric space rather than applying the estimator globally. We show that the ultimate goal of this technique is to characterize the mapping between the space of photometric observables and redshift space as this characterization then allows us to infer the clustering-redshift p.d.f. of a single galaxy. We apply this technique to estimate the redshift distributions of luminous red galaxies and emission line galaxies from the SDSS, infrared sources from WISE and radio sources from FIRST. We show that consistent redshift distributions are found using both quasars and absorber systems as reference populations. This technique brings valuable information on the third dimension of astronomical datasets. It is widely applicable to a large range of extra-galactic surveys.