Probing the sparse tails of redshift distributions with Voronoi tessellations

TL;DR

This paper presents a non-parametric Voronoi tessellation-based algorithm for estimating galaxy redshift distributions from photometric data, effectively capturing distribution tails and providing unbiased moment estimates.

Contribution

Introduces a novel Voronoi tessellation method for non-parametric redshift distribution estimation using photometric and spectroscopic galaxy samples.

Findings

Performs well at reconstructing the tails of redshift distributions.

Provides unbiased estimates of the first and second moments.

Validated on a mock dataset with known distribution.

Abstract

We introduce an algorithm to estimate the redshift distribution of a sample of galaxies selected photometrically given a subsample with measured spectroscopic redshifts. The approach uses a non-parametric Voronoi tessellation density estimator to interpolate the galaxy distribution in the redshift and photometric color space. We test the method on a mock dataset with a known color-redshift distribution. We find that the Voronoi tessellation estimator performs well at reconstructing the tails of the redshift distribution of individual galaxies and gives unbiased estimates of the first and second moments. The source code is publicly available at http://bitbucket.org/bengranett/tailz.