Estimating the Redshift Distribution of Faint Galaxy Samples

TL;DR

This paper introduces an empirical weighting method to accurately estimate the redshift distribution of faint galaxy samples without relying on individual photometric redshift estimates, improving over traditional photo-z techniques.

Contribution

The paper presents a novel weighting approach using nearest-neighbor techniques to derive galaxy redshift distributions directly from photometric observables, avoiding biases of photo-z estimates.

Findings

The weighting method accurately recovers the true redshift distribution.

It outperforms neural network photo-z estimates in accuracy.

Effective when the spectroscopic sample covers the photometric sample's observable range.

Abstract

We present an empirical method for estimating the underlying redshift distribution N(z) of galaxy photometric samples from photometric observables. The method does not rely on photometric redshift (photo-z) estimates for individual galaxies, which typically suffer from biases. Instead, it assigns weights to galaxies in a spectroscopic subsample such that the weighted distributions of photometric observables (e.g., multi-band magnitudes) match the corresponding distributions for the photometric sample. The weights are estimated using a nearest-neighbor technique that ensures stability in sparsely populated regions of color-magnitude space. The derived weights are then summed in redshift bins to create the redshift distribution. We apply this weighting technique to data from the Sloan Digital Sky Survey as well as to mock catalogs for the Dark Energy Survey, and compare the results to those from the estimation of photo-z's derived by a neural network algorithm. We find that the weighting method accurately recovers the underlying redshift distribution, typically better than the photo-z reconstruction, provided the spectroscopic subsample spans the range of photometric observables covered by the photometric sample.