DNF - Galaxy photometric redshift by Directional Neighbourhood Fitting

TL;DR

This paper introduces DNF, a novel galaxy photometric redshift estimation algorithm that combines a new neighborhood metric, a fitting strategy, and a probability distribution method, demonstrating superior accuracy and error reliability on multiple datasets.

Contribution

The paper presents the Directional Neighbourhood Fitting (DNF) algorithm, integrating a new neighborhood metric, a fitting approach, and a probability distribution method for improved photometric redshift estimation.

Findings

DNF outperforms existing empirical photo-z tools in accuracy.

DNF provides reliable error estimates.

DNF achieves high-quality results across multiple datasets.

Abstract

Wide field images taken in several photometric bands allow simultaneous measurement of redshifts for thousands of galaxies. A variety of algorithms to make this measurement have appeared in the last few years, the majority of which can be classified as either template or training based methods. Among the latter, nearest neighbour estimatorsstand out as one of the most successful, in terms of both precision and the quality of error estimation. In this paper we describe the Directional Neighbourhood Fitting (DNF) algorithm based on the following: a new neighbourhood metric (Directional Neighbourhood), a photo-z estimation strategy (Neighbourhood Fitting) and a method for generating the photo-z probability distribution function. We compare DNF with other well-known empirical photometric redshift tools using different public datasets (Sloan Digital Sky Survey, VIMOS VLT Deep Survey and Photo-z Accuracy Testing). DNF achieves high-quality results with reliable error.