All-purpose, all-sky photometric redshifts for the Legacy Imaging Surveys Data Release 8
Kenneth J. Duncan

TL;DR
This paper introduces a machine-learning based method for estimating photometric redshifts across the entire Legacy Imaging Surveys Data Release 8, achieving high accuracy and bias reduction for over 900 million galaxies up to redshift 7.
Contribution
The paper presents a novel, data-driven machine learning approach using sparse Gaussian processes and GMMs for unbiased, accurate photometric redshift estimation across a wide parameter space.
Findings
Significantly less biased and more accurate redshifts at z > 1.
Reliable predictions for rare high-redshift objects like quasars at z > 6.
Provides high-quality photo-z catalog for over 900 million galaxies.
Abstract
In this paper we present photometric redshift (photo-) estimates for the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys, currently the most sensitive optical survey covering the majority of the extra-galactic sky. Our photo- methodology is based on a machine-learning approach, using sparse Gaussian processes augmented with Gaussian mixture models (GMMs) that allow regions of parameter space to be identified and trained separately in a purely data-driven way. The same GMMs are also used to calculate cost-sensitive learning weights that mitigate biases in the spectroscopic training sample. By design, this approach aims to produce reliable and unbiased predictions for all parts of the parameter space present in wide area surveys. Compared to previous literature estimates using the same underlying photometry, our photo-s are significantly less biased and more…
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