The sensitivity of GPz estimates of photo-z posterior PDFs to realistically complex training set imperfections
Natalia Stylianou, Alex I. Malz, Peter Hatfield, John Franklin, Crenshaw, Julia Gschwend

TL;DR
This study assesses how realistic training set imperfections impact the accuracy of GPz photometric redshift estimates, revealing significant sensitivity to emission line confusion and sample incompleteness.
Contribution
It provides a detailed analysis of GPz's robustness to complex training set imperfections in photometric redshift estimation.
Findings
Photo-z quality drops significantly with >1% line confusion.
Sample incompleteness below redshift 1.5 degrades predictions.
GPz's sensitivity varies with different types of training set imperfections.
Abstract
The accurate estimation of photometric redshifts is crucial to many upcoming galaxy surveys, for example the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). Almost all Rubin extragalactic and cosmological science requires accurate and precise calculation of photometric redshifts; many diverse approaches to this problem are currently in the process of being developed, validated, and tested. In this work, we use the photometric redshift code GPz to examine two realistically complex training set imperfections scenarios for machine learning based photometric redshift calculation: i) where the spectroscopic training set has a very different distribution in colour-magnitude space to the test set, and ii) where the effect of emission line confusion causes a fraction of the training spectroscopic sample to not have the true redshift. By evaluating the sensitivity of GPz to a…
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Taxonomy
TopicsGalaxies: Formation, Evolution, Phenomena · Astronomy and Astrophysical Research · Gamma-ray bursts and supernovae
