An automated method for finding the most distant quasars
File(s)
Published version
Author(s)
Lenz, Lena
Mortlock, Daniel
Leistedt, Boris
Barnett, Rhys
Hewett, Paul C
Type
Journal Article
Abstract
Upcoming surveys such as Euclid, the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) and the Nancy Grace Roman Telescope (Roman) will detect hundreds of high-redshift (z ≳ 7) quasars, but distinguishing them from the billions of other sources in these catalogues represents a significant data analysis challenge. We address this problem by extending existing selection methods by using both i) Bayesian model comparison on measured fluxes and ii) a likelihood-based goodness-of-fit test on images, which are then combined using the Fβ statistic (where β is a parameter which can be tuned to prioritise completeness). The result is an automated, reproduceable and objective high-redshift quasar selection pipeline. We test this on both simulations and real data from the cross-matched Sloan Digital Sky Survey (SDSS) and UKIRT Infrared Deep Sky Survey (UKIDSS) catalogues. On this cross-matched dataset we achieve an area under the curve (AUC) score of up to 0.81 and an F3 score of up to 0.79 ; or, if the completeness is fixed to be 0.9 then we can obtain an efficiency of 0.15. This is sufficient to be applied to the Euclid, LSST and Roman data when available.
Date Issued
2025-07-30
Date Acceptance
2025-07-01
Citation
The Open Journal of Astrophysics, 2025, 8
ISSN
2565-6120
Publisher
Maynooth Academic Publishing
Journal / Book Title
The Open Journal of Astrophysics
Volume
8
Copyright Statement
Copyright © 2025 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
License URL
Publication Status
Published
Date Publish Online
2025-07-30