Z-Sequence: photometric redshift predictions for galaxy clusters with sequential random k-nearest neighbours : Estimating Photometric Redshifts Of Clusters

Chan, Matthew and Stott, John (2021) Z-Sequence: photometric redshift predictions for galaxy clusters with sequential random k-nearest neighbours : Estimating Photometric Redshifts Of Clusters. Monthly Notices of the Royal Astronomical Society, 503 (4). pp. 6078-6097. ISSN 0035-8711

Text (2104.11335)
2104.11335.pdf - Accepted Version
Available under License Creative Commons Attribution-NonCommercial.
Download (17MB)

Abstract

We introduce Z-Sequence, a novel empirical model that utilizes photometric measurements of observed galaxies within a specified search radius to estimate the photometric redshift of galaxy clusters. Z-Sequence itself is composed of a machine learning ensemble based on the k-nearest neighbours algorithm. We implement an automated feature selection strategy that iteratively determines appropriate combinations of filters and colours to minimize photometric redshift prediction error. We intend for Z-Sequence to be a standalone technique but it can be combined with cluster finders that do not intrinsically predict redshift, such as our own DEEP-CEE. In this proof-of-concept study, we train, fine-tune, and test Z-Sequence on publicly available cluster catalogues derived from the Sloan Digital Sky Survey. We determine the photometric redshift prediction error of Z-Sequence via the median value of |Δz|/(1 + z⁠) (across a photometric redshift range of 0.05 ≤ z ≤ 0.6) to be ∼0.01 when applying a small search radius. The photometric redshift prediction error for test samples increases by 30–50 per cent when the search radius is enlarged, likely due to line-of-sight interloping galaxies. Eventually, we aim to apply Z-Sequence to upcoming imaging surveys such as the Legacy Survey of Space and Time to provide photometric redshift estimates for large samples of as yet undiscovered and distant clusters.