Z-Sequence: Photometric redshift predictions for galaxy clusters with sequential random k-nearest neighbours

• Published in: arXiv.org
• Main Authors: Chan, Matthew C, Stott, John P
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 22.04.2021
• Subjects: Algorithms; Errors; Galactic clusters; Galaxies; Machine learning; Photometry; Physics - Astrophysics of Galaxies; Physics - Cosmology and Nongalactic Astrophysics; Red shift; Searching; Sky surveys (astronomy); Stars & galaxies
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2104.11335

We introduce Z-Sequence, a novel empirical model that utilises 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 minimise 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 \(|\Delta z|/(1+z)\) (across a photometric redshift range of \(0.05 \le \textit{z} \le 0.6\)) to be \(\sim0.01\) 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.