Z-Sequence: photometric redshift predictions for galaxy clusters with sequential random k-nearest neighbours
Abstract
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 |Δ$$z$$|/(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.
- Authors:
-
- Department of Physics, Lancaster University, Lancaster LA1 4YB, UK
- Publication Date:
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1778569
- Resource Type:
- Published Article
- Journal Name:
- Monthly Notices of the Royal Astronomical Society
- Additional Journal Information:
- Journal Name: Monthly Notices of the Royal Astronomical Society Journal Volume: 503 Journal Issue: 4; Journal ID: ISSN 0035-8711
- Publisher:
- Oxford University Press
- Country of Publication:
- United Kingdom
- Language:
- English
Citation Formats
Chan, Matthew C., and Stott, John P. Z-Sequence: photometric redshift predictions for galaxy clusters with sequential random k-nearest neighbours. United Kingdom: N. p., 2021.
Web. doi:10.1093/mnras/stab858.
Chan, Matthew C., & Stott, John P. Z-Sequence: photometric redshift predictions for galaxy clusters with sequential random k-nearest neighbours. United Kingdom. https://doi.org/10.1093/mnras/stab858
Chan, Matthew C., and Stott, John P. Fri .
"Z-Sequence: photometric redshift predictions for galaxy clusters with sequential random k-nearest neighbours". United Kingdom. https://doi.org/10.1093/mnras/stab858.
@article{osti_1778569,
title = {Z-Sequence: photometric redshift predictions for galaxy clusters with sequential random k-nearest neighbours},
author = {Chan, Matthew C. and Stott, John P.},
abstractNote = {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 |Δ$z$|/(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.},
doi = {10.1093/mnras/stab858},
journal = {Monthly Notices of the Royal Astronomical Society},
number = 4,
volume = 503,
place = {United Kingdom},
year = {Fri Mar 26 00:00:00 EDT 2021},
month = {Fri Mar 26 00:00:00 EDT 2021}
}
https://doi.org/10.1093/mnras/stab858
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