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

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Z-Sequence: photometric redshift predictions for galaxy clusters with sequential random k-nearest neighbours : Estimating Photometric Redshifts Of Clusters. / Chan, Matthew; Stott, John.

In: Monthly Notices of the Royal Astronomical Society, Vol. 503, No. 4, 30.06.2021, p. 6078-6097.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Chan M, Stott J. 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. 2021 Jun 30;503(4):6078-6097. Epub 2021 Mar 26. doi: 10.1093/mnras/stab858

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@article{a87a089e8195454a8ed261c7b18d6400,
title = "Z-Sequence: photometric redshift predictions for galaxy clusters with sequential random k-nearest neighbours: Estimating Photometric Redshifts Of Clusters",
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.",
keywords = "astro-ph.GA, astro-ph.CO",
author = "Matthew Chan and John Stott",
year = "2021",
month = jun,
day = "30",
doi = "10.1093/mnras/stab858",
language = "English",
volume = "503",
pages = "6078--6097",
journal = "Monthly Notices of the Royal Astronomical Society",
issn = "0035-8711",
publisher = "OXFORD UNIV PRESS",
number = "4",

}

RIS

TY - JOUR

T1 - Z-Sequence: photometric redshift predictions for galaxy clusters with sequential random k-nearest neighbours

T2 - Estimating Photometric Redshifts Of Clusters

AU - Chan, Matthew

AU - Stott, John

PY - 2021/6/30

Y1 - 2021/6/30

N2 - 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.

AB - 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.

KW - astro-ph.GA

KW - astro-ph.CO

U2 - 10.1093/mnras/stab858

DO - 10.1093/mnras/stab858

M3 - Journal article

VL - 503

SP - 6078

EP - 6097

JO - Monthly Notices of the Royal Astronomical Society

JF - Monthly Notices of the Royal Astronomical Society

SN - 0035-8711

IS - 4

ER -