Rights statement: This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Monthly Notices of the Royal Astronomical Society following peer review. The definitive publisher-authenticated version Matthew C Chan, John P Stott, AutoEnRichness: A hybrid empirical and analytical approach for estimating the richness of galaxy clusters, Monthly Notices of the Royal Astronomical Society, Volume 516, Issue 1, October 2022, Pages 316–335, https://doi.org/10.1093/mnras/stac2210 is available online at: https://academic.oup.com/mnras/article-abstract/516/1/316/6657608
Accepted author manuscript, 7.59 MB, PDF document
Available under license: CC BY: Creative Commons Attribution 4.0 International License
Accepted author manuscript
Licence: CC BY: Creative Commons Attribution 4.0 International License
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - AutoEnRichness: A hybrid empirical and analytical approach for estimating the richness of galaxy clusters
T2 - Estimating Galaxy Cluster Richness
AU - Chan, Matthew
AU - Stott, John
N1 - This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Monthly Notices of the Royal Astronomical Society following peer review. The definitive publisher-authenticated version Matthew C Chan, John P Stott, AutoEnRichness: A hybrid empirical and analytical approach for estimating the richness of galaxy clusters, Monthly Notices of the Royal Astronomical Society, Volume 516, Issue 1, October 2022, Pages 316–335, https://doi.org/10.1093/mnras/stac2210 is available online at: https://academic.oup.com/mnras/article-abstract/516/1/316/6657608
PY - 2022/8/24
Y1 - 2022/8/24
N2 - We introduce AutoEnRichness, a hybrid approach that combines empirical and analytical strategies to determine the richness of galaxy clusters (in the redshift range of 0.1 ≤ z ≤ 0.35) using photometry data from the Sloan Digital Sky Survey Data Release 16, where cluster richness can be used as a proxy for cluster mass. In order to reliably estimate cluster richness, it is vital that the background subtraction is as accurate as possible when distinguishing cluster and field galaxies to mitigate severe contamination. AutoEnRichness is comprised of a multi-stage machine learning algorithm that performs background subtraction of interloping field galaxies along the cluster line-of-sight and a conventional luminosity distribution fitting approach that estimates cluster richness based only on the number of galaxies within a magnitude range and search area. In this proof-of-concept study, we obtain a balanced accuracy of 83.20 per cent when distinguishing between cluster and field galaxies as well as a median absolute percentage error of 33.50 per cent between our estimated cluster richnesses and known cluster richnesses within r200. In the future, we aim for AutoEnRichness to be applied on upcoming large-scale optical surveys, such as the Legacy Survey of Space and Time and Euclid, to estimate the richness of a large sample of galaxy groups and clusters from across the halo mass function. This would advance our overall understanding of galaxy evolution within overdense environments as well as enable cosmological parameters to be further constrained.
AB - We introduce AutoEnRichness, a hybrid approach that combines empirical and analytical strategies to determine the richness of galaxy clusters (in the redshift range of 0.1 ≤ z ≤ 0.35) using photometry data from the Sloan Digital Sky Survey Data Release 16, where cluster richness can be used as a proxy for cluster mass. In order to reliably estimate cluster richness, it is vital that the background subtraction is as accurate as possible when distinguishing cluster and field galaxies to mitigate severe contamination. AutoEnRichness is comprised of a multi-stage machine learning algorithm that performs background subtraction of interloping field galaxies along the cluster line-of-sight and a conventional luminosity distribution fitting approach that estimates cluster richness based only on the number of galaxies within a magnitude range and search area. In this proof-of-concept study, we obtain a balanced accuracy of 83.20 per cent when distinguishing between cluster and field galaxies as well as a median absolute percentage error of 33.50 per cent between our estimated cluster richnesses and known cluster richnesses within r200. In the future, we aim for AutoEnRichness to be applied on upcoming large-scale optical surveys, such as the Legacy Survey of Space and Time and Euclid, to estimate the richness of a large sample of galaxy groups and clusters from across the halo mass function. This would advance our overall understanding of galaxy evolution within overdense environments as well as enable cosmological parameters to be further constrained.
KW - astro-ph.GA
KW - astro-ph.CO
U2 - 10.1093/mnras/stac2210
DO - 10.1093/mnras/stac2210
M3 - Journal article
VL - 516
SP - 316
EP - 335
JO - Monthly Notices of the Royal Astronomical Society
JF - Monthly Notices of the Royal Astronomical Society
SN - 0035-8711
IS - 1
ER -