English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Machine learning (ML) techniques, in particular supervised regressionalgorithms, are a promising new way to use multiple observables to predict acluster's mass or other key features. To investigate this approach we use the\textsc{MACSIS} sample of simulated hydrodynamical galaxy clusters to train avariety of ML models, mimicking different datasets. We find that compared topredicting the cluster mass from the $\sigma -M$ relation, the scatter in thepredicted-to-true mass ratio is reduced by a factor of 4, from $0.130\pm0.004$dex (${\simeq} 35$ per cent) to $0.031 \pm 0.001$ dex (${\simeq} 7$ per cent)when using the same, interloper contaminated, spectroscopic galaxy sample.Interestingly, omitting line-of-sight galaxy velocities from the training sethas no effect on the scatter when the galaxies are taken from within$r_{200c}$. We also train ML models to reproduce estimated masses derived frommock X-ray and weak lensing analyses. While the weak lensing masses can berecovered with a similar scatter to that when training on the true mass, thehydrostatic mass suffers from significantly higher scatter of ${\simeq} 0.13$dex (${\simeq} 35$ per cent). Training models using dark matter onlysimulations does not significantly increase the scatter in predicted clustermass compared to training on simulated clusters with hydrodynamics. In summary,we find ML techniques to offer a powerful method to predict masses for largesamples of clusters, a vital requirement for cosmological analysis with futuresurveys.

An application of machine learning techniques to galaxy cluster mass estimation using the MACSIS simulations