Machine learning ( ML ) techniques , in particular supervised regression algorithms , are a promising new way to use multiple observables to predict a cluster ’ s mass or other key features .
To investigate this approach we use the MACSIS sample of simulated hydrodynamical galaxy clusters to train a variety of ML models , mimicking different datasets .
We find that compared to predicting the cluster mass from the \sigma - M relation , the scatter in the predicted-to-true mass ratio is reduced by a factor of 4 , from 0.130 \pm 0.004 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 set has no effect on the scatter when the galaxies are taken from within r _ { 200 c } .
We also train ML models to reproduce estimated masses derived from mock X-ray and weak lensing analyses .
While the weak lensing masses can be recovered with a similar scatter to that when training on the true mass , the hydrostatic mass suffers from significantly higher scatter of { \simeq } 0.13 dex ( { \simeq } 35 per cent ) .
Training models using dark matter only simulations does not significantly increase the scatter in predicted cluster mass compared to training on simulated clusters with hydrodynamics .
In summary , we find ML techniques to offer a powerful method to predict masses for large samples of clusters , a vital requirement for cosmological analysis with future surveys .