  We present a machine learning approach for estimating galaxy cluster masses,
trained using both Chandra and eROSITA mock X-ray observations of 2,041
clusters from the Magneticum simulations. We train a random forest regressor,
an ensemble learning method based on decision tree regression, to predict
cluster masses using an input feature set. The feature set uses core-excised
X-ray luminosity and a variety of morphological parameters, including surface
brightness concentration, smoothness, asymmetry, power ratios, and ellipticity.
The regressor is cross-validated and calibrated on a training sample of 1,615
clusters (80% of sample), and then results are reported as applied to a test
sample of 426 clusters (20% of sample). This procedure is performed for two
different mock observation series in an effort to bracket the potential
enhancement in mass predictions that can be made possible by including
dynamical state information. The first series is computed from idealized
Chandra-like mock cluster observations, with high spatial resolution, long
exposure time (1 Ms), and the absence of background. The second series is
computed from realistic-condition eROSITA mocks with lower spatial resolution,
short exposures (2 ks), instrument effects, and background photons modeled. We
report a 20% reduction in the mass estimation scatter when either series is
used in our random forest model compared to a standard regression model that
only employs core-excised luminosity. The morphological parameters that hold
the highest feature importance are smoothness, asymmetry, and surface
brightness concentration. Hence, these parameters, which encode the dynamical
state of the cluster, can be used to make more accurate predictions of cluster
masses in upcoming surveys, offering a crucial step forward for cosmological
analyses.
