  We evaluate the effectiveness of deep learning (DL) models for reconstructing
the masses of galaxy clusters using X-ray photometry data from next-generation
surveys. We establish these constraints using a catalogue of realistic mock
eROSITA X-ray observations which use hydrodynamical simulations to model
realistic cluster morphology, background emission, telescope response, and AGN
sources. Using bolometric X-ray photon maps as input, DL models achieve a
predictive mass scatter of $\sigma_{\ln M_\mathrm{500c}} = 17.8\%$, a factor of
two improvements on scalar observables such as richness $N_\mathrm{gal}$, 1D
velocity dispersion $\sigma_\mathrm{v,1D}$, and photon count $N_\mathrm{phot}$
as well as a $32\%$ improvement upon idealised, volume-integrated measurements
of the bolometric X-ray luminosity $L_X$. We then show that extending this
model to handle multichannel X-ray photon maps, separated in low, medium, and
high energy bands, further reduces the mass scatter to $16.2\%$. We also tested
a multimodal DL model incorporating both dynamical and X-ray cluster probes and
achieved marginal gains at a mass scatter of $15.9\%$. Finally, we conduct a
quantitative interpretability study of our DL models and find that they greatly
down-weight the importance of pixels in the centres of clusters and at the
location of AGN sources, validating previous claims of DL modelling
improvements and suggesting practical and theoretical benefits for using DL in
X-ray mass inference.
