  The eROSITA X-ray telescope, launched in 2019, is predicted to observe
roughly 100,000 galaxy clusters. Follow-up observations of these clusters from
Chandra, for example, will be needed to resolve outstanding questions about
galaxy cluster physics. Deep Chandra cluster observations are expensive and
follow-up of every eROSITA cluster is infeasible, therefore, objects chosen for
follow-up must be chosen with care. To address this, we have developed an
algorithm for predicting longer duration, background-free observations based on
mock eROSITA observations. We make use of the hydrodynamic cosmological
simulation Magneticum, have simulated eROSITA instrument conditions using
SIXTE, and have applied a novel convolutional neural network to output a deep
Chandra-like "super observation" of each cluster in our simulation sample. Any
follow-up merit assessment tool should be designed with a specific use case in
mind; our model produces observations that accurately and precisely reproduce
the cluster morphology, which is a critical ingredient for determining cluster
dynamical state and core type. Our model will advance our understanding of
galaxy clusters by improving follow-up selection and demonstrates that
image-to-image deep learning algorithms are a viable method for simulating
realistic follow-up observations.
