A Machine-learning Approach to Enhancing eROSITA Observations

Abstract

Abstract 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 it is unfeasible to follow up every eROSITA cluster, therefore the objects that are 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, simulate eROSITA instrument conditions using SIXTE, and apply 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 a cluster’s dynamical state and core type. Our model will advance our understanding of galaxy clusters by improving follow-up selection, and it demonstrates that image-to-image deep learning algorithms are a viable method for simulating realistic follow-up observations.