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.
@article{arxiv.2207.14324,
title = {A Machine Learning Approach to Enhancing eROSITA Observations},
author = {John Soltis and Michelle Ntampaka and John Wu and John ZuHone and August Evrard and Arya Farahi and Matthew Ho and Daisuke Nagai},
journal= {arXiv preprint arXiv:2207.14324},
year = {2022}
}
Comments
21 pages, 11 figures, 3 tables. Minor changes upon revision. Corrected caption of Figure 3. Added discussion of alternative asymmetry metrics. To be published in the Astrophysical Journal