A Machine Learning Approach to Enhancing eROSITA Observations

TL;DR

This paper introduces a deep learning algorithm that predicts detailed, long-duration X-ray observations of galaxy clusters from eROSITA data, aiding in efficient follow-up planning with Chandra.

Contribution

We developed a convolutional neural network that generates realistic deep X-ray observations from simulated eROSITA data, improving follow-up selection for galaxy clusters.

Findings

Model accurately reproduces cluster morphology.

Deep learning enhances simulation of follow-up observations.

Method aids in selecting clusters for detailed study.

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 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.