Speaker
Description
Galaxy clusters are the largest gravitationally bound structures in the universe, hosting megaparsec-scale diffuse radio emission in the form of halos and relics. These non-thermal synchrotron sources trace shocks, turbulence, and magnetic fields in the intracluster medium, providing a unique window into particle acceleration processes and cosmic magnetism. The upcoming Square Kilometre Array (SKA) will revolutionise our ability to study these faint, extended sources, but it will also require advanced automation in data analysis. To prepare for the SKA era, statistically robust mock datasets and automated tools are essential for overcoming observational biases and enabling effective machine learning (ML) applications. As a key component of this effort, we are developing observation-based framework to generate realistic mock catalogues of cluster-scale radio emission. These catalogues will play a critical role in training automated detection algorithms and assessing biases in observational samples. This talk will present an overview of the framework and the initial results from our study.
| Stream | Science or Engineering |
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