Speaker
Dominik Elsaesser
(TU Dortmund)
Description
Driven by a wealth of upcoming missions and observatories across a wide frequency range, astronomy is entering a new era of survey science. As data volumes grow rapidly and data structures become more heterogeneous, progress regarding source populations will still crucially depend from efficient identification of sources across these multi-frequency and multi-messenger datasets. In recent years, machine learning approaches have matured into a powerful research tool for automatic source classification. In this talk, chances and challenges of machine learning methods as used already for gamma-ray observations will be discussed in the context of upcoming large radio surveys.
Primary author
Dominik Elsaesser
(TU Dortmund)
