Machine learning methods for gravitational-wave data analysis
Maximilian Dax – Hector Fellow Bernhard Schölkopf
The detection of gravitational waves (GWs) has opened a new window on the universe, through which we can study the physics of black-hole and neutron-star mergers. By analyzing GWs we can infer properties of the corresponding astrophysical systems. Current analysis methods are however too computationally expensive to deal with the growing amount of data. My research is thus concerned with the development of more efficient methods for the GW analysis using powerful machine learning methods.