Year: 2023
Type: dissertation
Abstract: Gravitational waves, predicted by Albert Einstein in 1916 and first directly observed in 2015, are a powerful window into the universe, and its past. Currently, multiple detectors around the globe are in operation. While the technology has matured to a point where detections are common, there are still unsolved problems. Traditional search algorithms are only optimal under assumptions which do not hold in contemporary detectors. In addition, high data rates and latency requirements can be challenging. In this thesis, we use new methods based on recent advancements in machine learning to tackle these issues. We develop search algorithms competitive with conventional methods in a realistic setting. In doing so, we cover a mock data challenge which we have organized, and which served as a framework to obtain some of these results. Finally, we demonstrate the power of our search algorithms by applying them to data from the second half of LIGO's third observing run. We find that the events targeted by our searches are identified reliably.
Author: Zelenka, Ondřej
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Funders: National Science Foundation, Friedrich-Schiller-Universität Jena, National Research Foundation, Deutsche Forschungsgemeinschaft, Ministry of Education, Culture, Sports, Science and Technology, Academia Sinica, Gottfried Wilhelm Leibniz Universität Hannover, Max-Planck-Gesellschaft, Centre National de la Recherche Scientifique, Istituto Nazionale di Fisica Nucleare, Ministry of Science and ICT, South Korea, National Science and Technology Council, Science and Technology Facilities Council, Leibniz-Gemeinschaft, Japan Society for the Promotion of Science
OpenAlex ID: W6979980384