Hybrid Workshop: While this workshop is offered in-person, participants have the option to attend talks virtually. Please indicate in your registration form if you will attend this workshop in-person or virtually under the “Participant Category” field. Both in-person and virtual participants will receive instruction on how to participate a couple weeks prior to the workshop. Please note that remote participation is subject to technological constraints that may limit interaction with speakers and other participants.
Detection of gravitational waves requires the operation of very sophisticated detectors producing large amounts of data. The sensitivity of the gravitational-wave detectors to astrophysical signals is limited by the noise associated with the instruments themselves and their environment. Invaluable astrophysical information is buried in data sets that may be too large or complex to be analyzed with traditional data-processing techniques.
To make the analysis of gravitational-wave detector data more efficient it becomes increasingly more important to characterize and mitigate the detector noise sources, as well as find more powerful ways to extract information from the detector data. Methods for the analysis of gravitational-wave detector data range from standard signal processing algorithms to novel machine learning algorithms. This workshop will focus on the development of these techniques for a more efficient handling of gravitational-wave data sets, reduction of detector noise, identification of astrophysical signals and increase in detection confidence. It will bring together astrophysicists, mathematicians and statisticians working on the state-of-the-art data analysis.
This workshop will include a poster session; a request for posters will be sent to registered participants in advance of the workshop.
Hybrid Workshop: While this workshop is offered in-person, participants can also register and attend talks virtually. Register here.
(Missouri University of Science and Technology)
Peter Couvares (California Institute of Technology)
Gabriela González (Louisiana State University)
Ik Siong Heng (University of Glasgow)
Antonio Marquina (University of Valencia)