Speaker
Description
An important aspect of gravitational-wave astronomy is solving inverse problems, i.e., determining the properties of astrophysical sources from their gravitational signals. This involves the construction of complex forward models for possible signals by solving the equations of general relativity, as well as the use of these forward models in data-analysis algorithms to extract and characterise actual signals in detector data. Machine learning is increasingly used to confront modern challenges in these tasks, although it faces unique hurdles such as noise-dominated data and the need for high precision in modelling. It is also crucial to clarify how any proposed learning method relates to the existing Bayesian framework for solving gravitational-wave inverse problems. In this talk, I will discuss broad strategies for developing machine-learning methods that are tailored to the needs of the field while remaining defensible on scientific rigour and principle.
| Contribution length | Short |
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