Speaker
Description
Accurate modeling of gravitational-wave (GW) waveforms is a crucial component of GW detection and parameter estimation. The waveform templates for GW astronomy are primarily based on the effective-one-body framework and the phenomenological framework. However, in the case of binary systems with complex orbital dynamics, such as eccentric binaries, these waveform templates still face challenges in terms of computational efficiency or accuracy. Over the past decade, reduced-order modeling has significantly accelerated the generation of accurate waveforms, leading to the development of a series of numerical relativity surrogate models. Meanwhile, the rapid progress of machine learning has given rise to a new generation of data-driven surrogate models. In this talk, I will briefly review the current state of data-driven waveform modeling and present our recent work on a deep learning-based model for rapid generation of eccentric binary black hole (BBH) waveforms. Furthermore, I will discuss the potential applications of such data-driven approaches in gravitational-wave astronomy.
| Session Selection | Astronomy and Astrophysics |
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