A transformer-based model counts and separates up to five overlapping compact binary merger signals in simulated Cosmic Explorer noise, achieving 99.89% counting accuracy and high waveform overlap.
Gravitational Wave Mixture Separation for Future Gravitational Wave Observatories Utilizing Deep Learning
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abstract
Future GW observatories, such as the Einstein Telescope (ET), are expected to detect gravitational wave signals, some of which are likely to overlap with each other. This overlap may lead to misidentification as a single GW event, potentially biasing the estimated parameters of mixture GWs. In this paper, we adapt the concept of speech separation to address this issue by applying it to signal separation of overlapping GWs. We show that deep learning models can effectively separate overlapping GW signals. The proposed method may aid in eliminating biases in parameter estimation for such signals.
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Compact Binary Coalescence Gravitational Wave Signals Counting and Separation
A transformer-based model counts and separates up to five overlapping compact binary merger signals in simulated Cosmic Explorer noise, achieving 99.89% counting accuracy and high waveform overlap.