Enhancing the reliability of machine learning for gravitational wave parameter estimation with attention-based models
This work addresses reliability issues in gravitational wave astronomy for astrophysicists, but it is incremental as it builds on existing attention-based methods.
The authors tackled the problem of unreliable gravitational wave parameter estimation from binary black hole mergers by developing Vision Transformer models to estimate effective spin and chirp mass, and they used attention maps to show that as models focus more on glitches, parameter estimates become more biased, suggesting a method to distinguish reliable results.
We introduce a technique to enhance the reliability of gravitational wave parameter estimation results produced by machine learning. We develop two independent machine learning models based on the Vision Transformer to estimate effective spin and chirp mass from spectrograms of gravitational wave signals from binary black hole mergers. To enhance the reliability of these models, we utilize attention maps to visualize the areas our models focus on when making predictions. This approach enables demonstrating that both models perform parameter estimation based on physically meaningful information. Furthermore, by leveraging these attention maps, we demonstrate a method to quantify the impact of glitches on parameter estimation. We show that as the models focus more on glitches, the parameter estimation results become more strongly biased. This suggests that attention maps could potentially be used to distinguish between cases where the results produced by the machine learning model are reliable and cases where they are not.