AI-Driven Waveform Error Compensation for Precision Cosmology

Dhani et al. (2024) reveal a critical flaw: current waveform models (e.g., SEOBNRv5PHM) introduce biases in spin and mass estimates that propagate to cosmological parameters like H₀. This is catastrophic for "golden" loud events. While Stachurski et al. (2023) used normalizing flows for inference, we propose using generative adversarial networks (GANs) to synthesize corrected waveforms. By training on numerical-relativity simulations (the ground truth), the GAN would learn the mapping between biased and accurate waveforms across parameter space (e.g., high mass ratios, strong precession). Unlike Dhani et al.’s post-analysis bias quantification, our approach would embed corrections directly into the data pipeline. This could enable unbiased H₀ measurements from LIGO-Virgo-KAGRA (LVK) events and future 3G detectors, where biases worsen with redshift.

References:

1. Systematic Biases in Estimating the Properties of Black Holes Due to Inaccurate Gravitational-Wave Models. A. Dhani, S. Volkel, A. Buonanno, H. Estellés, J. Gair, H. Pfeiffer, L. Pompili, A. Toubiana (2024).
2. Cosmological inference using gravitational waves and normalizing flows. F. Stachurski, C. Messenger, M. Hendry (2023). Physical Review D.