Towards a Foundational AI model for Gravitational Wave Data Analysis

With the advent of third-generation gravitational wave detectors and increasing sensitivity of the current LIGO instruments, the number of detected gravitational wave signals from compact binary mergers is expected to grow exponentially. This presents a significant computational challenge, as traditional pipelines for gravitational wave detection, parameter estimation, and detector characterization often rely on optimal but computationally intensive techniques. In this work, we leverage the transformative potential of state-of-the-art transformer models, particularly OpenAI's Whisper, for gravitational wave detection and parameter estimation using real LIGO data. We fine-tune Whisper—originally trained on 680,000 hours of human speech—on a dataset of simulated gravitational wave signals and demonstrate promising results on these tasks. This work aims to lay the groundwork for developing a foundational AI model for gravitational wave data analysis using large language models, which has the potential to address the challenges of scalability and real-time processing of gravitational wave data.