Best of both worlds: integrating principled matched-filtering searches with AI/ML tools

In the infinite data and compute limit, machine learning (ML) methods can be optimal, however this idealistic situation is not often realized in practice. On the other hand, principled data-analysis methods are robust, but they make simplistic assumptions (e.g., the noise is roughly Gaussian). I will present how ML algorithms can enhance matched-filtering gravitational wave pipelines by: (i) generating optimal template banks (ii) weighting templates to downplay unphysical binary configurations (iii) mitigating non-Gaussian noise. Incorporating these advancements in the IAS search pipeline, I will present new detections of black holes in the astrophysically significant pair-instability mass gap and IMBH mass ranges. I will also showcase how large language model (LLM) agentic systems can lower the entry barrier for non-specialists to work with GW data-analysis codebases.