Dingo-Pop: AI-Powered Gravitational Wave Population Inference

Dingo-Pop introduces a novel approach to gravitational wave population inference, leveraging the power of transformers to overcome the computational limitations of traditional Bayesian methods. By embedding gravitational wave strain data into low-dimensional tokens and training a transformer on simulated catalogs, Dingo-Pop achieves significant speedups in population posterior estimation. The ability to perform end-to-end inference in approximately one second represents a substantial improvement over per-event analyses that can take hours or days. This acceleration enables new classes of large-scale injection studies, allowing researchers to explore the impact of selection effects and systematic uncertainties on population inference. The application of Dingo-Pop to the estimation of the Hubble constant demonstrates its potential for addressing fundamental questions in cosmology. The framework's ability to handle variable catalog sizes using a single network is a key advantage, allowing it to scale efficiently as the number of detected gravitational wave events continues to grow. The well-calibrated posteriors obtained with Dingo-Pop, consistent with traditional methods, provide confidence in its accuracy and reliability. As gravitational wave detectors become more sensitive and the number of detected events increases, Dingo-Pop and similar AI-powered tools will play an increasingly important role in extracting valuable astrophysical information from these data.

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