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arXiv · 2605.11274

End-to-End Population Inference from Gravitational-Wave Strain using Transformers

Konstantin Leyde·Stephen R. Green·Maximilian Dax·Matthew Mould·Cecilia Maria Fabbri·Jonathan Gair
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Abstract

The population of compact binaries encodes information about their astrophysical origins and the expansion of the universe. Hierarchical Bayesian methods infer these properties by combining single-event posteriors. As catalogs grow, however, this approach becomes computationally expensive and is subject to increasing Monte Carlo uncertainty. We introduce Dingo-Pop, a simulation-based framework that infers population posteriors directly from gravitational-wave strain data. The data for each event are embedded into low-dimensional tokens and combined using a transformer trained on simulated catalogs subject to selection effects. This enables (i) population inference without per-event Monte Carlo sampling noise, (ii) amortization across variable catalog sizes using a single network, and (iii) end-to-end inference in about one second. We train a network for catalog sizes of 25 to 1000 events, and obtain well-calibrated posteriors consistent with traditional methods. By avoiding per-event analyses that can take hours to days, Dingo-Pop enables new classes of large-scale injection studies; as an application, we examine how spectral-siren Hubble constant uncertainties change with catalog size.

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BibTeXRIS

Konstantin Leyde, Stephen R. Green, Maximilian Dax, Matthew Mould, Cecilia Maria Fabbri, Jonathan Gair. 2026-05-11. End-to-End Population Inference from Gravitational-Wave Strain using Transformers. https://arxiv.org/abs/2605.11274

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