Dingo-Pop uses a transformer to perform amortized, end-to-end population inference from GW strain data in seconds, bypassing per-event Monte Carlo sampling.
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Demonstrates direct comparison of observable compact-binary populations from GW data to astrophysical models, with unbiased inference shown possible and applied to O3 data.
Pedagogical derivation from first principles of hierarchical Bayesian inference for population properties of compact binaries in the presence of selection effects, with two worked examples.
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End-to-End Population Inference from Gravitational-Wave Strain using Transformers
Dingo-Pop uses a transformer to perform amortized, end-to-end population inference from GW strain data in seconds, bypassing per-event Monte Carlo sampling.
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Comparing astrophysical models to gravitational-wave data in the observable space
Demonstrates direct comparison of observable compact-binary populations from GW data to astrophysical models, with unbiased inference shown possible and applied to O3 data.
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Inferring the properties of a population of compact binaries in presence of selection effects
Pedagogical derivation from first principles of hierarchical Bayesian inference for population properties of compact binaries in the presence of selection effects, with two worked examples.