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.
GWKokab is a new modular JAX framework that uses normalizing flow samplers for efficient inference on subpopulations of compact binary mergers.
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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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An Implementation to Identify the Properties of Multiple Population of Gravitational Wave Sources
GWKokab is a new modular JAX framework that uses normalizing flow samplers for efficient inference on subpopulations of compact binary mergers.