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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Bilby introduces a user-friendly Python library for accurate Bayesian inference on gravitational-wave signals from compact binaries and other sources, including hierarchical population modeling.
Population-informed hierarchical parameter estimation is required for unbiased astrophysical interpretation of gravitational-wave events rather than using standard individual posteriors with reference priors.
Probabilistic neural networks recover consistent posteriors to Gaussian processes in PTA gravitational-wave background inference while substantially reducing training and MCMC runtime.
citing papers explorer
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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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Bilby: A user-friendly Bayesian inference library for gravitational-wave astronomy
Bilby introduces a user-friendly Python library for accurate Bayesian inference on gravitational-wave signals from compact binaries and other sources, including hierarchical population modeling.
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Gravitational-wave astronomy requires population-informed parameter estimation
Population-informed hierarchical parameter estimation is required for unbiased astrophysical interpretation of gravitational-wave events rather than using standard individual posteriors with reference priors.
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Replacing Gaussian Processes with Neural Networks in Pulsar Timing Array Inference of the Gravitational-Wave Background
Probabilistic neural networks recover consistent posteriors to Gaussian processes in PTA gravitational-wave background inference while substantially reducing training and MCMC runtime.
- Inferring the properties of a population of compact binaries in presence of selection effects