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.
Mining Gravitational-wave Catalogs To Understand Binary Stellar Evolution: A New Hierarchical Bayesian Framework
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
abstract
Catalogs of stellar-mass compact binary systems detected by ground-based gravitational-wave instruments (such as Advanced LIGO and Advanced Virgo) will offer insights into the demographics of progenitor systems and the physics guiding stellar evolution. Existing techniques approach this through phenomenological modeling, discrete model selection, or model mixtures. Instead, we explore a novel technique that mines gravitational-wave catalogs to directly infer posterior probability distributions of the hyper-parameters describing formation and evolutionary scenarios (e.g. progenitor metallicity, kick parameters, and common-envelope efficiency). We use a bank of compact-binary population synthesis simulations to train a Gaussian-process emulator that acts as a prior on observed parameter distributions (e.g. chirp mass, redshift, rate). This emulator slots into a hierarchical population inference framework to extract the underlying astrophysical origins of systems detected by Advanced LIGO and Advanced Virgo. Our method is fast, easily expanded with additional simulations, and can be adapted for training on arbitrary population synthesis codes, as well as different detectors like LISA.
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representative citing papers
Joint strong-lensing and population inference on resolved gravitational-wave events finds no lensed events and tightens constraints on the black-hole merger rate peak redshift and high-redshift tail.
A neural posterior estimator trained on simulated LISA foreground spectra recovers galactic binary population parameters, including total number, with good accuracy in validation tests.
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.
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.
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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Joint population and strong-lensing inference for resolved gravitational-wave events probes the black-hole merger rate beyond the peak of star formation
Joint strong-lensing and population inference on resolved gravitational-wave events finds no lensed events and tightens constraints on the black-hole merger rate peak redshift and high-redshift tail.
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Inferring the population properties of galactic binaries from LISA's stochastic foreground
A neural posterior estimator trained on simulated LISA foreground spectra recovers galactic binary population parameters, including total number, with good accuracy in validation tests.
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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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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.
- Replacing Gaussian Processes with Neural Networks in Pulsar Timing Array Inference of the Gravitational-Wave Background