Neural spline flows perform fast posterior inference on 11-dimensional millilensed GW parameters with accuracy comparable to dynesty for most quantities and a 3-day to 0.8-second speedup.
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3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
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gr-qc 3years
2025 3representative citing papers
Spin sorting with the default spin model distinguishes spinning and nonspinning binary black hole populations in simulations and shows real data rule out a fully nonspinning population but allow mixed ones with up to 80% nonspinning sources.
GWKokab is a new modular JAX framework that uses normalizing flow samplers for efficient inference on subpopulations of compact binary mergers.
citing papers explorer
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Parameter inference of millilensed gravitational waves using neural spline flows
Neural spline flows perform fast posterior inference on 11-dimensional millilensed GW parameters with accuracy comparable to dynesty for most quantities and a 3-day to 0.8-second speedup.
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Disentangling spinning and nonspinning binary black hole populations with spin sorting
Spin sorting with the default spin model distinguishes spinning and nonspinning binary black hole populations in simulations and shows real data rule out a fully nonspinning population but allow mixed ones with up to 80% nonspinning sources.
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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.