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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gr-qc 2years
2025 2verdicts
CONDITIONAL 2representative citing papers
GP15, a ResNet plus normalizing-flow model trained on stacked spectrograms, recovers most binary black hole parameters in agreement with LVK posteriors on 33 three-detector events, with generation of 10,000 posterior samples in about one second.
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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Assessment of normalizing flows for parameter estimation on time-frequency representations of gravitational-wave data
GP15, a ResNet plus normalizing-flow model trained on stacked spectrograms, recovers most binary black hole parameters in agreement with LVK posteriors on 33 three-detector events, with generation of 10,000 posterior samples in about one second.