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.
Real-Time Gravitational Wave Science with Neural Posterior Estimation
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
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2026 7representative citing papers
Normalizing flows replace binned histograms for estimating multi-detector signal parameters in PyCBC, slashing storage by three orders of magnitude with under 0.05% sensitivity loss and up to 6.55% gains in specific cases.
Neural network surrogate approximates precessing compact binary gravitational waveforms up to 1000x faster than the base EOB model with validated accuracy.
A glitch-robust amortized inference framework combining normalizing flows, time-frequency multimodal fusion, and contrastive learning outperforms MCMC for Taiji massive black hole binary parameter estimation under noise contamination.
Maximum-likelihood-based posterior predictive checks detect model misspecification better than event-level versions for uncertain spin tilts, but current detector sensitivity limits their power; the Gaussian Component Spins model underpredicts high spin magnitudes and overpredicts anti-aligned tilts
Simulation-based inference reliably extracts physical parameters from noisy spectra of analogue black holes.
Baselines of 8-11 ms light travel time for two CE detectors provide a reasonable compromise for BBH sky localization, with third detectors eliminating multimodality for most or all events.
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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Normalizing flows for density estimation in multi-detector gravitational-wave searches
Normalizing flows replace binned histograms for estimating multi-detector signal parameters in PyCBC, slashing storage by three orders of magnitude with under 0.05% sensitivity loss and up to 6.55% gains in specific cases.
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Fast neural network surrogate for multimodal effective-one-body gravitational waveforms from generically precessing compact binaries
Neural network surrogate approximates precessing compact binary gravitational waveforms up to 1000x faster than the base EOB model with validated accuracy.
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Robust parameter inference for Taiji via time-frequency contrastive learning and normalizing flows
A glitch-robust amortized inference framework combining normalizing flows, time-frequency multimodal fusion, and contrastive learning outperforms MCMC for Taiji massive black hole binary parameter estimation under noise contamination.
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Posterior Predictive Checks for Gravitational-wave Populations: Limitations and Improvements
Maximum-likelihood-based posterior predictive checks detect model misspecification better than event-level versions for uncertain spin tilts, but current detector sensitivity limits their power; the Gaussian Component Spins model underpredicts high spin magnitudes and overpredicts anti-aligned tilts
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Spectroscopy of analogue black holes using simulation-based inference
Simulation-based inference reliably extracts physical parameters from noisy spectra of analogue black holes.
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Not too close! Evaluating the impact of the baseline on the localization of binary black holes by next-generation gravitational-wave detectors
Baselines of 8-11 ms light travel time for two CE detectors provide a reasonable compromise for BBH sky localization, with third detectors eliminating multimodality for most or all events.