Lightning-Fast Gravitational Wave Parameter Inference through Neural Amortization
This addresses a computational bottleneck for astrophysicists analyzing gravitational wave data, though it appears incremental as it builds on existing neural inference techniques.
The paper tackled the slow parameter inference for gravitational waves from compact binaries, which typically takes days using Markov Chain Monte Carlo, and demonstrated a neural simulation-based inference method that speeds it up by up to three orders of magnitude to minutes without performance loss.
Gravitational waves from compact binaries measured by the LIGO and Virgo detectors are routinely analyzed using Markov Chain Monte Carlo sampling algorithms. Because the evaluation of the likelihood function requires evaluating millions of waveform models that link between signal shapes and the source parameters, running Markov chains until convergence is typically expensive and requires days of computation. In this extended abstract, we provide a proof of concept that demonstrates how the latest advances in neural simulation-based inference can speed up the inference time by up to three orders of magnitude -- from days to minutes -- without impairing the performance. Our approach is based on a convolutional neural network modeling the likelihood-to-evidence ratio and entirely amortizes the computation of the posterior. We find that our model correctly estimates credible intervals for the parameters of simulated gravitational waves.