English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Paper

Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference

MPS-Authors
/persons/resource/persons231046

Green,  Stephen
Astrophysical and Cosmological Relativity, AEI-Golm, MPI for Gravitational Physics, Max Planck Society;

/persons/resource/persons238174

Gair,  Jonathan
Astrophysical and Cosmological Relativity, AEI-Golm, MPI for Gravitational Physics, Max Planck Society;

/persons/resource/persons192115

Pürrer,  Michael
Astrophysical and Cosmological Relativity, AEI-Golm, MPI for Gravitational Physics, Max Planck Society;

/persons/resource/persons127862

Buonanno,  Alessandra
Astrophysical and Cosmological Relativity, AEI-Golm, MPI for Gravitational Physics, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)

2210.05686.pdf
(Preprint), 3MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Dax, M., Green, S., Gair, J., Pürrer, M., Wildberger, J., Macke, J. H., et al. (in preparation). Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference.


Cite as: https://hdl.handle.net/21.11116/0000-000B-428E-B
Abstract
We combine amortized neural posterior estimation with importance sampling for
fast and accurate gravitational-wave inference. We first generate a rapid
proposal for the Bayesian posterior using neural networks, and then attach
importance weights based on the underlying likelihood and prior. This provides
(1) a corrected posterior free from network inaccuracies, (2) a performance
diagnostic (the sample efficiency) for assessing the proposal and identifying
failure cases, and (3) an unbiased estimate of the Bayesian evidence. By
establishing this independent verification and correction mechanism we address
some of the most frequent criticisms against deep learning for scientific
inference. We carry out a large study analyzing 42 binary black hole mergers
observed by LIGO and Virgo with the SEOBNRv4PHM and IMRPhenomXPHM waveform
models. This shows a median sample efficiency of $\approx 10\%$ (two
orders-of-magnitude better than standard samplers) as well as a ten-fold
reduction in the statistical uncertainty in the log evidence. Given these
advantages, we expect a significant impact on gravitational-wave inference, and
for this approach to serve as a paradigm for harnessing deep learning methods
in scientific applications.