Complete parameter inference for GW150914 using deep learning
Problem
Iterative MCMC sampling for gravitational-wave parameter inference is computationally costly as detection rates grow and fast multimessenger alerts are needed.
Why it matters
Growing detector sensitivity increases detections, making rapid inference essential.
What the AI did
A neural-network conditional density estimator (normalizing flows, neural spline flow) was trained to learn non-iterative surrogate models of the Bayesian posterior over the full 15-dimensional binary black hole parameter space.
Results
not stated in source
Impact
Evidence supports a demonstrated method applied to GW150914; no quantitative accuracy is stated.
Record
| Venue | Machine Learning Science and Technology |
|---|---|
| Published | 2021 |
| Work type | article |
| Authors | Stephen Green, J. R. Gair |
| Citations | 112 (OpenAlex, at import time) |
| References | 58 |
| Model or method | Normalizing flows (neural spline flow) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1088/2632-2153/abfaed |
| Open access | gold |
| Keywords | gravitational waves, GW150914, normalizing flows, deep learning, parameter inference, Bayesian posterior |
Importance rating
significant — thousands to millions affected
Abstract states detection rates grow with detector sensitivity and fast multimessenger alerts are needed; supports a major physics field, but no quantified scale given.
More in Physics & Astronomy
| # | Paper | Year | Citations | Importance |
|---|---|---|---|---|
| 1 | Magnetic control of tokamak plasmas through deep reinforcement learning | 2022 | 739 |
7
|
| 2 | Solving the quantum many-body problem with artificial neural networks | 2017 | 2,205 |
7
|
| 3 | Learning high-accuracy error decoding for quantum processors | 2024 | 90 |
7
|
| 4 | Optimizing Quantum Error Correction Codes with Reinforcement Learning | 2019 | 158 |
7
|
| 5 | Machine-learning-assisted correction of correlated qubit errors in a topological code | 2018 | 141 |
7
|