Learning Bayesian Posteriors with Neural Networks for Gravitational-Wave Inference

Area of Science:

• Astrophysics
• Machine Learning
• Data Science

Background:

• Bayesian inference is crucial for gravitational-wave astronomy but computationally intensive.
• Current methods for parameter estimation can be slow, limiting real-time analysis.
• Deep learning offers potential for accelerating complex scientific computations.

Purpose of the Study:

• To develop a deep learning framework for rapid Bayesian inference in gravitational-wave astronomy.
• To create a neural network that directly outputs posterior distributions for source parameters.
• To enable near-instantaneous parameter estimation from gravitational-wave detector data.

Main Methods:

• Trained a deep neural network to approximate posterior distributions.
• Utilized reduced-order modeling for compact data representation.
• Employed a neural-network waveform interpolant for efficient model generation.

Main Results:

• The deep learning model successfully generates parametrized approximations of posterior distributions.
• The scheme leverages efficient data compression via reduced-order modeling.
• The approach significantly speeds up the Bayesian inference process.

Conclusions:

• This deep learning approach achieves rapid Bayesian inference for gravitational-wave data.
• The method has significant implications for low-latency parameter estimation.
• It will aid in characterizing the scientific potential of future gravitational-wave observatories.