
Gravitationalwave parameter estimation with autoregressive neural network flows
We introduce the use of autoregressive normalizing flows for rapid likel...
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Learning Bayes' theorem with a neural network for gravitationalwave inference
We wish to achieve the Holy Grail of Bayesian inference with deeplearni...
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Hierarchical Inference With Bayesian Neural Networks: An Application to Strong Gravitational Lensing
In the past few years, approximate Bayesian Neural Networks (BNNs) have ...
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Automating Inference of Binary Microlensing Events with Neural Density Estimation
Automated inference of binary microlensing events with traditional sampl...
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Bayesian parameter estimation using conditional variational autoencoders for gravitationalwave astronomy
Gravitational wave (GW) detection is now commonplace and as the sensitiv...
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New methods to assess and improve LIGO detector duty cycle
A network of three or more gravitational wave detectors simultaneously t...
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Likelihoodfree inference of experimental Neutrino Oscillations using Neural Spline Flows
We discuss the application of Neural Spline Flows, a neural density esti...
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Complete parameter inference for GW150914 using deep learning
The LIGO and Virgo gravitationalwave observatories have detected many exciting events over the past five years. As the rate of detections grows with detector sensitivity, this poses a growing computational challenge for data analysis. With this in mind, in this work we apply deep learning techniques to perform fast likelihoodfree Bayesian inference for gravitational waves. We train a neuralnetwork conditional density estimator to model posterior probability distributions over the full 15dimensional space of binary black hole system parameters, given detector strain data from multiple detectors. We use the method of normalizing flows—specifically, a neural spline normalizing flow—which allows for rapid sampling and density estimation. Training the network is likelihoodfree, requiring samples from the data generative process, but no likelihood evaluations. Through training, the network learns a global set of posteriors: it can generate thousands of independent posterior samples per second for any strain data consistent with the prior and detector noise characteristics used for training. By training with the detector noise power spectral density estimated at the time of GW150914, and conditioning on the event strain data, we use the neural network to generate accurate posterior samples consistent with analyses using conventional sampling techniques.
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