Complete parameter inference for GW150914 using deep learning. (arXiv:2008.03312v1 [astro-ph.IM])

<a href="http://arxiv.org/find/astro-ph/1/au:+Green_S/0/1/0/all/0/1">Stephen R. Green</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Gair_J/0/1/0/all/0/1">Jonathan Gair</a>

The LIGO and Virgo gravitational-wave 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 likelihood-free Bayesian inference for gravitational waves. We

train a neural-network conditional density estimator to model posterior

probability distributions over the full 15-dimensional 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 likelihood-free, 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.

The LIGO and Virgo gravitational-wave 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 likelihood-free Bayesian inference for gravitational waves. We

train a neural-network conditional density estimator to model posterior

probability distributions over the full 15-dimensional 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 likelihood-free, 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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