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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