A conditional variational autoencoder trained on simulated microlensed binary black hole signals estimates lens mass and source offset with well-calibrated posteriors, runs about 10,000 times faster than Bilby, and cuts Bilby's runtime by half when its estimates guide the priors.
Use of conditional variational auto encoder to analyze ringdown gravitational waves
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abstract
Recently, several deep learning methods are proposed for the gravitational wave data analysis. One is conditional variational auto encoder (CVAE), proposed by Gabbard et al. [1]. We study the accuracy of a CVAE in the context of the estimation of the QNM frequency of the ringdown. We show that the accuracy of the estimation by the CVAE is better than the matched filtering. The areas of confidence regions are also compared and it is shown that the CVAE can return smaller confidence regions. Also, we assess the reliability of the confidence regions estimated by the CVAE. Our work confirms that the deep learning method has ability to compete with or overcome the matched filtering.
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Parameter estimation of microlensed gravitational waves with Conditional Variational Autoencoders
A conditional variational autoencoder trained on simulated microlensed binary black hole signals estimates lens mass and source offset with well-calibrated posteriors, runs about 10,000 times faster than Bilby, and cuts Bilby's runtime by half when its estimates guide the priors.