ROMAN: Reduced-Order Modeling with Artificial Neurons. (arXiv:1811.05491v1 [astro-ph.IM])
<a href="http://arxiv.org/find/astro-ph/1/au:+Chua_A/0/1/0/all/0/1">Alvin J. K. Chua</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Galley_C/0/1/0/all/0/1">Chad R. Galley</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Vallisneri_M/0/1/0/all/0/1">Michele Vallisneri</a>
Gravitational-wave data analysis is rapidly absorbing techniques from deep
learning, with a focus on convolutional networks and related methods that treat
noisy time series as images. We pursue an alternative approach, in which
waveforms are first represented as weighted sums over reduced bases
(reduced-order modeling); we then train artificial neural networks to map
gravitational-wave source parameters into basis coefficients. Statistical
inference proceeds directly in coefficient space, where it is theoretically
straightforward and computationally efficient. The neural networks also provide
analytic waveform derivatives, which are useful for gradient-based sampling
schemes. We demonstrate fast and accurate coefficient interpolation for the
case of a four-dimensional binary-inspiral waveform family, and discuss
promising applications of our framework in parameter estimation.
Gravitational-wave data analysis is rapidly absorbing techniques from deep
learning, with a focus on convolutional networks and related methods that treat
noisy time series as images. We pursue an alternative approach, in which
waveforms are first represented as weighted sums over reduced bases
(reduced-order modeling); we then train artificial neural networks to map
gravitational-wave source parameters into basis coefficients. Statistical
inference proceeds directly in coefficient space, where it is theoretically
straightforward and computationally efficient. The neural networks also provide
analytic waveform derivatives, which are useful for gradient-based sampling
schemes. We demonstrate fast and accurate coefficient interpolation for the
case of a four-dimensional binary-inspiral waveform family, and discuss
promising applications of our framework in parameter estimation.
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