16887

Deep Neural Networks to Enable Real-time Multimessenger Astrophysics

Daniel George, E. A. Huerta
Department of Astronomy, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
arXiv:1701.00008 [astro-ph.IM], (30 Dec 2016)

@article{george2016deep,

   title={Deep Neural Networks to Enable Real-time Multimessenger Astrophysics},

   author={George, Daniel and Huerta, E. A.},

   year={2016},

   month={dec},

   archivePrefix={"arXiv"},

   primaryClass={astro-ph.IM}

}

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We introduce a new methodology for time-domain signal processing, based on deep learning neural networks, which has the potential to revolutionize data analysis in science. To illustrate how this enables real-time multimessenger astrophysics, we designed two deep convolutional neural networks that can analyze time-series data from observatories including advanced LIGO. The first neural network recognizes the presence of gravitational waves from binary black hole mergers, and the second one estimates the mass of each black hole, given weak signals hidden in extremely noisy time-series inputs. We highlight the advantages offered by this novel method, which outperforms matched-filtering or conventional machine learning techniques, and propose strategies to extend our implementation for simultaneously targeting different classes of gravitational wave sources while ignoring anomalous noise transients. Our results strongly indicate that deep neural networks are highly efficient and versatile tools for directly processing raw noisy data streams. Furthermore, we pioneer a new paradigm to accelerate scientific discovery by combining high-performance simulations on traditional supercomputers and artificial intelligence algorithms that exploit innovative hardware architectures such as deep-learning-optimized GPUs. This unique approach immediately provides a natural framework to unify multi-spectrum observations in real-time, thus enabling coincident detection campaigns of gravitational waves sources and their electromagnetic counterparts.
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