http://arxiv.org/abs/1711.07966
The recent Nobel-prize-winning detections of gravitational waves from merging black holes and the subsequent detection of the collision of two neutron stars in coincidence with electromagnetic observations have inaugurated a new era of multimessenger astrophysics. To enhance the scope of this emergent science, the use of deep convolutional neural networks were proposed for the detection and characterization of gravitational wave signals in real-time. This approach, Deep Filtering, was initially demonstrated using simulated LIGO noise. In this article, we present the extension of Deep Filtering using real noise from the first observing run of LIGO, for both detection and parameter estimation of gravitational waves from binary black hole mergers with continuous data streams from multiple LIGO detectors. We show for the first time that machine learning can detect and estimate the true parameters of a real GW event observed by LIGO. Our comparisons show that Deep Filtering is far more computationally efficient than matched-filtering, while retaining similar performance, allowing real-time processing of weak time-series signals in non-stationary non-Gaussian noise, with minimal resources, and also enables the detection of new classes of gravitational wave sources that may go unnoticed with existing detection algorithms. This framework is uniquely suited to enable coincident detection campaigns of gravitational waves and their multimessenger counterparts in real-time.
D. George and E. Huerta
Wed, 22 Nov 17
65/67
Comments: Version accepted to NIPS 2017 conference workshop on Deep Learning for Physical Sciences and selected for contributed talk. Also awarded 1st place at ACM SRC at SC17. This is a shorter version of arXiv:1711.03121
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