  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, we
proposed the use of deep convolutional neural networks for the detection and
characterization of gravitational wave signals in real-time. This method, Deep
Filtering, was initially demonstrated using simulated LIGO noise. In this
article, we present the extension of Deep Filtering using real data 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 sensitivity and lower
errors, 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 approach is uniquely suited to enable
coincident detection campaigns of gravitational waves and their multimessenger
counterparts in real-time.
