English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

The recent Nobel-prize-winning detections of gravitational waves from mergingblack holes and the subsequent detection of the collision of two neutron starsin coincidence with electromagnetic observations have inaugurated a new era ofmultimessenger astrophysics. To enhance the scope of this emergent field ofscience, we pioneered the use of deep learning with convolutional neuralnetworks, that take time-series inputs, for rapid detection andcharacterization of gravitational wave signals. This approach, Deep Filtering,was initially demonstrated using simulated LIGO noise. In this article, wepresent the extension of Deep Filtering using real data from LIGO, for bothdetection and parameter estimation of gravitational waves from binary blackhole mergers using continuous data streams from multiple LIGO detectors. Wedemonstrate for the first time that machine learning can detect and estimatethe true parameters of real events observed by LIGO. Our results show that DeepFiltering achieves similar sensitivities and lower errors compared tomatched-filtering while being far more computationally efficient and moreresilient to glitches, allowing real-time processing of weak time-seriessignals in non-stationary non-Gaussian noise with minimal resources, and alsoenables the detection of new classes of gravitational wave sources that may gounnoticed with existing detection algorithms. This unified framework for dataanalysis is ideally suited to enable coincident detection campaigns ofgravitational waves and their multimessenger counterparts in real-time.

Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data