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 Open

Presents the pdf analysis as premium feature

PDF Analysis

Insights

Presents the summarisation as premium feature

Summarisation

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

One of the key challenges of real-time detection and parameter estimation ofgravitational waves from compact binary mergers is the computational cost ofconventional matched-filtering and Bayesian inference approaches. Inparticular, the application of these methods to the full signal parameter spaceavailable to the gravitational-wave detectors, and/or real-time parameterestimation is computationally prohibitive. On the other hand, rapid detectionand inference are critical for prompt follow-up of the electromagnetic andastro-particle counterparts accompanying important transients, such as binaryneutron-star and black-hole neutron-star mergers. Training deep neural networksto identify specific signals and learn a computationally efficientrepresentation of the mapping between gravitational-wave signals and theirparameters allows both detection and inference to be done quickly and reliably,with high sensitivity and accuracy. In this work we apply a deep-learningapproach to rapidly identify and characterize transient gravitational-wavesignals from binary neutron-star mergers in real LIGO data. We show for thefirst time that artificial neural networks can promptly detect and characterizebinary neutron star gravitational-wave signals in real LIGO data, anddistinguish them from noise and signals from coalescing black-hole binaries. Weillustrate this key result by demonstrating that our deep-learning frameworkclassifies correctly all gravitational-wave events from the Gravitational-WaveTransient Catalog, GWTC-1 [Phys. Rev. X 9 (2019), 031040]. These resultsemphasize the importance of using realistic gravitational-wave detector data inmachine learning approaches, and represent a step towards achieving real-timedetection and inference of gravitational waves.

Detection and Parameter Estimation of Gravitational Waves from Binary Neutron-Star Mergers in Real LIGO Data using Deep Learning