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/

Global: Zendy Plus annual

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

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Deep learning can be used to drastically decrease the processing time ofparameter estimation for coalescing binaries of compact objects including blackholes and neutron stars detected in gravitational waves (GWs). As a first step,we present two neural network models trained to rapidly estimate the posteriordistributions of the chirp mass and mass ratio of a detected binary black holesystem from the GW strain data of LIGO Hanford and Livingston Observatories.Using these parameters the component masses can be predicted, which hasimplications for the prediction of the likelihood that a merger contains aneutron star. The results are compared to the 'gold standard' of parameterestimation of gravitational waves used by the LIGO-Virgo Collaboration (LVC),LALInference. Our models predict posterior distributions consistent with thatfrom LALInference while using orders of magnitude less processing time once themodels are trained. The median predictions are within the 90% credibleintervals of LALInference for all predicted parameters when tested on realbinary black hole events detected during the LVC's first and second observingruns. We argue that deep learning has strong potential for low-latencyhigh-accuracy parameter estimation suitable for real-time GW search pipelines.

Rapid Mass Parameter Estimation of Binary Black Hole Coalescences Using Deep Learning