Published February 2, 2023 | Version v1
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Test sets and results for paper 'Rapid localization of gravitational wave sources from compact binary coalescences using deep learning'

  • 1. The University of Western Australia
  • 2. The Commonwealth Scientific and Industrial Research Organization
  • 3. International Centre for Radio Astronomy Research

Description

This folder contains the input data (signal-to-noise ratio time series for gravitational wave detections) and results (sky localization areas) obtained from the deep learning based sky localization model 'GW-SkyLocator', and the rapid online gravitational wave sky localization tool 'BAYESTAR' on a set of injections of gravitational wave signals from compact binary mergers.

For details of the work, please refer to the paper, 'Rapid localization of gravitational wave sources from compact binary coalescences using deep learning' (https://arxiv.org/abs/2207.14522).

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