GWSkyNet: a real-time classifier for public gravitational-wave candidates

Abstract

The rapid release of accurate sky localization for gravitational-wave candidates is crucial for multi-messenger observations. During the third observing run of Advanced LIGO and Advanced Virgo, automated gravitational-wave alerts were publicly released within minutes of detection. Subsequent inspection and analysis resulted in the eventual retraction of a fraction of the candidates. Updates could be delayed by up to several days, sometimes issued during or after exhaustive multi-messenger followup campaigns. We introduce GWSkyNet, a real-time framework to distinguish between astrophysical events and instrumental artefacts using only publicly available information from the LIGO-Virgo open public alerts. This framework consists of a non-sequential convolutional neural network involving sky maps and metadata. GWSkyNet achieves a prediction accuracy of 93.5% on a testing data set.

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• Gravity Spy: Lessons Learned and a Path Forward
• Applications of machine learning in gravitational wave research with current interferometric detectors
• Utilizing the null stream of the Einstein Telescope
• Analyzing the Galactic pulsar distribution with machine learning
• Rapid identification of time-frequency domain gravitational wave signals from binary black holes using deep learning
• Robust inference of gravitational wave source parameters in the presence of noise transients using normalizing flows
• Search for exotic gravitational wave signals beyond general relativity using deep learning
• Using t-SNE for characterizing glitches in LIGO detectors
• Astrophysical or Terrestrial: Machine learning classification of gravitational-wave candidates using multiple-search information
• Conditional variational autoencoders for cosmological model discrimination and anomaly detection in cosmic microwave background power spectra