Improving the background of gravitational-wave searches for core collapse supernovae: A machine learning approach

Understand

Based on the prior O1-O2 observing runs, about 30% of the data collected by Advanced LIGO and Virgo in the next observing runs are expected to be single-interferometer data, i.e., they will be collected at times when only one detector in the network is operating in observing mode.

• Searches for gravitational wave signals from supernova events do not rely on matched filtering techniques because of the stochastic nature of the signals.
• If a Galactic supernova occurs during single-interferometer times, separation of its unmodelled gravitational-wave signal from noise will be even more difficult due to lack of coherence between detectors.
• We present a novel machine learning method to perform single-interferometer supernova searches based on the standard LIGO-Virgo coherentWave-Burst pipeline.