Improving significance of binary black hole mergers in Advanced LIGO data using deep learning : Confirmation of GW151216

Understand

We present a novel Machine Learning (ML) based strategy to search for binary black hole (BBH) mergers in data from ground-based gravitational wave (GW) observatories.

• This is the first ML-based search that not only recovers all the compact binary coalescences (CBCs) in the first GW transients catalog (GWTC-1), but also makes a clean detection of GW151216 by only adding a new coincident ranking statistic (MLStat) to a standard analysis that was used for GWTC-1.
• In CBC searches, reducing contamination by terrestrial and instrumental transients, which create a loud noise background by triggering numerous false alarms, is crucial to improving the sensitivity for detecting true events.
• The sheer volume of data and a large number of expected detections also prompts the use of ML techniques.