Enhancing transient gravitational wave analyses with machine learning

PIRSA ID: 21050017
Series: Strong Gravity
Event Type: Seminar
Scientific Area(s):
Strong Gravity
End date:
  • Ik Siong Heng, University of Glasgow

Gravitational wave observations are beginning to reveal the nature of the dark side of our universe. The Advanced LIGO and Virgo detectors have observed dozens of binary black hole mergers during the recent third observing run and, with planned sensitivity improvements, expect to observe significantly more binary black hole mergers in future observing runs. The combination of the increased number of detections  and the sheer volume of data associated with each detection provides a significant data analysis challenge. In recent years, various machine learning approaches such as convolutional neural networks have been explored as a basis for rapid analyses for gravitational wave data. This seminar will give a brief introduction to current transient gravitational wave data analysis methodology and highlight novel applications of machine learning for rapid detection of binary black holes and rapid inference of their astrophysical properties. The use of generative machine learning algorithms for transient gravitational wave signal generation will also be discussed.