Speaker
Description
In 2015, the first gravitational wave signals from colliding binary black holes were detected. Subsequent detections of gravitational waves lead researchers to observe a new population of massive, stellar-origin black holes. These signals are tiny ripples of the fabric of space-time. Even though the global network of gravitational-wave detectors is one of the most sensitive instruments on the planet, the signals are buried in detector noise. Analysis of gravitational-waves data and the detection of these signals is a crucial mission for the gravitational-waves community. In this project, I used a machine learning technique to analyse simulated gravitational-wave time-series data from a network of ground-based detectors. More precisely, time-series data from three detectors were converted into spectrograms using a constant Q-transform and combined into a single RGB figure. Subsequently, I used transfer learning to train a state-of-the-art convolutional neural network to detect gravitational wave signals from the merger of binary black holes.
