Applying Machine Learning for Gravitational-wave Burst Data Analysis
The direct detection of gravitational waves is enabled by both instrumental technology and massive data analysis. Data glitches can easily be mistaken for gravitational-wave signals, and their robust identification and removal will help any search for gravitational waves. We apply machine-learning algorithms (MLAs) to the problem, using data from auxiliary channels within the LIGO detectors that monitor degrees of freedom unaffected by astrophysical signals. Noise sources may produce artifacts in these auxiliary channels as well as the gravitational-wave channel. The number of auxiliary channel parameters describing these disturbances may also be extremely large; high dimensionality is an area where MLAs are particularly well suited. We demonstrate the feasibility and applicability of three different MLAs: artificial neural networks, support vector machines, and random forests, for veto analysis of gravitational wave bursts.
Time: June 29, 2016, 2:30 pm
Venue: A601, NAOC