A low-latency Glitch Classification Algorithm Based in Waveform Morphology for Advanced LIGO
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Through a fellowship funded by the National Science Foundation (NSF) Mr. Gabbard applied programming and mathematical skills for the analysis of gravitational wave data. Mr. Gabbard presented a novel and efficient machine learning algorithm for classification of signals that arise in gravitational wave channels of the Laser Interferometer Gravitational Wave Observatory (LIGO). Using data from LIGO’s sixth science run (S6), a new glitch classification algorithm based mainly on the morphology of the waveform as well as several other parameters (signal-to-noise ratio (SNR), duration, bandwidth, etc.) was developed.This was done using two novel methods, Kohonen Self Organizing Feature Maps (SOM), and discrete wavelet transform coefficients. The study showed the feasibility of utilizing SOMs to display a multidimensional trigger set in a low-latency two dimensional format.