Data mining and machine learning improve gravitational-wave detector sensitivity

Application of data mining and machine learning techniques can significantly improve the sensitivity of current interferometric gravitational-wave detectors. Such instruments are complex multi-input single output systems, with close-to-linear dynamics and hundreds of active feedback control loops. We show how the application of brute-force data-mining techniques allows us to discover correlations between auxiliary monitoring channels and the main gravitational-wave output channel. We also discuss the result of the application of a parametric and time-domain noise subtraction algorithm, that allows a significant improvement of the detector sensitivity at frequencies below 30 Hz.