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The data taken by the advanced LIGO and Virgo gravitational-wave detectorscontains short duration noise transients that limit the significance ofastrophysical detections and reduce the duty cycle of the instruments. As theadvanced detectors are reaching sensitivity levels that allow for multipledetections of astrophysical gravitational-wave sources it is crucial to achievea fast and accurate characterization of non-astrophysical transient noiseshortly after it occurs in the detectors. Previously we presented three methodsfor the classification of transient noise sources. They are Principal ComponentAnalysis for Transients (PCAT), Principal Component LALInference Burst (PC-LIB)and Wavelet Detection Filter with Machine Learning (WDF-ML). In this study wecarry out the first performance tests of these algorithms on gravitational-wavedata from the Advanced LIGO detectors. We use the data taken between the 3rd ofJune 2015 and the 14th of June 2015 during the 7th engineering run (ER7), andoutline the improvements made to increase the performance and lower the latencyof the algorithms on real data. This work provides an important test forunderstanding the performance of these methods on real, non stationary data inpreparation for the second advanced gravitational-wave detector observationrun, planned for later this year. We show that all methods can classifytransients in non stationary data with a high level of accuracy and show thebenefits of using multiple classifiers.

Classification methods for noise transients in advanced gravitational-wave detectors II: performance tests on Advanced LIGO data