Finding the Origin of Noise Transients in LIGO Data with Machine Learning (Communications in Computational Physics, 2018)
Quality improvement of interferometric data collected by gravitational-wave detectors such as Advanced LIGO and Virgo is mission critical for the successof gravitational-wave astrophysics. Gravitational-wave detectors are sensitive to avariety of disturbances of non-astrophysical origin with characteristic frequencies inthe instrument band of sensitivity. Removing non-astrophysical artifacts that corrupt the data stream is crucial for increasing the number and statistical significanceof gravitational-wave detections and enabling refined astrophysical interpretations ofthe data. Machine learning has proved to be a powerful tool for analysis of massivequantities of complex data in astronomy and related fields of study. We present twomachine learning methods, based on random forest and genetic programming algorithms, that can be used to determine the origin of non-astrophysical transients in theLIGO detectors. We use two classes of transients with known instrumental origin thatwere identified during the first observing run of Advanced LIGO to show that thealgorithms can successfully identify the origin of non-astrophysical transients in realinterferometric data and thus assist in the mitigation of instrumental and environmental disturbances in gravitational-wave searches. While the datasets described in thispaper are specific to LIGO, and the exact procedures employed were unique to thesame, the random forest and genetic programming code bases and means by whichthey were applied as a dual machine learning approach are completely portable to anynumber of instruments in which noise is believed to be generated through mechanicalcouplings, the source of which is not yet discovered.
Keywords: Machine learning; gravitational waves; noise mitigation.