Data Clustering Techniques for the Correlation of Environmental Noise to LIGO Detector Sensitivity

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Abstract:: LIGO is sensitive to environmental vibrations which can couple in via scattered light in the interferometer. This noise limits the sensitivity to the early part of the inspiral as well as to the hypothetical population of black holes of greater than 100 solar masses. The detector complexity makes it such that it is a time intensive task to properly study the behavior of the circa 100 sensors monitoring the ambient noise. Sensors include accelerometers, microphones, magnetometers, seismometers, etc. As part of this project, we will implement an un-supervised learning algorythm (via Keras/Tensorflow) which would find correlations between LIGO noise in certain frequency bands and these sensors. This would help identify areas of the detector which need improvement with the end goal of increasing the LIGO sensitivity.

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LIGO Document T1900287-v1