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Scattered light noise affects the sensitivity of gravitational wavesdetectors. The characterization of such noise is needed to mitigate it. Thetime-varying filter empirical mode decomposition algorithm is suitable foridentifying signals with time-dependent frequency such as scattered light noise(or scattering). We present a fully automated pipeline based on the pytvfemdlibrary, a python implementation of the tvf-EMD algorithm, to identify objectsinducing scattering in the gravitational-wave channel with their motion. Thepipeline application to LIGO Livingston O3 data shows that most scatteringnoise is due to the penultimate mass at the end of the X-arm of the detector(EXPUM) and with a motion in the micro-seismic frequency range.

Gwadaptive_scattering: an automated pipeline for scattered light noise characterization