Gwadaptive_scattering: an automated pipeline for scattered light noise characterization

Abstract

Scattered light noise affects the sensitivity of gravitational waves detectors. The characterization of such noise is needed to mitigate it. The time-varying filter empirical mode decomposition algorithm is suitable for identifying signals with time-dependent frequency such as scattered light noise (or scattering). We present a fully automated pipeline based on the pytvfemd library, a python implementation of the tvf-EMD algorithm, to identify objects inducing scattering in the gravitational-wave channel with their motion. The pipeline application to LIGO Livingston O3 data shows that most scattering noise 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.

14 pages, 8 figures

References in corpus (7)

• Advanced LIGO
• GWTC-2: Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run
• Observation of gravitational waves from two neutron star-black hole coalescences
• Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science
• LIGO Detector Characterization in the Second and Third Observing Runs
• Discovering features in gravitational-wave data through detector characterization, citizen science and machine learning
• Daily monitoring of scattered light noise due to microseismic variability at the Virgo interferometer

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• Detecting and Diagnosing Terrestrial Gravitational-Wave Mimics Through Feature Learning
• Detailed Analysis of Local Climate at the CTAO-North Site on La Palma from 20 Years of MAGIC Weather Station Data
• Architectural Optimization and Feature Learning for High-Dimensional Time Series Datasets