Abstract
Noise of non-astrophysical origin will contaminate science data taken by the advanced laser interferometer gravitational-wave observatory and advanced Virgo gravitational-wave detectors. Prompt characterization of instrumental and environmental noise transients will be critical for improving the sensitivity of the advanced detectors in the upcoming science runs. During the science runs of the initial gravitational-wave detectors, noise transients were manually classified by visually examining the time–frequency scan of each event. Here, we present three new algorithms designed for the automatic classification of noise transients in advanced detectors. Two of these algorithms are based on principal component analysis. They are principal component analysis for transients and an adaptation of LALInference burst. The third algorithm is a combination of an event generator called wavelet detection filter and machine learning techniques for classification. We test these algorithms on simulated data sets, and we show their ability to automatically classify transients by frequency, signal to noise ratio and waveform morphology.
Highlights
The sensitivity of advanced gravitational-wave detectors will be limited by multiple sources of noise from the hardware subsystems and the environment
If the number of glitch types requested by principal component analysis for transients (PCAT) is higher than the actual number of glitch types in the data set, the waveforms will be classified by waveform morphology first, and split into further sub-types by frequency and signal to noise ratio (SNR)
As LALInference burst (LIB) needs a set of principal components (PCs) in advance to create a signal model, it is only possible for LIB to classify known types of transients in the data
Summary
The sensitivity of advanced gravitational-wave detectors will be limited by multiple sources of noise from the hardware subsystems and the environment. The advanced laser interferometer gravitational-wave observatory (aLIGO) detectors [1, 2], which are expected to become fully operational in late summer 2015, and advanced Virgo [3], which is expected to become fully operational in 2016, will include upgrades to all hardware subsystems including suspensions, lasers, seismic isolation and optics. The upgrades are designed to reduce noise sources and significantly improve the sensitivity of the initial LIGO and Virgo instruments. Thermal noise due to Brownian motion will be the most dominant noise source in the most sensitive frequency range of the instruments. Understanding detector noise, which may affect the discovery of gravitational waves, will be critical for increasing the chances of detecting an astrophysical gravitational-wave signal
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