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The classification of frequently occurring terrestrial-origin transient signals, called glitches, in the time series data from gravitational wave detectors is important for mitigating their adverse effects on searches for rare and valuable astrophysical signals. While formally a time series classification problem, recent successes in glitch classification have all come from using their time-frequency image representations. Using transfer learning with the VGG16 deep convolutional neural network for image classification, we compare the efficacy of different types of image representations for classifying simulated glitches. We find the novel result that training the network with 2D plots of the noisy glitch time series provides better classification accuracy than their time-frequency images. 

This dataset contains the data used in the paper (arXiv:2301.02398) on the estimation and subtraction of glitches in gravitational wave data using an adaptive spline fitting method called SHAPES . Each .zip file corresponds to one of the glitches considered in the paper. The name of the class to which the glitch belongs (e.g., "Blip") is included in the name of the corresponding .zip file (e.g., BLIP_SHAPESRun_20221229T125928.zip). When uncompressed, each .zip file expands to a folder containing the following. An HDF5 file containing the Whitened gravitational wave (GW) strain data in which the glitch appeared. The data has been whitened using a proprietary code. The original (unwhitened) strain data file is available from gwosc.org. The name of the original data file is the part preceding the token '__dtrndWhtnBndpss' in the name of the file.A JSON file containing information pertinent to the glitch that was analyzed (e.g., start and stop indices in the whitened data time series).A set of .mat  files containing segmented estimates of the glitch as described in the paper.  A MATLAB script, plotglitch.m, has been provided that plots, for a given glitch folder name, the data segment that was analyzed in the paper. Another script, plotshapesestimate.m, plots the estimated glitch. These scripts require the JSONLab package. 

Abstract Transient signals of instrumental and environmental origins (‘glitches’) in gravitational wave data elevate the false alarm rate of searches for astrophysical signals and reduce their sensitivity. Glitches that directly overlap astrophysical signals hinder their detection and worsen parameter estimation errors. As the fraction of data occupied by detectable astrophysical signals will be higher in next generation detectors, such problematic overlaps could become more frequent. These adverse effects of glitches can be mitigated by estimating and subtracting them out from the data, but their unpredictable waveforms and large morphological diversity pose a challenge. Subtraction of glitches using data from auxiliary sensors as predictors works but not for the majority of cases. Thus, there is a need for nonparametric glitch mitigation methods that do not require auxiliary data, work for a large variety of glitches, and have minimal effect on astrophysical signals in the case of overlaps. In order to cope with the high rate of glitches, it is also desirable that such methods be computationally fast. We show that adaptive spline fitting, in which the placement of free knots is optimized to estimate both smooth and non-smooth curves in noisy data, offers a promising approach to satisfying these requirements for broadband short-duration glitches, the type that appear quite frequently. The method is demonstrated on glitches drawn from three distinct classes in the Gravity Spy database as well as on the glitch that overlapped the binary neutron star signal GW170817. The impact of glitch subtraction on the GW170817 signal, or those like it injected into the data, is seen to be negligible.