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1. Transient glitch mitigation in Advanced LIGO data

   https://doi.org/10.1103/PhysRevD.104.102004  

   Merritt, J. D.; Farr, Ben; Hur, Rachel; Edelman, Bruce; Doctor, Zoheyr (November 2021, Physical Review D)
2. Glitch subtraction with adaptive spline -- Part 1

   https://doi.org/10.5281/zenodo.7592442  

   Mohanty, Soumya D (January 2023, Zenodo)

   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. 

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3. Detection of anomalies amongst LIGO’s glitch populations with autoencoders

   https://doi.org/10.1088/1361-6382/ad1f26  

   Laguarta, Paloma; van_der_Laag, Robin; Lopez, Melissa; Dooney, Tom; Miller, Andrew L; Schmidt, Stefano; Cavaglia, Marco; Caudill, Sarah; Driessens, Kurt; Karel, Joël; et al (February 2024, Classical and Quantum Gravity)

   Abstract Gravitational wave (GW) interferometers are able to detect a change in distance of ~1/10 000th the size of a proton. Such sensitivity leads to large rates of non-gaussian, transient bursts of noise, also known as glitches, which hinder the detection and parameter estimation of short- and long-lived GW signals in the main detector strain. Glitches, come in a wide range of frequency-amplitude-time morphologies and may be caused by environmental or instrumental processes, so a key step towards their mitigation is to understand their population. Current approaches for their identification use supervised models to learn their morphology in the main strain with a fixed set of classes, but do not consider relevant information provided by auxiliary channels that monitor the state of the interferometers. In this work, we present an unsupervised algorithm to find anomalous glitches. Firstly, we encode a subset of auxiliary channels from Laser Interferometer Gravitational-Wave Observatory Livingston in the fractal dimension (FD), which measures the complexity of the signal. For this aim, we speed up the fractal dimension calculation to encode $1$h of data in $11$s. Secondly, we learn the underlying distribution of the data using an autoencoder with cyclic periodic convolutions. In this way, we learn the underlying distribution of glitches and we uncover unknown glitch morphologies, and overlaps in time between different glitches and misclassifications. This led to the discovery of $6.6\mathrm{\%}$anomalies in the input data. The results of this investigation stress the learnable structure of auxiliary channels encoded in FD and provide a flexible framework for glitch discovery. 

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4. Antialigned spin of GW191109: Glitch mitigation and its implications

   https://doi.org/10.1103/PhysRevD.111.024046  

   Udall, Rhiannon; Hourihane, Sophie; Miller, Simona; Davis, Derek; Chatziioannou, Katerina; Isi, Max; Deshong, Howard (January 2025, Physical Review D)
5. Glitch subtraction from gravitational wave data using adaptive spline fitting

   Mohanty, Soumya D; Chowdhury, Mohammad A (May 2023, Classical and quantum gravity)