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Published 2024 | Version v2

GRITCLEAN Code and Files -- Part 2

Authors/Creators

  • 1. ROR icon The University of Texas Rio Grande Valley

Description

This dataset contains a compressed folder of the data and MATLAB scripts used produce relevant figures and candidates for GRITCLEAN: A glitch veto scheme for Gravitational wave data as presented in https://arxiv.org/abs/2401.15237 

The codes in this dataset include:

  1. A PSO-based matched filtering search pipeline which can be run on either the positive or the negative chirp time space.
  2. A standalone MATLAB script called GRITCLEAN.m which can run the GRITCLEAN hierarchical vetoes on a set of positive and negative chirp time space estimated parameters. 
  3. A plotting script to generate relevant figures.

The files in this dataset include:

  1. GVSsegPSDtrainidxs.mat, a binary MATLAB file containing training indices for all segments from which the Power Spectral Densities (PSDs) are estimated, this is done via the scripts provided, namely, getsegPSD.m and createPSD.m.
  2. A sample HDF5 file used (H-H1_GWOSC_O3a_4KHZ_R1-1243394048-4096.hdf5)
  3. JSON files containing information about the data segments and the strain data files from which they originate from. 
  4. Text files containing the parameters estimated by the PSO-based pipeline across the positive and negative chirp time space runs. 

Detailed instructions on dependencies, downloading the dataset and running the codes are given in a README.txt file included with this dataset. The user is recommended to go through this file first.

The scripts enclosed have dependencies on JSONLAB , the Parallel Computing Toolbox and Signal Processing Toolbox for MATLAB, along with additional scripts provided in GitHub repositories  Accelerated-Network-Analysis  and SDMBIGDAT19 . Instructions on installing these dependencies are provided in README.txt.

 

All codes have been developed and tested on MATLAB R2022 and R2023.

Files

GRITCLEAN_codes_v2.zip

Files (344.4 kB)

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Additional details

Funding

U.S. National Science Foundation
PHY: Accelerated Always-On Fully-Coherent Network Analysis for Gravitational Wave Searches 2207935