DOI: 10.5281/zenodo.7997424
title: GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run — Data behind the figures

description: This material is part of several data products associated with GWTC-3, the third Gravitational-Wave Transient Catalog from the LIGO Scientific Collaboration, the Virgo Collaboration, and the KAGRA Collaboration. For more information, see the paper (dcc.ligo.org/LIGO-P2000318/public), the related material linked from this page, and the GWTC-3 data release documentation (www.gw-openscience.org/GWTC-3/).



Data behind the figures


This page contains the data behind various paper figures. The material for each figure is contained in a tar file. A short description can be found below. Figures not included here are associated with one of the other GWTC-3 data releases.


 


Figure 1



	
Figure01.tar.gz



Data and script to produce GWTC-3: Figure 1. This shows the number of candidates with probability of astrophysical origin > 50% as a function of surveyed time–volume.


The dates for the first observing run (O1) and second observing run (O2) candidates are hard-coded into the script, and the dates for third observing run (O3a and O3b) candidates are included in two text files. The effective binary neutron star time–volume (BNS-VT) for each observing run is stored in separate .csv files.


Each .csv file contains two columns, the first is the GPS time and the second is the cumulative effective BNS VT in Mpc3 kyr (this is converted to Gpc3 yr in the included script).


The included script reproduces Figure 1 from GWTC-3 using the supplied data.


 


Figure 2



	
Figure02.tar.gz



Data and plotting scripts for GWTC-3: Figure 2. The figure shows sensitivity curves for LIGO Hanford, LIGO Livingston, and Virgo.


The Python script reads the .txt files containing strain data for Hanford, Livingston, and Virgo and saves figures as PDF files.


The sensitivity curves are representative of performance during O3b. Further examples of sensitivity curves across observing runs can be found from the Gravitational Wave Open Science Center.


 


Figure 3



	
Figure03.tar.gz



Data and script to produce GWTC-3: Figure 3. The left panel shows the binary neutron star inspiral range of LIGO Hanford, LIGO Livingston, and Virgo versus time. The right panel shows histograms of the binary neutron star ranges for LIGO Hanford, LIGO Livingston, and Virgo.


The Python script (figure_3.py) reads the range.txt files and the histogram.txt files to produce each panel and saves them as PDF files.


Further summary information about the O3b run can be obtained from the Gravitational Wave open Science Center.


 


Figure 4



	
Figure04.tar.gz



Data and script to produce GWTC-3: Figure 4. This plot shows the rate of non-Gaussian noise transients (glitches) in the LIGO Hanford, LIGO Livingston and Virgo data across O3b. The recorded glitches are identified by the Omicron pipeline with signal-to-noise ratio of > 6.5. There is a reduction in the LIGO glitch rate after the introduction of reaction chain (RC) tracking, which reduced the incidence of scattered light (slow scattering) glitches.


The script glitch_rates_GWTC-3_Fig_4.py produces Figure 4 of GWTC-3 making use of the glitch rates stored in the glitch_rates_GWTC-3_Fig_4.h5 file. Run the script within an igwn-py37 or igwn-py38 Conda environment, paying attention to having the hdf5 file glitch_rates_GWTC-3_Fig_4.h5 in the same directory of the script. Pass the argument -v or --verbose for additional info about the rates.


 


Figure 5



	
Figure05.tar.gz



Script to produce GWTC-3: Figure 5. This figure illustrates the time–frequency structure of two common types of glitch seen in O3: slow scattering and fast scattering. Both are caused by light scattering within the LIGO detectors.


The Python script scattering_GWTC-3_Fig_5.py produces Figure 5 in the GWTC-3 Catalog paper using open data. The script saves the plot as a PDF file namely, scattering_GWTC-3_Fig_5.pdf and the data used to generate the plot in the files data_fast_scattering.txt and data_slow_scattering.txt.


For further examples of the time–frequency structure of glitches, the community-science project Gravity Spy catalogs visualizations of glitches in gravitational-wave data.


 


Figure 12



	
Figure12.tar.gz



Data and script to produce GWTC-3: Figure 12. This plots results of the waveform consistency test (as does Figure 13), plotting the match between waveform templates and minimally modeled reconstructions. The on-source results are for the candidate signals, while the off-source results are for simulated signals with compatible properties.


The waveform reconstructions are performed using BayesWave and cWB. The two pipelines select different sets of candidates to analyze.


The Python script (figure_12.py) reads data from files FittingFactor.txt for Bayeswave and FittingFactor_C01.txt for cWB to produce the corresponding match–match plots (PDF files).


 


Figure 13



	
Figure13.tar.gz



Data and script to produce GWTC-3: Figure 13. This plots results of the waveform consistency test (as does Figure 12), plotting the p-values for the minimally modeled waveform reconstructions. The p-values are plotted in increasing order.


The waveform reconstructions are performed using BayesWave and cWB. The two pipelines select different sets of candidates to analyze.


The script (figure_13.py) reads data from files FittingFactor.txt for Bayeswave and FittingFactor_C01.txt for cWB (the same files used to produce Figure 12) to produce the corresponding p-value plots (PDF files).


 


Figure 14



	
Figure14.tar.gz



Data and script to produce GWTC-3 Figure 14. This figure shows representative noise amplitude spectral densities for LIGO Hanford, LIGO Livingston, and Virgo during Observing Run 2 and Observing Run 3b.


The script (figure_14.py) reads data from the .txt files included in the release to reproduce Figure 14 from GWTC-3 in PDF format.


 


Figure 15



	
Figure15.tar.gz



Script to produce GWTC-3: Figure 15. This shows differences in the data used to analyze GW200115_042309, with and without glitch subtraction. A low frequency cut (illustrated by the dotted white line) was used to remove the glitch in the first analysis of this candidate, whereas glitch subtraction is now used when performing parameter estimation. The curving orange line shows the approximate signal track.


The script reads in the deglitched frame L-L1_HOFT_CLEAN_SUB60HZ_C01_T1700406_v4-1263095808-4096.gwf, downloaded from an associated data release, query raw public LIGO Livingston data, and reproduce Figure 15 from GWTC-3 in PDF format.


 


Figure 16



	
Figure16.tar.gz



Script to produce GWTC-3: Figure 16. This figure illustrates the time–frequency structure of data containing three O3 candidates identified only by cWB (the same as shown in Figure 17). Each shows evidence of instrumental origin. 


The script queries public LIGO data and reproduce Figure 16 from GWTC-3 in PDF format.


 


Figure 17



	
Figure17.tar.gz



Data and script for GWTC-3: Figure 17. This figure illustrates the time–frequency structure of candidate signals as reconstructed by cWB for three O3 candidates identified only by cWB (the same as shown in Figure 16). Each shows evidence of instrumental origin. For a compact binary coalescence signal, we would expect the signal to have a chirp structure, sweeping up from low to high frequencies.


The script figs.py reads data (the reconstruction from cWB) from the .txt files to produce the corresponding time–frequency plots (PDF files). The script must be run three times to produce the panels of Figure 17: the event names are hardcoded into the script, which must be edited to produce the desired panel.


 


How to download all files from this page


If you would like to download all files on this page, we recommend zenodo_get:


pip install zenodo_get
zenodo-get RECORD_ID_OR_DOI



where the record ID for the most recent version of this page is 5571766 and IDs for other versions can be found in the Versions section at the side of this page.


 


For more general background on gravitational-wave data analysis, try the materials from a GW Open Data Workshop or the guide to LIGO–Virgo data analysis.


 
description: LIGO Laboratory and Advanced LIGO are funded by the United States National Science Foundation (NSF) as well as the Science and Technology Facilities Council (STFC) of the United Kingdom, the Max-Planck-Society (MPS), and the State of Niedersachsen/Germany for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the Australian Research Council. Virgo is funded, through the European Gravitational Observatory (EGO), by the French Centre National de Recherche Scientifique (CNRS), the Italian Istituto Nazionale di Fisica Nucleare (INFN) and the Dutch Nikhef, with contributions by institutions from Belgium, Germany, Greece, Hungary, Ireland, Japan, Monaco, Poland, Portugal, Spain. The construction and operation of KAGRA are funded by Ministry of Education, Culture, Sports, Science and Technology (MEXT), and Japan Society for the Promotion of Science (JSPS), National Research Foundation (NRF) and Ministry of Science and ICT (MSIT) in Korea, Academia Sinica (AS) and the Ministry of Science and Technology (MoST) in Taiwan.