DOI: 10.5281/zenodo.16857059
title: GWTC-4.0: Glitch modelling for events

description: This material is part of the data release associated with version 4.0 of the Gravitational-Wave Transient Catalog (GWTC-4.0), from the LIGO Scientific Collaboration, the Virgo Collaboration, and the KAGRA Collaboration. For more information, see the papers describing the catalog (introduction, methods, results), the related material linked from this page, and the GWTC-4.0 data release documentation (www.gw-openscience.org/GWTC-4.0/).

Glitch model for GWTC-4.0 events

This record provides glitch models for events in the GWTC-4.0 catalog that used BayesWave glitch subtraction.

Each data file for events processed with BayesWave contains three channels:



The calibrated strain data, including any glitches that are present,

A model of the glitches, produced using the BayesWave algorithm,

The calibrated data with the glitch model subtracted, used for parameter estimation.


Below, we provide a table detailing the event, detector, filename, and channels within the frame file.




Event
Detector
Filename
Channels




GW230606_004305
H
H-H1_HOFT_C00_BAYESWAVE_S00-1370046464-4096.gwf


H1:GDS-CALIB_STRAIN_CLEANH1:GDS-CALIB_STRAIN_CLEAN_glitchH1:GDS-CALIB_STRAIN_CLEAN_BAYESWAVE_S00



GW230707_124047
H
H-H1_HOFT_C00_BAYESWAVE_S00-1372766208-4096.gwf
H1:GDS-CALIB_STRAIN_CLEANH1:GDS-CALIB_STRAIN_CLEAN_glitchH1:GDS-CALIB_STRAIN_CLEAN_BAYESWAVE_S00


GW230708_053705
H
H-H1_HOFT_C00_BAYESWAVE_S00-1372827648-4096.gwf
H1:GDS-CALIB_STRAIN_CLEANH1:GDS-CALIB_STRAIN_CLEAN_glitchH1:GDS-CALIB_STRAIN_CLEAN_BAYESWAVE_S00


GW230806_204041
H
H-H1_HOFT_C00_AR_BAYESWAVE_S00-1375387648-4096.gwf
H1:GDS-CALIB_STRAIN_CLEAN_ARH1:GDS-CALIB_STRAIN_CLEAN_AR_glitchH1:GDS-CALIB_STRAIN_CLEAN_AR_BAYESWAVE_S00


GW230819_171910
L
L-L1_HOFT_C00_BAYESWAVE_S00-1376497664-4096.gwf
L1:GDS-CALIB_STRAIN_CLEANL1:GDS-CALIB_STRAIN_CLEAN_glitchL1:GDS-CALIB_STRAIN_CLEAN_BAYESWAVE_S00


GW231113_122623
L
L-L1_HOFT_C00_BAYESWAVE_S00-1383911424-4096.gwf
L1:GDS-CALIB_STRAIN_CLEANL1:GDS-CALIB_STRAIN_CLEAN_glitchL1:GDS-CALIB_STRAIN_CLEAN_BAYESWAVE_S00


GW231114_043211
H
H-H1_HOFT_C00_BAYESWAVE_S00-1383968768-4096.gwf
H1:GDS-CALIB_STRAIN_CLEANH1:GDS-CALIB_STRAIN_CLEAN_glitchH1:GDS-CALIB_STRAIN_CLEAN_BAYESWAVE_S00


GW231118_090602
H
H-H1_HOFT_C00_BAYESWAVE_S00-1384333312-4096.gwf
H1:GDS-CALIB_STRAIN_CLEANH1:GDS-CALIB_STRAIN_CLEAN_glitchH1:GDS-CALIB_STRAIN_CLEAN_BAYESWAVE_S00


GW231123_135430
H
H-H1_HOFT_C00_BAYESWAVE_S00-1384779776-4096.gwf
H1:GDS-CALIB_STRAIN_CLEANH1:GDS-CALIB_STRAIN_CLEAN_glitchH1:GDS-CALIB_STRAIN_CLEAN_BAYESWAVE_S00


GW231129_081745
L
L-L1_HOFT_C00_BAYESWAVE_S00-1385279488-4096.gwf
L1:GDS-CALIB_STRAIN_CLEANL1:GDS-CALIB_STRAIN_CLEAN_glitchL1:GDS-CALIB_STRAIN_CLEAN_BAYESWAVE_S00


GW231221_135041
H
H-H1_HOFT_C00_AR_BAYESWAVE_S00-1387200512-4096.gwf
H1:GDS-CALIB_STRAIN_CLEAN_ARH1:GDS-CALIB_STRAIN_CLEAN_AR_glitchH1:GDS-CALIB_STRAIN_CLEAN_AR_BAYESWAVE_S00


GW231223_032836
H
H-H1_HOFT_C00_BAYESWAVE_S00-1387335680-4096.gwf
H1:GDS-CALIB_STRAIN_CLEANH1:GDS-CALIB_STRAIN_CLEAN_glitchH1:GDS-CALIB_STRAIN_CLEAN_BAYESWAVE_S00




Accessing the files in bulk

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

Further information

For more general background on gravitational-wave data quality, try the materials from a GW Open Data Workshop or the guide to LIGO–Virgo data analysis.
description: This material is based upon work supported by NSF's LIGO Laboratory, which is a major facility fully funded by the National Science Foundation. The authors also gratefully acknowledge the support of 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 GEO 600 detector. Additional support for Advanced LIGO was provided by the Australian Research Council.The authors gratefully acknowledge the Italian Istituto Nazionale di Fisica Nucleare (INFN),  the French Centre National de la Recherche Scientifique (CNRS) and the Netherlands Organization for Scientific Research (NWO)for the construction and operation of the Virgo detector and the creation and support of the EGO consortium. The authors also gratefully acknowledge research support from these agencies as well as by the Council of Scientific and Industrial Research of India, the Department of Science and Technology, India, the Science & Engineering Research Board (SERB), India, the Ministry of Human Resource Development, India,the Spanish Agencia Estatal de Investigación (AEI),the Spanish Ministerio de Ciencia, Innovación y Universidades, the European Union NextGenerationEU/PRTR (PRTR-C17.I1), the ICSC - CentroNazionale di Ricerca in High Performance Computing, Big Data and Quantum Computing, funded by the European Union NextGenerationEU, the Comunitat Autònoma de les Illes Balears through the Conselleria d'Educació i Universitats, the Conselleria d'Innovació, Universitats, Ciència i Societat Digital de la Generalitat Valenciana and the CERCA Programme Generalitat de Catalunya, Spain, the Polish National Agency for Academic Exchange, the National Science Centre of Poland and the European Union - European Regional Development Fund; the Foundation for Polish Science (FNP), the Polish Ministry of Science and Higher Education, the Swiss National Science Foundation (SNSF), the Russian Science Foundation, the European Commission, the European Social Funds (ESF), the European Regional Development Funds (ERDF), the Royal Society, the Scottish Funding Council, the Scottish Universities Physics Alliance, the Hungarian Scientific Research Fund (OTKA), the French Lyon Institute of Origins (LIO), the Belgian Fonds de la Recherche Scientifique (FRS-FNRS), Actions de Recherche Concertées (ARC) and Fonds Wetenschappelijk Onderzoek - Vlaanderen (FWO), Belgium, the Paris Île-de-France Region, the National Research, Development and Innovation Office of Hungary (NKFIH), the National Research Foundation of Korea, the Natural Sciences and Engineering Research Council of Canada (NSERC), the Canadian Foundation for Innovation (CFI), the Brazilian Ministry of Science, Technology, and Innovations, the International Center for Theoretical Physics South American Institute for Fundamental Research (ICTP-SAIFR), the Research Grants Council of Hong Kong, the National Natural Science Foundation of China (NSFC), the Israel Science Foundation (ISF), the US-Israel Binational Science Fund (BSF), the Leverhulme Trust, the Research Corporation, the National Science and Technology Council (NSTC), Taiwan, the United States Department of Energy, and the Kavli Foundation. The authors gratefully acknowledge the support of the NSF, STFC, INFN and CNRS for provision of computational resources. This work was supported by MEXT, the JSPS Leading-edge Research Infrastructure Program, JSPS Grant-in-Aid for Specially Promoted Research 26000005, JSPS Grant-in-Aid for Scientific Research on Innovative Areas 2402: 24103006, 24103005, and 2905: JP17H06358, JP17H06361 and JP17H06364, JSPS Core-to-Core Program A. Advanced Research Networks, JSPS Grants-in-Aid for Scientific Research (S) 17H06133 and 20H05639, JSPS Grant-in-Aid for Transformative Research Areas (A) 20A203: JP20H05854, the joint research program of the Institute for Cosmic Ray Research, University of Tokyo, the National Research Foundation (NRF), the Computing Infrastructure Project of the Global Science experimental Data hub Center (GSDC) at KISTI, the Korea Astronomy and Space Science Institute (KASI), the Ministry of Science and ICT (MSIT) in Korea, Academia Sinica (AS), the AS Grid Center (ASGC) and the National Science and Technology Council (NSTC) in Taiwan under grants including the Science Vanguard Research Program, the Advanced Technology Center (ATC) of NAOJ, and the Mechanical Engineering Center of KEK.