Data quality up to the third observing run of advanced LIGO: Gravity Spy glitch classifications

J. Glanzer; S. Banagiri; S. B. Coughlin; S. Soni; M. Zevin; Christopher Philip Luke Berry; O. Patane; S. Bahaadini; N. Rohani; K. Crowston; V. Kalogera; C. Østerlund; L. Trouille; A. Katsaggelos

doi:10.1088/1361-6382/acb633

Data quality up to the third observing run of advanced LIGO: Gravity Spy glitch classifications

J. Glanzer, S. Banagiri, S. B. Coughlin, S. Soni, M. Zevin, Christopher Philip Luke Berry^*, O. Patane, S. Bahaadini, N. Rohani, K. Crowston, V. Kalogera, C. Østerlund, L. Trouille, A. Katsaggelos

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

26 Scopus citations

Abstract

Understanding the noise in gravitational-wave detectors is central to detecting and interpreting gravitational-wave signals. Glitches are transient, non-Gaussian noise features that can have a range of environmental and instrumental origins. The Gravity Spy project uses a machine-learning algorithm to classify glitches based upon their time-frequency morphology. The resulting set of classified glitches can be used as input to detector-characterisation investigations of how to mitigate glitches, or data-analysis studies of how to ameliorate the impact of glitches. Here we present the results of the Gravity Spy analysis of data up to the end of the third observing run of advanced laser interferometric gravitational-wave observatory (LIGO). We classify 233981 glitches from LIGO Hanford and 379805 glitches from LIGO Livingston into morphological classes. We find that the distribution of glitches differs between the two LIGO sites. This highlights the potential need for studies of data quality to be individually tailored to each gravitational-wave observatory.

Original language	English (US)
Article number	065004
Journal	Classical and Quantum Gravity
Volume	40
Issue number	6
DOIs	https://doi.org/10.1088/1361-6382/acb633
State	Published - Mar 16 2023

Funding

We thank the citizen-science volunteers of Gravity Spy who have contributed to the classifications of LIGO data. We are grateful to Marissa Walker and the anonymous referees for comments on the manuscript. Gravity Spy is partly supported by the National Science Foundation (NSF) Award INSPIRE 1547880 and partially by Award IIS-2107334. This work is supported by the NSF under Grant PHY-1912648. J G is supported by NSF Grant PHY-2110509. S B acknowledges support by NSF Grants PHY-1912648 and IIS-2107334. S S acknowledges support of the NSF Grant PHY-1764464 to the LIGO Laboratory. M Z is supported by NASA through the NASA Hubble Fellowship Grant HST-HF2-51474.001-A awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Incorporated, under NASA contract NAS5-26555. CPLB acknowledges support from the CIERA Board of Visitors Research Professorship, and Science and Technology Facilities Council (STFC) Grant ST/V005634/1. O P is supported by NSF Grant PHY-1559694. V K was partially supported through a CIFAR Senior Fellowship, NSF Grant PHY-1912648, and by Northwestern University. This material is based upon work supported by NSF\u2019s LIGO Laboratory which is a major facility fully funded by the National Science Foundation. This research has made use of data and software obtained from GWOSC ( gw-openscience.org ), a service of LIGO Laboratory, the LIGO Scientific Collaboration, the Virgo Collaboration, and KAGRA. The authors gratefully acknowledge the support of the United States NSF for the construction and operation of the LIGO Laboratory and Advanced LIGO as well as STFC of the United Kingdom, and the Max-Planck-Society for support of the construction of Advanced LIGO. Additional support for Advanced LIGO was provided by the Australian Research Council. Advanced LIGO was built under Award PHY-0823459. LIGO was constructed by the California Institute of Technology and Massachusetts Institute of Technology with funding from the National Science Foundation and operates under Cooperative Agreement PHY-1764464. This work used computing resources at CIERA funded by NSF Grant PHY-1726951, and the computational resources and staff contributions provided for the Quest high performance computing facility at Northwestern University which is jointly supported by the Office of the Provost, the Office for Research, and Northwestern University Information Technology. The authors are grateful for computational resources provided by the LIGO Laboratory and supported by National Science Foundation Grants PHY-0757058 and PHY-0823459. This document has been assigned LIGO document number LIGO-P2200238 . The data that support the findings of this study are openly available from Zenodo []. We thank the citizen-science volunteers of Gravity Spy who have contributed to the classifications of LIGO data. We are grateful to Marissa Walker and the anonymous referees for comments on the manuscript. Gravity Spy is partly supported by the National Science Foundation (NSF) Award INSPIRE 1547880 and partially by Award IIS-2107334. This work is supported by the NSF under Grant PHY-1912648. J G is supported by NSF Grant PHY-2110509. S B acknowledges support by NSF Grants PHY-1912648 and IIS-2107334. S S acknowledges support of the NSF Grant PHY-1764464 to the LIGO Laboratory. M Z is supported by NASA through the NASA Hubble Fellowship Grant HST-HF2-51474.001-A awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Incorporated, under NASA contract NAS5-26555. CPLB acknowledges support from the CIERA Board of Visitors Research Professorship, and Science and Technology Facilities Council (STFC) Grant ST/V005634/1. O P is supported by NSF Grant PHY-1559694. V K was partially supported through a CIFAR Senior Fellowship, NSF Grant PHY-1912648, and by Northwestern University. This material is based upon work supported by NSF\u2019s LIGO Laboratory which is a major facility fully funded by the National Science Foundation. This research has made use of data and software obtained from GWOSC (gw-openscience.org), a service of LIGO Laboratory, the LIGO Scientific Collaboration, the Virgo Collaboration, and KAGRA. The authors gratefully acknowledge the support of the United States NSF for the construction and operation of the LIGO Laboratory and Advanced LIGO as well as STFC of the United Kingdom, and the Max-Planck-Society for support of the construction of Advanced LIGO. Additional support for Advanced LIGO was provided by the Australian Research Council. Advanced LIGO was built under Award PHY-0823459. LIGO was constructed by the California Institute of Technology and Massachusetts Institute of Technology with funding from the National Science Foundation and operates under Cooperative Agreement PHY-1764464. This work used computing resources at CIERA funded by NSF Grant PHY-1726951, and the computational resources and staff contributions provided for the Quest high performance computing facility at Northwestern University which is jointly supported by the Office of the Provost, the Office for Research, and Northwestern University Information Technology. The authors are grateful for computational resources provided by the LIGO Laboratory and supported by National Science Foundation Grants PHY-0757058 and PHY-0823459. This document has been assigned LIGO document number LIGO-P2200238. The data that support the findings of this study are openly available from Zenodo [46].

Keywords

Gravity Spy
LIGO
glitches
gravitational waves
machine learning

ASJC Scopus subject areas

Physics and Astronomy (miscellaneous)

Access to Document

10.1088/1361-6382/acb633

Cite this

Glanzer, J., Banagiri, S., Coughlin, S. B., Soni, S., Zevin, M., Berry, C. P. L., Patane, O., Bahaadini, S., Rohani, N., Crowston, K., Kalogera, V., Østerlund, C., Trouille, L., & Katsaggelos, A. (2023). Data quality up to the third observing run of advanced LIGO: Gravity Spy glitch classifications. Classical and Quantum Gravity, 40(6), Article 065004. https://doi.org/10.1088/1361-6382/acb633

@article{fb780dd50c8e4d538edc7f0e2ef61431,

title = "Data quality up to the third observing run of advanced LIGO: Gravity Spy glitch classifications",

abstract = "Understanding the noise in gravitational-wave detectors is central to detecting and interpreting gravitational-wave signals. Glitches are transient, non-Gaussian noise features that can have a range of environmental and instrumental origins. The Gravity Spy project uses a machine-learning algorithm to classify glitches based upon their time-frequency morphology. The resulting set of classified glitches can be used as input to detector-characterisation investigations of how to mitigate glitches, or data-analysis studies of how to ameliorate the impact of glitches. Here we present the results of the Gravity Spy analysis of data up to the end of the third observing run of advanced laser interferometric gravitational-wave observatory (LIGO). We classify 233981 glitches from LIGO Hanford and 379805 glitches from LIGO Livingston into morphological classes. We find that the distribution of glitches differs between the two LIGO sites. This highlights the potential need for studies of data quality to be individually tailored to each gravitational-wave observatory.",

keywords = "Gravity Spy, LIGO, glitches, gravitational waves, machine learning",

author = "J. Glanzer and S. Banagiri and Coughlin, {S. B.} and S. Soni and M. Zevin and Berry, {Christopher Philip Luke} and O. Patane and S. Bahaadini and N. Rohani and K. Crowston and V. Kalogera and C. {\O}sterlund and L. Trouille and A. Katsaggelos",

note = "Funding Information: We thank the citizen-science volunteers of Gravity Spy who have contributed to the classifications of LIGO data. We are grateful to Marissa Walker and the anonymous referees for comments on the manuscript. Gravity Spy is partly supported by the National Science Foundation (NSF) Award INSPIRE 1547880 and partially by Award IIS-2107334. This work is supported by the NSF under Grant PHY-1912648. J G is supported by NSF Grant PHY-2110509. S B acknowledges support by NSF Grants PHY-1912648 and IIS-2107334. S S acknowledges support of the NSF Grant PHY-1764464 to the LIGO Laboratory. M Z is supported by NASA through the NASA Hubble Fellowship Grant HST-HF2-51474.001-A awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Incorporated, under NASA contract NAS5-26555. CPLB acknowledges support from the CIERA Board of Visitors Research Professorship, and Science and Technology Facilities Council (STFC) Grant ST/V005634/1. O P is supported by NSF Grant PHY-1559694. V K was partially supported through a CIFAR Senior Fellowship, NSF Grant PHY-1912648, and by Northwestern University. This material is based upon work supported by NSF{\textquoteright}s LIGO Laboratory which is a major facility fully funded by the National Science Foundation. This research has made use of data and software obtained from GWOSC (gw-openscience.org), a service of LIGO Laboratory, the LIGO Scientific Collaboration, the Virgo Collaboration, and KAGRA. The authors gratefully acknowledge the support of the United States NSF for the construction and operation of the LIGO Laboratory and Advanced LIGO as well as STFC of the United Kingdom, and the Max-Planck-Society for support of the construction of Advanced LIGO. Additional support for Advanced LIGO was provided by the Australian Research Council. Advanced LIGO was built under Award PHY-0823459. LIGO was constructed by the California Institute of Technology and Massachusetts Institute of Technology with funding from the National Science Foundation and operates under Cooperative Agreement PHY-1764464. This work used computing resources at CIERA funded by NSF Grant PHY-1726951, and the computational resources and staff contributions provided for the Quest high performance computing facility at Northwestern University which is jointly supported by the Office of the Provost, the Office for Research, and Northwestern University Information Technology. The authors are grateful for computational resources provided by the LIGO Laboratory and supported by National Science Foundation Grants PHY-0757058 and PHY-0823459. This document has been assigned LIGO document number LIGO-P2200238. The data that support the findings of this study are openly available from Zenodo [46]. Publisher Copyright: {\textcopyright} 2023 The Author(s). Published by IOP Publishing Ltd",

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month = mar,

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doi = "10.1088/1361-6382/acb633",

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Glanzer, J, Banagiri, S, Coughlin, SB, Soni, S, Zevin, M, Berry, CPL, Patane, O, Bahaadini, S, Rohani, N, Crowston, K, Kalogera, V, Østerlund, C, Trouille, L & Katsaggelos, A 2023, 'Data quality up to the third observing run of advanced LIGO: Gravity Spy glitch classifications', Classical and Quantum Gravity, vol. 40, no. 6, 065004. https://doi.org/10.1088/1361-6382/acb633

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T1 - Data quality up to the third observing run of advanced LIGO

T2 - Gravity Spy glitch classifications

AU - Glanzer, J.

AU - Banagiri, S.

AU - Coughlin, S. B.

AU - Soni, S.

AU - Zevin, M.

AU - Berry, Christopher Philip Luke

AU - Patane, O.

AU - Bahaadini, S.

AU - Rohani, N.

AU - Crowston, K.

AU - Kalogera, V.

AU - Østerlund, C.

AU - Trouille, L.

AU - Katsaggelos, A.

N1 - Funding Information: We thank the citizen-science volunteers of Gravity Spy who have contributed to the classifications of LIGO data. We are grateful to Marissa Walker and the anonymous referees for comments on the manuscript. Gravity Spy is partly supported by the National Science Foundation (NSF) Award INSPIRE 1547880 and partially by Award IIS-2107334. This work is supported by the NSF under Grant PHY-1912648. J G is supported by NSF Grant PHY-2110509. S B acknowledges support by NSF Grants PHY-1912648 and IIS-2107334. S S acknowledges support of the NSF Grant PHY-1764464 to the LIGO Laboratory. M Z is supported by NASA through the NASA Hubble Fellowship Grant HST-HF2-51474.001-A awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Incorporated, under NASA contract NAS5-26555. CPLB acknowledges support from the CIERA Board of Visitors Research Professorship, and Science and Technology Facilities Council (STFC) Grant ST/V005634/1. O P is supported by NSF Grant PHY-1559694. V K was partially supported through a CIFAR Senior Fellowship, NSF Grant PHY-1912648, and by Northwestern University. This material is based upon work supported by NSF’s LIGO Laboratory which is a major facility fully funded by the National Science Foundation. This research has made use of data and software obtained from GWOSC (gw-openscience.org), a service of LIGO Laboratory, the LIGO Scientific Collaboration, the Virgo Collaboration, and KAGRA. The authors gratefully acknowledge the support of the United States NSF for the construction and operation of the LIGO Laboratory and Advanced LIGO as well as STFC of the United Kingdom, and the Max-Planck-Society for support of the construction of Advanced LIGO. Additional support for Advanced LIGO was provided by the Australian Research Council. Advanced LIGO was built under Award PHY-0823459. LIGO was constructed by the California Institute of Technology and Massachusetts Institute of Technology with funding from the National Science Foundation and operates under Cooperative Agreement PHY-1764464. This work used computing resources at CIERA funded by NSF Grant PHY-1726951, and the computational resources and staff contributions provided for the Quest high performance computing facility at Northwestern University which is jointly supported by the Office of the Provost, the Office for Research, and Northwestern University Information Technology. The authors are grateful for computational resources provided by the LIGO Laboratory and supported by National Science Foundation Grants PHY-0757058 and PHY-0823459. This document has been assigned LIGO document number LIGO-P2200238. The data that support the findings of this study are openly available from Zenodo [46]. Publisher Copyright: © 2023 The Author(s). Published by IOP Publishing Ltd

PY - 2023/3/16

Y1 - 2023/3/16

N2 - Understanding the noise in gravitational-wave detectors is central to detecting and interpreting gravitational-wave signals. Glitches are transient, non-Gaussian noise features that can have a range of environmental and instrumental origins. The Gravity Spy project uses a machine-learning algorithm to classify glitches based upon their time-frequency morphology. The resulting set of classified glitches can be used as input to detector-characterisation investigations of how to mitigate glitches, or data-analysis studies of how to ameliorate the impact of glitches. Here we present the results of the Gravity Spy analysis of data up to the end of the third observing run of advanced laser interferometric gravitational-wave observatory (LIGO). We classify 233981 glitches from LIGO Hanford and 379805 glitches from LIGO Livingston into morphological classes. We find that the distribution of glitches differs between the two LIGO sites. This highlights the potential need for studies of data quality to be individually tailored to each gravitational-wave observatory.

AB - Understanding the noise in gravitational-wave detectors is central to detecting and interpreting gravitational-wave signals. Glitches are transient, non-Gaussian noise features that can have a range of environmental and instrumental origins. The Gravity Spy project uses a machine-learning algorithm to classify glitches based upon their time-frequency morphology. The resulting set of classified glitches can be used as input to detector-characterisation investigations of how to mitigate glitches, or data-analysis studies of how to ameliorate the impact of glitches. Here we present the results of the Gravity Spy analysis of data up to the end of the third observing run of advanced laser interferometric gravitational-wave observatory (LIGO). We classify 233981 glitches from LIGO Hanford and 379805 glitches from LIGO Livingston into morphological classes. We find that the distribution of glitches differs between the two LIGO sites. This highlights the potential need for studies of data quality to be individually tailored to each gravitational-wave observatory.

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Data quality up to the third observing run of advanced LIGO: Gravity Spy glitch classifications

Abstract

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Gravity Spy Machine Learning Classifications of LIGO Glitches from Observing Runs O1, O2, O3a, and O3b

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Data quality up to the third observing run of advanced LIGO: Gravity Spy glitch classifications

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Funding

Keywords

ASJC Scopus subject areas

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Gravity Spy Machine Learning Classifications of LIGO Glitches from Observing Runs O1, O2, O3a, and O3b

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