Built on

Cabero, M., et al
Original
1901
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Coughlin, S.B., et al
Original
1903
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Walmsley, M., Smith, L., Lintott, C., Gal, Y., Bamford, S., Dickinson, H., Fortson, L., Kruk, S., Masters, K., Scarlata, C., Simmons, B., Smethurst, R., Wright, D.: Galaxy Zoo: probabilistic morphology through Bayesian CNNs and active learning. Mon. Not. R. Ast. Soc. 491
Original
1905
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Abbott, B.P., et al
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1908
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Einstein, A.: Approximative Integration of the Field Equations of Gravitation. Sitzungsber. Preuss. Akad. Wiss. Berlin (Math. Phys.) 1916
1916
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Einstein, A.: Über Gravitationswellen. Sitzungsber. Preuss. Akad. Wiss. Berlin (Math. Phys.) 1918
1918
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Vygotsky, L.S.: Mind in Society: Development of Higher Psychological Processes. Harvard University Press, ??? (1978)
1978
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Davis, D., White, L.V., Saulson, P.R.: Utilizing aLIGO Glitch Classifications to Validate Gravitational-Wave Candidates. Class. Quant. Grav. 37
Original
2002
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Torres-Forné, A., Cuoco, E., Font, J.A., Marquina, A.: Application of dictionary learning to denoise LIGO’s blip noise transients. Phys. Rev. D 102
Original
2002
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Chatterji, S., Blackburn, L., Martin, G., Katsavounidis, E.: Multiresolution techniques for the detection of gravitational-wave bursts. Class. Quant. Grav. 21
2004
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Benkő, Z., Bábel, T., Somogyvári, Z.: Model-free detection of unique events in time series. Sci. Rep. 12
Original
2004
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Robinet, F., Arnaud, N., Leroy, N., Lundgren, A., Macleod, D., McIver, J.: Omicron: a tool to characterize transient noise in gravitational-wave detectors. SoftwareX 12
Original
2007
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Soni, S., et al
Original
2007
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Blackburn, L., et al
Original
2008
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Lintott, C.J., et al
Original
2008
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Singh, S., Singh, A., Prajapati, A., Pathak, K.N.: Deep learning for estimating parameters of gravitational waves. Mon. Not. Roy. Astron. Soc. 508
Original
2008
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Buikema, A., et al
Original
2008
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Bonney, R., Cooper, C.B., Dickinson, J., Kelling, S., Phillips, T., Rosenberg, K.V., Shirk, J.: Citizen Science: A Developing Tool for Expanding Science Knowledge and Scientific Literacy 59
2009
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Cardamone, C.N., et al
Original
2009
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Ball, N.M., Brunner, R.J.: Data Mining and Machine Learning in Astronomy. Int. J. Mod. Phys. D 19
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2010
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Marianer, T., Poznanski, D., Prochaska, J.X.: A semisupervised machine learning search for never-seen gravitational-wave sources. Mon. Not. Roy. Astron. Soc. 500
Original
2010
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Cabero, M., Mahabal, A., McIver, J.: GWSkyNet: a real-time classifier for public gravitational-wave candidates. Astrophys. J. Lett. 904
Original
2010
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Jadhav, S., Mukund, N., Gadre, B., Mitra, S., Abraham, S.: Improving significance of binary black hole mergers in Advanced LIGO data using deep learning: Confirmation of GW151216. Phys. Rev. D 104
Original
2010
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Raddick, M.J., Bracey, G., Gay, P.L., Lintott, C.J., Murray, P., Schawinski, K., Szalay, A.S., Vandenberg, J.: Galaxy Zoo: Exploring the Motivations of Citizen Science Volunteers. Astron. Educ. Rev. 9
Original
2010
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Eisner, N.L., Barragán, O., Lintott, C., Aigrain, S., Nicholson, B., Boyajian, T.S., Howell, S., Johnston, C., Lakeland, B., Miller, G., McMaster, A., Parviainen, H., Safron, E.J., Schwamb, M.E., Trouille, L., Vaughan, S., Zicher, N., Allen, C., Allen, S., Bouslog, M., Johnson, C., Simon, M.N., Wolfenbarger, Z., Baeten, E.M.L., Bundy, D.M., Hoffman, T.: Planet Hunters TESS II: findings from the first two years of TESS. Mon. Not. R. Ast. Soc. 501
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2011
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Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25
2012
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Engeström, Y.: Learning by Expanding. Cambridge University Press, ??? (2014)
2014
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Rotman, D., Hammock, J., Preece, J., Boston, C.L., Hansen, D.L., Bowser, A., He, Y.: Does motivation in citizen science change with time and culture? the companion publication of the 17th ACM conference, pp. 229–232 (2014). https://doi.org/10.1145/2556420.2556492
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Aasi, J., et al
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Acernese, F., et al
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2015
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Sauermann, H., Franzoni, C.: Crowd science user contribution patterns and their implications. Proceedings of the National Academy of Sciences of the United States of America 112
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Abbott, B.P., et al
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Abbott, B.P., et al
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Lundgren, A.: aLIGO LHO Logbook. 27138 (2016)
2016
Earlier work this paper cites.
Abbott, B.P., et al
Original
2016
Earlier work this paper cites.

Similar

Nuttall, L.K.: Characterizing transient noise in the LIGO detectors. Phil. Trans. Roy. Soc. Lond. A 376
Original
2017
Cited alongside, same era.
Zevin, M., et al
Original
2017
Cited alongside, same era.
Bahaadini, S., Rohani, N., Coughlin, S., Zevin, M., Kalogera, V., Katsaggelos, A.K.: Deep Multi-view Models for Glitch Classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2931–2935 (2017). https://doi.org/10.1109/ICASSP.2017.7952693
2017
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Areeda, J.S., Smith, J.R., Lundgren, A.P., Maros, E., Macleod, D.M., Zweizig, J.: LigoDV-web: Providing easy, secure and universal access to a large distributed scientific data store for the LIGO Scientific Collaboration. Astron. Comput. 18
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2017
Cited alongside, same era.

Then

Davis, D., Walker, M.: Detector Characterization and Mitigation of Noise in Ground-Based Gravitational-Wave Interferometers. Galaxies 10
2022
Later among the works it cites.
Zevin, M., Coughlin, S., Chase, E., Allen, S., Bahaadini, S., Berry, C., Crowston, K., Harandi, M., Jackson, C., Kalogera, V., Katsaggelos, A., Osterlund, C., Patane, O., Rohani, N., Smith, J., Soni, S., Trouille, L.: Gravity Spy Volunteer Classifications of LIGO Glitches from Observing Runs O1, O2, O3a, and O3b. https://doi.org/10.5281/zenodo.5911227 . https://doi.org/10.5281/zenodo.5911227
2022
Later among the works it cites.
Ashton, G., Thiele, S., Lecoeuche, Y., McIver, J., Nuttall, L.K.: Parameterised population models of transient non-Gaussian noise in the LIGO gravitational-wave detectors. Class. Quant. Grav. 39
Original
2022
Later among the works it cites.
Macas, R., Pooley, J., Nuttall, L.K., Davis, D., Dyer, M.J., Lecoeuche, Y., Lyman, J.D., McIver, J., Rink, K.: Impact of noise transients on low latency gravitational-wave event localization. Phys. Rev. D 105
Original

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2023

Gravity Spy: Lessons Learned and a Path Forward

Zevin, Michael, Jackson, Corey B., Doctor, Zoheyr et al.

Understand

The Gravity Spy project aims to uncover the origins of glitches, transient bursts of noise that hamper analysis of gravitational-wave data.

By using both the work of citizen-science volunteers and machine-learning algorithms, the Gravity Spy project enables reliable classification of glitches.
Citizen science and machine learning are intrinsically coupled within the Gravity Spy framework, with machine-learning classifications providing a rapid first-pass classification of the dataset and enabling tiered volunteer training, and volunteer-based classifications verifying the machine classifications, bolstering the machine-learning training set and identifying new morphological classes of glitches.
These classifications are now routinely used in studies characterizing the performance of the LIGO gravitational-wave detectors.

Built on

Cabero, M., et al
Original
1901
Earlier work this paper cites.
Coughlin, S.B., et al
Original
1903
Earlier work this paper cites.
Walmsley, M., Smith, L., Lintott, C., Gal, Y., Bamford, S., Dickinson, H., Fortson, L., Kruk, S., Masters, K., Scarlata, C., Simmons, B., Smethurst, R., Wright, D.: Galaxy Zoo: probabilistic morphology through Bayesian CNNs and active learning. Mon. Not. R. Ast. Soc. 491
Original
1905
Earlier work this paper cites.
Abbott, B.P., et al
Original
1908
Earlier work this paper cites.
Einstein, A.: Approximative Integration of the Field Equations of Gravitation. Sitzungsber. Preuss. Akad. Wiss. Berlin (Math. Phys.) 1916
1916
Earlier work this paper cites.
Einstein, A.: Über Gravitationswellen. Sitzungsber. Preuss. Akad. Wiss. Berlin (Math. Phys.) 1918
1918
Earlier work this paper cites.
Vygotsky, L.S.: Mind in Society: Development of Higher Psychological Processes. Harvard University Press, ??? (1978)
1978
Earlier work this paper cites.
Davis, D., White, L.V., Saulson, P.R.: Utilizing aLIGO Glitch Classifications to Validate Gravitational-Wave Candidates. Class. Quant. Grav. 37
Original
2002
Earlier work this paper cites.
Torres-Forné, A., Cuoco, E., Font, J.A., Marquina, A.: Application of dictionary learning to denoise LIGO’s blip noise transients. Phys. Rev. D 102
Original
2002
Earlier work this paper cites.
Chatterji, S., Blackburn, L., Martin, G., Katsavounidis, E.: Multiresolution techniques for the detection of gravitational-wave bursts. Class. Quant. Grav. 21
2004
Earlier work this paper cites.
Benkő, Z., Bábel, T., Somogyvári, Z.: Model-free detection of unique events in time series. Sci. Rep. 12
Original
2004
Earlier work this paper cites.
Robinet, F., Arnaud, N., Leroy, N., Lundgren, A., Macleod, D., McIver, J.: Omicron: a tool to characterize transient noise in gravitational-wave detectors. SoftwareX 12
Original
2007
Earlier work this paper cites.
Soni, S., et al
Original
2007
Earlier work this paper cites.
Blackburn, L., et al
Original
2008
Earlier work this paper cites.
Lintott, C.J., et al
Original
2008
Earlier work this paper cites.
Singh, S., Singh, A., Prajapati, A., Pathak, K.N.: Deep learning for estimating parameters of gravitational waves. Mon. Not. Roy. Astron. Soc. 508
Original
2008
Earlier work this paper cites.
Buikema, A., et al
Original
2008
Earlier work this paper cites.
Bonney, R., Cooper, C.B., Dickinson, J., Kelling, S., Phillips, T., Rosenberg, K.V., Shirk, J.: Citizen Science: A Developing Tool for Expanding Science Knowledge and Scientific Literacy 59
2009
Earlier work this paper cites.
Cardamone, C.N., et al
Original
2009
Earlier work this paper cites.
Ball, N.M., Brunner, R.J.: Data Mining and Machine Learning in Astronomy. Int. J. Mod. Phys. D 19
Original
2010
Earlier work this paper cites.
Marianer, T., Poznanski, D., Prochaska, J.X.: A semisupervised machine learning search for never-seen gravitational-wave sources. Mon. Not. Roy. Astron. Soc. 500
Original
2010
Earlier work this paper cites.
Cabero, M., Mahabal, A., McIver, J.: GWSkyNet: a real-time classifier for public gravitational-wave candidates. Astrophys. J. Lett. 904
Original
2010
Earlier work this paper cites.
Jadhav, S., Mukund, N., Gadre, B., Mitra, S., Abraham, S.: Improving significance of binary black hole mergers in Advanced LIGO data using deep learning: Confirmation of GW151216. Phys. Rev. D 104
Original
2010
Earlier work this paper cites.
Raddick, M.J., Bracey, G., Gay, P.L., Lintott, C.J., Murray, P., Schawinski, K., Szalay, A.S., Vandenberg, J.: Galaxy Zoo: Exploring the Motivations of Citizen Science Volunteers. Astron. Educ. Rev. 9
Original
2010
Earlier work this paper cites.
Eisner, N.L., Barragán, O., Lintott, C., Aigrain, S., Nicholson, B., Boyajian, T.S., Howell, S., Johnston, C., Lakeland, B., Miller, G., McMaster, A., Parviainen, H., Safron, E.J., Schwamb, M.E., Trouille, L., Vaughan, S., Zicher, N., Allen, C., Allen, S., Bouslog, M., Johnson, C., Simon, M.N., Wolfenbarger, Z., Baeten, E.M.L., Bundy, D.M., Hoffman, T.: Planet Hunters TESS II: findings from the first two years of TESS. Mon. Not. R. Ast. Soc. 501
Original
2011
Earlier work this paper cites.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25
2012
Earlier work this paper cites.
Engeström, Y.: Learning by Expanding. Cambridge University Press, ??? (2014)
2014
Earlier work this paper cites.
Rotman, D., Hammock, J., Preece, J., Boston, C.L., Hansen, D.L., Bowser, A., He, Y.: Does motivation in citizen science change with time and culture? the companion publication of the 17th ACM conference, pp. 229–232 (2014). https://doi.org/10.1145/2556420.2556492
2014
Earlier work this paper cites.
Aasi, J., et al
Original
2015
Earlier work this paper cites.
Acernese, F., et al
Original
2015
Earlier work this paper cites.
Sauermann, H., Franzoni, C.: Crowd science user contribution patterns and their implications. Proceedings of the National Academy of Sciences of the United States of America 112
2015
Earlier work this paper cites.
Abbott, B.P., et al
Original
2016
Earlier work this paper cites.
Abbott, B.P., et al
Original
2016
Earlier work this paper cites.
Lundgren, A.: aLIGO LHO Logbook. 27138 (2016)
2016
Earlier work this paper cites.
Abbott, B.P., et al
Original
2016
Earlier work this paper cites.

Similar

Nuttall, L.K.: Characterizing transient noise in the LIGO detectors. Phil. Trans. Roy. Soc. Lond. A 376
Original
2017
Cited alongside, same era.
Zevin, M., et al
Original
2017
Cited alongside, same era.
Bahaadini, S., Rohani, N., Coughlin, S., Zevin, M., Kalogera, V., Katsaggelos, A.K.: Deep Multi-view Models for Glitch Classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2931–2935 (2017). https://doi.org/10.1109/ICASSP.2017.7952693
2017
Cited alongside, same era.
Areeda, J.S., Smith, J.R., Lundgren, A.P., Maros, E., Macleod, D.M., Zweizig, J.: LigoDV-web: Providing easy, secure and universal access to a large distributed scientific data store for the LIGO Scientific Collaboration. Astron. Comput. 18
Original
2017
Cited alongside, same era.

Then

Davis, D., Walker, M.: Detector Characterization and Mitigation of Noise in Ground-Based Gravitational-Wave Interferometers. Galaxies 10
2022
Later among the works it cites.
Zevin, M., Coughlin, S., Chase, E., Allen, S., Bahaadini, S., Berry, C., Crowston, K., Harandi, M., Jackson, C., Kalogera, V., Katsaggelos, A., Osterlund, C., Patane, O., Rohani, N., Smith, J., Soni, S., Trouille, L.: Gravity Spy Volunteer Classifications of LIGO Glitches from Observing Runs O1, O2, O3a, and O3b. https://doi.org/10.5281/zenodo.5911227 . https://doi.org/10.5281/zenodo.5911227
2022
Later among the works it cites.
Ashton, G., Thiele, S., Lecoeuche, Y., McIver, J., Nuttall, L.K.: Parameterised population models of transient non-Gaussian noise in the LIGO gravitational-wave detectors. Class. Quant. Grav. 39
Original
2022
Later among the works it cites.
Macas, R., Pooley, J., Nuttall, L.K., Davis, D., Dyer, M.J., Lecoeuche, Y., Lyman, J.D., McIver, J., Rink, K.: Impact of noise transients on low latency gravitational-wave event localization. Phys. Rev. D 105
Original

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

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Vajente, G.: aLIGO LHO Logbook. 35073 (2017)

2017

Cited alongside, same era.

Bahaadini, S., Noroozi, V., Rohani, N., Coughlin, S., Zevin, M., Katsaggelos, A.K.: DIRECT: Deep Discriminative Embedding for Clustering of LIGO Data. In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 748–752 (2018). https://doi.org/10.1109/ICIP.2018.8451708

2018

Cited alongside, same era.

Bahaadini, S., Noroozi, V., Rohani, N., Coughlin, S., Zevin, M., Smith, J.R., Kalogera, V., Katsaggelos, A.: Machine learning for Gravity Spy: Glitch classification and dataset. Info. Sci. 444

2018

Cited alongside, same era.

Patane, O.: aLIGO LHO Logbook. 43177 (2018)

2018

Cited alongside, same era.

George, D., Shen, H., Huerta, E.A.: Classification and unsupervised clustering of LIGO data with Deep Transfer Learning. Phys. Rev. D 97