Skip to main content
SpringerLink
Log in

Time series anomaly detection for gravitational-wave detectors based on the Hilbert–Huang transform

  • Original Paper
  • Published:
Journal of the Korean Physical Society Aims and scope Submit manuscript

Abstract

We present a new event trigger generator based on the Hilbert–Huang transform, named EtaGen (\(\eta\)Gen). It decomposes time-series data into several adaptive modes without imposing a priori bases on the data. The adaptive modes are used to find transients (excesses) in the background noises. A clustering algorithm is used to gather excesses corresponding to a single event and to reconstruct its waveform. The performance of EtaGen is evaluated by how many injections are found in the LIGO simulated data. EtaGen is viable as an event trigger generator when compared directly with the performance of Omicron, which is currently the best event trigger generator used in the LIGO Scientific Collaboration and Virgo Collaboration.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. Here, we describe a mode to be adaptive if it is determined during the decomposition process to depend on the original time series data as opposed to the mode that is determined by a predefined basis set.

References

  1. Abbott, B. P., et al.,(Virgo, LIGO Scientific) 2016a. Characterization of transient noise in Advanced LIGO relevant to gravitational wave signal GW150914. Class. Quant. Grav. 33 (13), 134001. https://doi.org/10.1088/0264-9381/33/13/134001arXiv:1602.03844

  2. Abbott, B. P., et al.,(Virgo, LIGO Scientific) 2016b. GW150914: Implications for the stochastic gravitational wave background from binary black holes. Phys. Rev. Lett. 116 (13), 131102. https://doi.org/10.1103/PhysRevLett.116.131102arXiv:1602.03847

  3. Abbott, B. P., et al.,(Virgo, LIGO Scientific), 2016c. GW151226: Observation of Gravitational Waves from a 22-Solar-Mass Binary Black Hole Coalescence. Phys. Rev. Lett. 116 (24), 241103. https://doi.org/10.1103/PhysRevLett.116.241103arXiv:1606.04855

  4. Abbott, B. P., et al.,(LIGO Scientific) 2017a. Exploring the Sensitivity of Next Generation Gravitational Wave Detectors. Class. Quant. Grav. 34 (4), 044001. https://doi.org/10.1088/1361-6382/aa51f4arXiv:1607.08697

  5. Abbott, B. P., et al.,(VIRGO, LIGO Scientific) 2017b. GW170104: Observation of a 50-Solar-Mass Binary Black Hole Coalescence at Redshift 0.2. Phys. Rev. Lett. 118 (22), 221101. https://doi.org/10.1103/PhysRevLett.118.221101arXiv:1706.01812

  6. Abbott, B. P., et al.,(Virgo, LIGO Scientific) 2017c. GW170814: A Three-Detector Observation of Gravitational Waves from a Binary Black Hole Coalescence. Phys. Rev. Lett. 119 (14), 141101. https://doi.org/10.1103/PhysRevLett.119.141101arXiv:1709.09660

  7. Abbott, B. P., et al.,(Virgo, LIGO Scientific), 2017d. GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral. Phys. Rev. Lett. 119 (16), 161101. https://doi.org/10.1103/PhysRevLett.119.161101arXiv:1710.05832

  8. Abbott, B. P., et al.,(LIGO Scientific, Virgo) 2018. GWTC-1: A Gravitational-Wave Transient Catalog of Compact Binary Mergers Observed by LIGO and Virgo during the First and Second Observing Runs. arXiv:1811.12907

  9. J.B. Camp, J.K. Cannizzo, K. Numata, Application of the Hilbert–Huang transform to the search for gravitational waves. Phys. Rev. D 75, 061101 (2007). https://doi.org/10.1103/PhysRevD.75.061101. arXiv:gr-qc/0701148

    Article  ADS  Google Scholar 

  10. Faltermeier, R., Zeiler, A., Keck, I. R., Tomé, A. M., Brawanski, A., Lang, E. W., July 2010. Sliding empirical mode decomposition. In: The 2010 International Joint Conference on Neural Networks (IJCNN). pp. 1–8. https://doi.org/10.1109/IJCNN.2010.5596536

  11. R. Faltermeier, A. Zeiler, A.M. Tomé, A. Brawanski, E.W. Lang, Weighted sliding empirical mode decomposition. Adv. Adapt. Data Anal. 03(04), 509–526 (2011). https://doi.org/10.1142/S1793536911000891

    Article  MathSciNet  Google Scholar 

  12. P. Flandrin, G. Rilling, P. Goncalves, Empirical mode decomposition as a filter bank. IEEE Signal Process. Lett. 11(2), 112–114 (2004). https://doi.org/10.1109/LSP.2003.821662

    Article  ADS  Google Scholar 

  13. N.E. Huang, S.S.P. Shen, Hilbert-Huang Transform and Its Applications. WORLD SCIENTIFIC (2005). https://doi.org/10.1142/5862

  14. N.E. Huang, Z. Shen, S.R. Long, M.C. Wu, H.H. Shih, Q. Zheng, N.-C. Yen, C.C. Tung, H.H. Liu, The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. Lond. A 454(1971), 903–995 (1998). https://doi.org/10.1098/rspa.1998.0193

    Article  ADS  MathSciNet  MATH  Google Scholar 

  15. N.E. Huang, M.-L.C. Wu, S.R. Long, S.S. Shen, W. Qu, P. Gloersen, K.L. Fan, A confidence limit for the empirical mode decomposition and Hlbert spectral analysis. Proc. R. Soc. Lond. A 459(2037), 2317–2345 (2003). https://doi.org/10.1098/rspa.2003.1123

    Article  ADS  MathSciNet  MATH  Google Scholar 

  16. N.E. Huang, Z. Wu, A review on hilbert-huang transform: method and its applications to geophysical studies. Rev. Geophys. 46(2), RG2006 (2008). https://doi.org/10.1029/2007RG000228

    Article  ADS  Google Scholar 

  17. M. Kaneyama, K.-I. Oohara, H. Takahashi, Y. Sekiguchi, H. Tagoshi, M. Shibata, Analysis of gravitational waves from binary neutron star merger by Hilbert–Huang transform. Phys. Rev. D 93(12), 123010 (2016). https://doi.org/10.1103/PhysRevD.93.123010

    Article  ADS  MathSciNet  Google Scholar 

  18. McIver, J. L., 2015. The impact of terrestrial noise on the detectability and reconstruction of gravitational wave signals from core-collapse supernovae. Ph.D. thesis, Massachusetts U., Amherst

  19. Nuttall, L., Massinger, T. J., Areeda, J., Betzwieser, J., Dwyer, S., Effler, A., Fisher, R. P., Fritschel, P., Kissel, J. S., Lundgren, A. P., Macleod, D. M., Martynov, D., McIver, J., Mullavey, A., Sigg, D., Smith, J. R., Vajente, G., Williamson, A. R., Wipf, C. C., Improving the data quality of advanced ligo based on early engineering run results. Classical and Quantum Gravity 32 (24), 245005

  20. Rousseeuw, P. J., Croux, C., Alternatives to the median absolute deviation. J. Am. Statistical Assoc. (424). https://doi.org/10.2307/2291267

  21. K. Sakai, K.I. Oohara, H. Nakano, M. Kaneyama, H. Takahashi, Estimation of starting times of quasi-normal modes in ringdown gravitational waves with the Hilbert-Huang transform. Phys. Rev. D96(4), 044047 (2017). https://doi.org/10.1103/PhysRevD.96.044047. arXiv:1705.04107

    Article  ADS  Google Scholar 

  22. A. Stroeer, J. Camp, Ninja data analysis with a detection pipeline based on the Hilbert–Huang Transform. Class. Quant. Grav. 26, 114012 (2009). https://doi.org/10.1088/0264-9381/26/11/114012. arXiv:0903.2026

    Article  ADS  MathSciNet  Google Scholar 

  23. A. Stroeer, J.K. Cannizzo, J.B. Camp, N. Gagarin, Methods for detection and characterization of signals in noisy data with the Hilbert–Huang transform. Phys. Rev. D 79, 124022 (2009). https://doi.org/10.1103/PhysRevD.79.124022. arXiv:0903.4616

    Article  ADS  Google Scholar 

  24. G. Valdes, B. O’Reilly, M. Diaz, A Hilbert–Huang transform method for scattering identification in LIGO. Class. Quant. Grav. 34(23), 235009 (2017). https://doi.org/10.1088/1361-6382/aa8e6b

    Article  ADS  Google Scholar 

  25. G. Wang, X.-Y. Chen, F.-L. Qiao, Z. Wu, N.E. Huang, On intrinsic mode function. Adv. Adapt. Data Anal. 02(03), 277–293 (2010). https://doi.org/10.1142/S1793536910000549

    Article  MathSciNet  Google Scholar 

  26. Z. Wu, N.E. Huang, Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv. Adapt. Data Anal. 01(01), 1–41 (2009). https://doi.org/10.1142/S1793536909000047

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Jay Tasson for helpful comments and suggestions and Chris Pankow for letting us use the simulated GW data. EJS is grateful to Hyoungseok Chu for the fruitful contribution to the early stage of this work. This work was supported by Basic Science Research Program through a National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2020R1I1A2054376, and NRF-2018R1D1A1B07041004). This work is partially supported by the NRF grant funded by the Korea government’s Ministry of Science and CIT (No. 2019R1A2C2006787, No. 2016R1A5A1013277, and No. 2019R1C1C1010571). LIGO was constructed by the California Institute of Technology and the Massachusetts Institute of Technology with funding from the National Science Foundation and operates under cooperative agreement PHY-0757058. This paper carries LIGO Document Number LIGO-P1800294.

Author information

Authors and Affiliations

  1. Division of Basic Researches for Industrial Mathematics, National Institute for Mathematical Sciences, Daejeon, 34047, Republic of Korea

    Edwin J. Son, Whansun Kim, John J. Oh & Sang Hoon Oh

  2. Department of Physics, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea

    Young-Min Kim

  3. Department of Physics and Astronomy, University of British Columbia, Vancouver, BC V6T 1Z4, Canada

    Jessica McIver

Authors
  1. Edwin J. Son
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Whansun Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Young-Min Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jessica McIver
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. John J. Oh
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sang Hoon Oh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Young-Min Kim or John J. Oh.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Son, E.J., Kim, W., Kim, YM. et al. Time series anomaly detection for gravitational-wave detectors based on the Hilbert–Huang transform. J. Korean Phys. Soc. 78, 878–885 (2021). https://doi.org/10.1007/s40042-021-00094-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40042-021-00094-2

Keywords

Search

Navigation