Advertisement

Predictive models for identification of gravitational waves by applying data from LIGO observatory

  • J. Skeivalas
  • V. TurlaEmail author
  • M. Jurevicius
Original Paper
  • 18 Downloads

Abstract

This paper explores the possibility of identifying gravitational waves by statistically processing data obtained from the experiment performed by the Laser Interferometer Gravitational-Wave Observatory (LIGO observatory). For an analysis of the measurement data arrays, the parameter z from the Doppler formula and the theory of covariance functions has been used. The trend of oscillation vectors of detectors obtained at the Hanford and Livingston observatories was assessed by applying the least square method. In addition, this procedure partially eliminates random errors in the data obtained from measurements carried out by the observatory. Upon assessment of the impact of gravitational waves on the changes in the values of the parameters of interferometer laser beams, the estimates of the auto-covariance and cross-covariance functions of vibration vectors of detectors measured at the observatories were calculated by varying the quantised interval on the time scale. The covariance of algebraic addition of relevant vectors and single vectors was used in the calculation of the estimates of covariance functions. The average value of the parameter z from the Doppler formula was calculated according to the formula created by using the expression of cross-covariance function of algebraic addition Hanford Gravitational Wave–Livingston Gravitational Wave (HGW−LGW) vector and single LGW vector. The speed and the direction of spread of the gravitational waves’ component HGW → LGW in respect of the vector of the gravitational waves were established. The calculations were performed using the author’s original software based on MATLAB procedures.

Keywords

Gravitational waves Covariance function Quantised interval The Doppler formula 

PACS Nos.

02.50.Ey 02.50.Fz 13.85.Tp 42.30.−d 

Notes

References

  1. [1]
    R Naeye Sky and Telescope (2016)Google Scholar
  2. [2]
    B P Abbott et al. Astrophys. J. 818 L22 (2016). arXiv:1602.03846.  https://doi.org/10.3847/2041-8205/818/2/l22
  3. [3]
    B P Abbott et al. Living Rev. Relativ. 19. arXiv:1304.0670.  https://doi.org/10.1007/lrr-2016-1
  4. [4]
    V Savchenko et al. Astrophys. J. Lett. 820 L36. arXiv:1602.04180.  https://doi.org/10.3847/2041-8205/820/2/l36
  5. [5]
    K Cooper PhysicsWorld.com. Institute of Physics (2016)Google Scholar
  6. [6]
    D Castelvecchi Nature News (2015)Google Scholar
  7. [7]
    L Iorio Universe 1 38 (2015)Google Scholar
  8. [8]
    I Debono, G F Smoot Universe 2 (2016)Google Scholar
  9. [9]
    R G Vishwakarma Universe 2 (2016)Google Scholar
  10. [10]
    K Saikawa Universe 3 (2017)Google Scholar
  11. [11]
    M Bradley et al. Universe 3 (2017)Google Scholar
  12. [12]
    M I Ahmad et al. Signal Image Video Process. 12 379 (2018)CrossRefGoogle Scholar
  13. [13]
    T Commissariat, M Harris Physics World (2016)Google Scholar
  14. [14]
    B P Abbott et al. Phys. Rev. Lett. 116 241103.  https://doi.org/10.1103/physrevlett.116.241103
  15. [15]
    B P Abbott et al. Astrophys. J. Lett. 833 L1. arXiv:1602.03842.  https://doi.org/10.3847/2041-8205/833/1/l1
  16. [16]
    J Miller et al. Phys. Rev. D. 91 (2015) 062005. arXiv:1410.5882.  https://doi.org/10.1103/physrevd.91.062005
  17. [17]
    J Creswell et al. J. Cosmol. Astropart. Phys. 08 (2017)Google Scholar
  18. [18]
    J Creswell et al. J. Cosmol. Astropart. Phys. 03 (2018)Google Scholar
  19. [19]
    H Liu et al. J. Cosmol. Astropart. Phys. 02 (2018)Google Scholar
  20. [20]
    A Raman On the Signal Processing Operations in LIGO signals. arXiv:1711.07421 (2017)
  21. [21]
    R Penrose Correlated “noise” in LIGO gravitational wave signals: an implication of Conformal Cyclic Cosmology. arXiv:1707.04169 (2017)
  22. [22]
    J A Rueda et al. J. Cosmol. Astropart. Phys. 10 (2018)Google Scholar
  23. [23]
    J Aasi et al. (LIGO Scientific Collaboration) Class and Quantum Gravity 32 074001 (2015).  https://doi.org/10.1088/0264-9381/32/7/074001 ADSCrossRefGoogle Scholar
  24. [24]
    J Skeivalas Theory and Practice of GPS Networks (Vilnius: Technika) (2008)Google Scholar
  25. [25]
    M N S Jahromi et al. Signal, Image and Video Processing 2017 11 533 (2017)Google Scholar
  26. [26]
    K N Singh et al. Indian Journal of Physics 90 1215 (2016)ADSCrossRefGoogle Scholar
  27. [27]
    H Kahmen Elektronische Messverfahren in der Geodäsie. Grundlagen und Anwendungen (Verlag Karsruhe: Weichmann) 252 (1978)Google Scholar
  28. [28]
    K R Koch Einführung in die Byes-Statistik (Springer-Verlag Berlin Heidelberg) 224 (2000)Google Scholar
  29. [29]
    J Skeivalas, E Parseliunas Optical Engineering 52(7) (2013)Google Scholar

Copyright information

© Indian Association for the Cultivation of Science 2019

Authors and Affiliations

  1. 1.Department of Geodesy and CadastreVilnius Gediminas Technical UniversityVilniusLithuania
  2. 2.Department of Mechatronics, Robotics and Digital ManufacturingVilnius Gediminas Technical UniversityVilniusLithuania
  3. 3.Department of Mechanical and Materials EngineeringVilnius Gediminas Technical UniversityVilniusLithuania

Personalised recommendations