Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
184 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

A novel multi-layer modular approach for real-time fuzzy-identification of gravitational-wave signals (2206.06004v4)

Published 13 Jun 2022 in gr-qc, cs.LG, and cs.NE

Abstract: Advanced LIGO and Advanced Virgo ground-based interferometers are instruments capable to detect gravitational wave signals exploiting advanced laser interferometry techniques. The underlying data analysis task consists in identifying specific patterns in noisy timeseries, but it is made extremely complex by the incredibly small amplitude of the target signals. In this scenario, the development of effective gravitational wave detection algorithms is crucial. We propose a novel layered framework for real-time detection of gravitational waves inspired by speech processing techniques and, in the present implementation, based on a state-of-the-art machine learning approach involving a hybridization of genetic programming and neural networks. The key aspects of the newly proposed framework are: the well structured, layered approach, and the low computational complexity. The paper describes the basic concepts of the framework and the derivation of the first three layers. Even if the layers are based on models derived using a machine learning approach, the proposed layered structure has a universal nature. Compared to more complex approaches, such as convolutional neural networks, which comprise a parameter set of several tens of MB and were tested exclusively for fixed length data samples, our framework has lower accuracy (e.g., it identifies 45% of low signal-to-noise-ration gravitational wave signals, against 65% of the state-of-the-art, at a false alarm probability of $10{-2}$), but has a much lower computational complexity and a higher degree of modularity. Furthermore, the exploitation of short-term features makes the results of the new framework virtually independent against time-position of gravitational wave signals, simplifying its future exploitation in real-time multi-layer pipelines for gravitational-wave detection with new generation interferometers.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (55)
  1. Aasi J et al. 2015 Class. Quant. Grav. 32 074001
  2. Abbott B et al. 2016 Phys. Rev. Lett. 116 131103
  3. Abbott B et al. 2016 Phys. Rev. Lett. 116 061102
  4. Abbott B et al. 2016 Phys. Rev. Lett. 116 221101
  5. Abbott B et al. 2016 Phys. Rev. X 6 041015
  6. Abbott B et al. 2016 Phys. Rev. Lett. 116 241102
  7. Li T et al. 2012 Phys. Rev. D 85 082003
  8. Mishra C et al. 2010 Phys. Rev. D 82 064010
  9. Acernese F et al. 2015 Class. Quant. Grav. 32 024001
  10. Abbott B et al. 2017 (LIGO Scientific Collaboration and Virgo Collaboration), Phys. Rev. Lett. 119 141101
  11. Abbott B et al. 2019 (LIGO Scientific Collaboration and Virgo Collaboration), Phys. Rev. X 9 031040
  12. Abbott R et al. 2020 (LIGO Scientific Collaboration and Virgo Collaboration), Phys. Rev. Lett. 125 101102
  13. Abbott B et al. 2017 (LIGO Scientific Collaboration and Virgo Collaboration), Phys. Rev. Lett. 119 161101
  14. Abbott B et al. 2017 Astrophys. J. 848 L12
  15. Abbott B et al. 2017 Astrophys. J. Lett. 848 L13
  16. Abbott B et al. 2016 Living Rev. Relativity 19 1
  17. Abbott T et al. 2016 (Dark Energy Survey Collaboration) Mon. Not. R. Astron. Soc. 460 1270
  18. Abdo A et al. 2013 Astrophys. J. Suppl. Ser. 208 17
  19. Aartsen M et al. 2020 Astrophys. J. Lett. 898 L10
  20. Abe K et al. 2018 Astrophys. J. Lett. 857 L4
  21. Adrián-Martinez S et al. 2007 (The ANTARES collaboration, the LIGO scientific collaboration and the Virgo collaboration) J. Cosm. Astropart. Phys. 6 008
  22. Indik N et al. 2018 Phys. Rev. D 97 124008
  23. Owen B et al. 1999 Phys. Rev. D 60 022002
  24. Usman S et al. 2016 Class. Quant. Grav. 33 215004
  25. Adams T et al. 2016 Class. Quant. Grav. 33 175012
  26. Klimenko S et al. 2008 Class. Quant. Grav. 25 114029
  27. Klimenko S et al. 2016 Phys. Rev. D 93 042004
  28. Sylvestre J et al. 2002 Phys. Rev. D 66 102004
  29. Chatterji S et al. 2004 Class. Quant. Grav. 21 S1809
  30. George D and Huerta E 2018 Phys. Rev. D 97 044039
  31. Gabbard H et al. 2018 Phys. Rev. Lett. 120 141103
  32. Razzano M et al. 2018 Class. Quantum Grav. 35 095016
  33. George D and Huerta E 2018 Phys. Lett. B 778 64
  34. Russo M 2016 Swarm. Evo. Comp. 27 145
  35. Russo M 2020 Soft Comp. 24 16885
  36. Russo M et al. 2014 Solar Energy 105 264
  37. Buccheri E, Dell’Aquila D and Russo M 2021 Diab. Res. Clin. Pract. 174 108722
  38. Buccheri E, Dell’Aquila D and Russo M 2022 Obes. Medicine 31 100398
  39. Dell’Aquila D and Russo M 2021 Comp. Phys. Comm. 259 107667
  40. Husa S et al. 2016 Phys. Rev. D 93 044006
  41. Khan S et al. 2016 Phys. Rev. D 93 044007
  42. Beritelli F, Casale S and Cavallaro A 1997 Electron. Lett. 33 1846
  43. Beritelli F, Casale S and Cavallaro A 1998 IEEE J. Sel. Areas Comm. 16 1818
  44. Beritelli F, Casale S and Russo M 1999 Int. J. of Patt. Rec. Artif. Int. 13 109–132
  45. LIGO Scientific Collaboration 2018 LIGO Algorithm Library - LALSuite free software (GPL)
  46. Giannakopoulos T 2015 PloS one 10
  47. Rabiner R and Schafer R 2010 Theory and Applications of Digital Speech Processing (United States: Prentice Hall Press)
  48. Badak S et al. 2013 Phys. Rev. D 87 024033
  49. Blanchet L 1998 Class. Quant. Grav. 15 1971
  50. Blanchet et al. 2002 Phys. Rev. D 65 061501(R)
  51. Blanchet et al. 2004 Phys. Rev. Lett. 93 091101
  52. Pretorius F 2005 Class. Quantum Grav. 22 425
  53. Baker J G et al. 2006 Phys. Rev. Lett. 96 111102
  54. Campanelli M et al. 2006 Phys. Rev. D 96 111101
  55. Kidder L E 2008 Phys. Rev. D 77 044016
Citations (3)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now