Classification methods for noise transients in advanced gravitational-wave detectors

Classical and Quantum Gravity

Volumn 32, Issue 21, 2015, Pages

  Powell, Jade ^a   Trifirò, Daniele ^b,c   Cuoco, Elena ^d,e   Heng, Ik Siong ^a   Cavaglià, Marco ^c  

^a UNIVERSITY OF GLASGOW   (United Kingdom)

^b UNIVERSITY OF PISA   (Italy)

^c UNIVERSITY OF MISSISSIPPI   (United States)

^d EUROPEAN GRAVITATIONAL OBSERVATORY EGO   (Italy)

^e INFN SEZIONE DI PISA   (Italy)

Author keywords

detector characterization; gravitational waves; machine learning

Indexed keywords

EID: 84945115236     PISSN: 02649381     EISSN: 13616382     Source Type: Journal    
DOI: 10.1088/0264-9381/32/21/215012     Document Type: Article

References (34)

1. Harry G M and and (LIGO Scientific Collaboration) 2010 Class. Quantum Grav. 27 084006
2. Aasi J et al 2015 Class. Quantum Grav. 32 074001
3. Acernese F et al 2008 Class. Quantum Grav. 25 114045
4. Christensen N (LIGO and Virgo Scientific Collaborations) 2010 Class. Quantum Grav. 27 194010
5. Aasi J et al 2012 Class. Quantum Grav. 29 155002
6. Abadie J et al 2012 Phys. Rev. D 85 122007
7. Abadie J et al 2010 Class. Quantum Grav. 27 173001
8. Aasi J et al (The LIGO Scientific Collaboration, The Virgo Collaboration) 2014 arxiv:1410.7764
9. Mukherjee S, Obaid R and Matkarimov B 2010 J. Phys.: Conf. Ser. 243 012006
10. Essick R, Vitale S, Katsavounidis E, Vedovato G and Klimenko S 2015 Astrophys. J. 800 81
11. Acernese F et al 2007 Class. Quantum Grav. 24 S671
12. Prete M D (the Virgo and the LSC Collaboration) 2009 Class. Quantum Grav. 26 204022
13. Jackson J 2003 A User's Guide to Principal Components (Wiley Series in Probability and Mathematical Statistics: Applied Probability and Statistics) (New York: Wiley) ISBN 9780471471349 (http://books.google.com/books?id=0INHj28qCBUC)
14. Calder A J, Burton A, Miller P, Young A W and Akamatsu S 2001 Vis. Res. 41 1179-208
15. Bishop C M 2006 Pattern Recognition and Machine Learning (Information Science and Statistics) (Secaucus, NJ, USA: Springer) ISBN 0387310738
16. Lewicki M S 1998 Netw., Comput. Neural Syst. 9 R53
17. Allen B, Anderson W G, Brady P R, Brown D A and Creighton J D E 2005 arXiv:gr-qc/0509116v2 arXiv.org 122006
18. Pedregosa F et al 2011 J. Mach. Learn. Res. 12 2825-30
19. Bhat H and Kumar N 2010 On the derivation of the Bayesian information criterion Technical Report (http://nscs00.ucmerced.edu/∼nkumar4/BhatKumarBIC.pdf)
20. Hastie T and Tibshirani R J F 2001 The Elements of Statistical Learning (Berlin: Springer)
21. Veitch J et al 2015 Phys. Rev. 91 042003 D
22. Sivia D S 1996 Data Analysis, A Bayesian Tutorial (Oxford: Oxford Univ. Press)
23. Logue J, Ott C D, Heng I S, Kalmus P and Scargill J H C 2012 Phys. Rev. D 86 044023
24. Heng I S 2009 Class. Quantum Grav. 26 105005
25. Veitch J and Vecchio A 2010 Phys. Rev. D 81 062003
26. Acernese F et al 2005 Class. Quantum Grav. 22 S1041
27. Daubechies I et al 1992 Ten Lectures on Wavelets vol 61 (Philadelphia: SIAM)
28. Mallat S 1998 A Wavelet Tour of signal Processing (New York: Academic)
29. Cuoco E, Calamai G, Fabbroni L, Losurdo G, Mazzoni M, Stanga R and Vetrano F 2001 Class. Quantum Grav. 18 1727
30. Donoho D L and Johnstone J M 1994 Biometrika 81 425-55
31. Abu-Mostafa Y S, Magdon-Ismail M and Lin H T 2012 Learning from Data
32. Biswas R et al 2013 Phys. Rev. D 88 062003
33. Essick R, Blackburn L and Katsavounidis E 2013 Class. Quantum Grav. 30 155010
34. Frey B J and Dueck D 2007 Science 315 972-6