Regression of non-linear coupling of noise in LIGO detectors

被引:5
|
作者
Costa, C. F. Da Silva [1 ]
Billman, C. [1 ]
Effler, A. [2 ]
Klimenko, S. [1 ]
Cheng, H-P [1 ]
机构
[1] Univ Florida, Gainesville, FL 32611 USA
[2] LIGO Livingston Observ, Livingston, LA 70754 USA
关键词
gravitational waves; instrumental noise; noise regression;
D O I
10.1088/1361-6382/aaa536
中图分类号
P1 [天文学];
学科分类号
0704 ;
摘要
In 2015, after their upgrade, the advanced Laser Interferometer Gravitational-Wave Observatory (LIGO) detectors started acquiring data. The effort to improve their sensitivity has never stopped since then. The goal to achieve design sensitivity is challenging. Environmental and instrumental noise couple to the detector output with different, linear and non-linear, coupling mechanisms. The noise regression method we use is based on the Wiener-Kolmogorov filter, which uses witness channels to make noise predictions. We present here how this method helped to determine complex non-linear noise couplings in the output mode cleaner and in the mirror suspension system of the LIGO detector.
引用
收藏
页数:12
相关论文
共 50 条
  • [1] NON-LINEAR PROGRAMMING AND NON-LINEAR REGRESSION PROCEDURES
    EDWARDS, C
    JOURNAL OF FARM ECONOMICS, 1962, 44 (01): : 100 - 114
  • [2] Characterizing transient noise in the LIGO detectors
    Nuttall, L. K.
    PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 2018, 376 (2120):
  • [3] Environmental noise in advanced LIGO detectors
    Nguyen, P.
    Schofield, R. M. S.
    Effler, A.
    Austin, C.
    Adya, V
    Ball, M.
    Banagiri, S.
    Banowetz, K.
    Billman, C.
    Blair, C. D.
    Buikema, A.
    Cahillane, C.
    Clara, F.
    Covas, P. B.
    Dalya, G.
    Daniel, C.
    Dawes, B.
    DeRosa, R.
    Dwyer, S. E.
    Frey, R.
    Frolov, V. V.
    Ghirado, D.
    Goetz, E.
    Hardwick, T.
    Helmling-Cornell, A. F.
    Hollows, I. J.
    Kijbunchoo, N.
    Kruk, J.
    Laxen, M.
    Maaske, E.
    Mansell, G. L.
    McCarthy, R.
    Merfeld, K.
    Neunzert, A.
    Palamos, J. R.
    Parker, W.
    Pearlstone, B.
    Pele, A.
    Radkins, H.
    Roma, V
    Savage, R. L.
    Schale, P.
    Shoemaker, D.
    Shoemaker, T.
    Soni, S.
    Talukder, D.
    Tse, M.
    Valdes, G.
    Vidreo, M.
    Vorvick, C.
    CLASSICAL AND QUANTUM GRAVITY, 2021, 38 (14)
  • [4] Diagnostics for non-linear regression
    Castillo, E.
    Hadi, A. S.
    Minguez, R.
    JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, 2009, 79 (09) : 1109 - 1128
  • [5] ITERATED NON-LINEAR REGRESSION
    Svitek, Miroslav
    NEURAL NETWORK WORLD, 2014, 24 (04) : 411 - 420
  • [6] JACKKNIFING IN NON-LINEAR REGRESSION
    FOX, T
    HINKLEY, D
    LARNTZ, K
    TECHNOMETRICS, 1980, 22 (01) : 29 - 33
  • [7] NOISE IN NON-LINEAR RESISTORS
    TAKAGI, K
    JAPANESE JOURNAL OF APPLIED PHYSICS, 1980, 19 (07) : 1353 - 1357
  • [8] ON A NON-LINEAR NOISE PROBLEM
    STUMPERS, FLHM
    PHILIPS RESEARCH REPORTS, 1947, 2 (04): : 241 - 259
  • [9] Regression of environmental noise in LIGO data
    Tiwari, V.
    Drago, M.
    Frolov, V.
    Klimenko, S.
    Mitselmakher, G.
    Necula, V.
    Prodi, G.
    Re, V.
    Salemi, F.
    Vedovato, G.
    Yakushin, I.
    CLASSICAL AND QUANTUM GRAVITY, 2015, 32 (16)
  • [10] Neural networks and non-linear regression
    Stein, WE
    Dattero, R
    Mizzi, PJ
    DECISION SCIENCES INSTITUTE 1998 PROCEEDINGS, VOLS 1-3, 1998, : 1075 - 1077