Nonlinear time series analysis of the light curves from the black hole system GRS1915+105

GRS 1915+105 is a prominent black hole system exhibiting variability over a wide range of time scales and its observed light curves have been classified into 12 temporal states. Here we undertake a complete analysis of these light curves from all the states using various quantifiers from nonlinear time series analysis, such as the correlation dimension (D2), the correlation entropy (K2), singular value decomposition (SVD) and the multifractal spectrum (f(α) spectrum). An important aspect of our analysis is that, for estimating these quantifiers, we use algorithmic schemes which we have recently proposed and successfully tested on synthetic as well as practical time series from various fields. Though the schemes are based on the conventional delay embedding technique, they are automated so that the above quantitative measures can be computed using conditions prescribed by the algorithm and without any intermediate subjective analysis. We show that nearly half of the 12 temporal states exhibit deviation from randomness and their complex temporal behavior could be approximated by a few (three or four) coupled ordinary nonlinear differential equations. These results could be important for a better understanding of the processes that generate the light curves and hence for modeling the temporal behavior of such complex systems. To our knowledge, this is the first complete analysis of an astrophysical object (let alone a black hole system) using various techniques from nonlinear dynamics.

[1]  Henry D. I. Abarbanel,et al.  Analysis of Observed Chaotic Data , 1995 .

[2]  Dariusz M Plewczynski,et al.  Influence of colored noise on chaotic systems. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[3]  Kennel,et al.  Method to distinguish possible chaos from colored noise and to determine embedding parameters. , 1992, Physical review. A, Atomic, molecular, and optical physics.

[4]  H. Kantz,et al.  Nonlinear time series analysis , 1997 .

[5]  Wiedenmann,et al.  Determination of f( alpha ) for a limited random point set. , 1989, Physical review. A, General physics.

[6]  P. Sailhac,et al.  Nonlinear and multifractal approaches of the geomagnetic field , 1999 .

[7]  G. P. Pavlos,et al.  SVD analysis of the magnetospheric AE index time series and comparison with low-dimensional chaotic dynamics , 2001 .

[8]  D. Kugiumtzis,et al.  Test your surrogate data before you test for nonlinearity. , 1999, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[9]  James Theiler,et al.  Testing for nonlinearity in time series: the method of surrogate data , 1992 .

[10]  Jensen,et al.  Erratum: Fractal measures and their singularities: The characterization of strange sets , 1986, Physical review. A, General physics.

[11]  P. Grassberger,et al.  Characterization of Strange Attractors , 1983 .

[12]  Holger Kantz,et al.  Practical implementation of nonlinear time series methods: The TISEAN package. , 1998, Chaos.

[13]  Harald Atmanspacher,et al.  Deterministic chaos in accreting systems - analysis of the x-ray variability of Hercules X-1 , 1987 .

[14]  Schreiber,et al.  Improved Surrogate Data for Nonlinearity Tests. , 1996, Physical review letters.

[15]  T. Belloni,et al.  A model-independent analysis of the variability of GRS 1915+105 , 2000 .

[16]  Kurths,et al.  Linear and nonlinear time series analysis of the black hole candidate cygnus X-1 , 1999, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[17]  I. Papadakis,et al.  Characterizing black hole variability with nonlinear methods: the case of the X-ray Nova 4U 1543-47 , 2009, 0912.4676.

[18]  Michael J. Rycroft Nonlinear Time Series Analysis , 2000 .

[19]  R. E. Amritkar,et al.  Computing the multifractal spectrum from time series: an algorithmic approach. , 2009, Chaos.

[20]  J. Schweitzer,et al.  Nonlinear Time Series Analysis of the DB White Dwarf PG 1351+489 Light Intensity Curves , 2005 .

[21]  SEARCH FOR CHAOS IN NEUTRON STAR SYSTEMS: IS Cyg X-3 A BLACK HOLE? , 2009 .

[22]  G. P. King,et al.  Extracting qualitative dynamics from experimental data , 1986 .

[23]  Robert C. Hilborn,et al.  Chaos and Nonlinear Dynamics , 2000 .

[24]  Jay P. Norris,et al.  Is hercules X-1 a strange attractor? , 1989 .

[25]  T. Schreiber,et al.  Surrogate time series , 1999, chao-dyn/9909037.

[26]  The Chaotic Behavior of the Black Hole System GRS 1915+105 , 2004, astro-ph/0403144.

[27]  E. Ott Chaos in Dynamical Systems: Contents , 1993 .

[28]  G. Ambika,et al.  The Nonlinear Behavior of the Black Hole System GRS 1915+105 , 2006 .

[29]  G. Ambika,et al.  A non subjective approach to the GP algorithm for analysing noisy time series , 2006 .

[30]  J. Sprott Chaos and time-series analysis , 2001 .

[31]  University of Leicester,et al.  Non‐linear X‐ray variability in X‐ray binaries and active galaxies , 2005 .

[32]  MPE,et al.  Correlated spectral and temporal changes in 3C 390.3: a new link between AGN and Galactic black hole binaries? , 2006 .

[33]  Parametric characterisation of a chaotic attractor using the two scale Cantor measure , 2009, 0901.3187.

[34]  K. P. Harikrishnan,et al.  Combined use of correlation dimension and entropy as discriminating measures for time series analysis , 2009 .

[35]  H. G. E. Hentschel,et al.  The infinite number of generalized dimensions of fractals and strange attractors , 1983 .

[36]  T. Schreiber Interdisciplinary application of nonlinear time series methods , 1998, chao-dyn/9807001.