Parametric, nonparametric and parametric modelling of a chaotic circuit time series

The determination of a differential equation underlying a measured time series is a frequently arising task in nonlinear time series analysis. In the validation of a proposed model one often faces the dilemma that it is hard to decide whether possible discrepancies between the time series and model output are caused by an inappropriate model or by bad estimates of parameters in a correct type of model, or both. We propose a combination of parametric modelling based on Bock's multiple shooting algorithm and nonparametric modelling based on optimal transformations as a strategy to test proposed models and if rejected suggest and test new ones. We exemplify this strategy on an experimental time series from a chaotic circuit where we obtain an extremely accurate reconstruction of the observed attractor.Comment: 19 pages, 8 Fig

Tempting long-memory - on the interpretation of DFA results

We study the inference of long-range correlations by means of Detrended Fluctuation Analysis (DFA) and argue that power-law scaling of the fluctuation function and thus long-memory may not be assumed a priori but have to be established. This requires the investigation of the local slopes. We account for the variability characteristic for stochastic processes by calculating empirical confidence regions. Comparing a long-memory with a short-memory model shows that the inference of long-range correlations from a finite amount of data by means of DFA is not specific. We remark that scaling cannot be concluded from a straight line fit to the fluctuation function in a log-log representation. Furthermore, we show that a local slope larger than α=0.5 for large scales does not necessarily imply long-memory. We also demonstrate, that it is not valid to conclude from a finite scaling region of the fluctuation function to an equivalent scaling region of the autocorrelation function. Finally, we review DFA results for the Prague temperature data set and show that long-range correlations cannot not be concluded unambiguously

Cessation of X-ray Pulsation of GX 1+4

We report results from our weekly monitoring campaign on the X-ray pulsar GX 1+4 with the {\em Rossi X-ray Timing Explorer} satellite. The spin-down trend of GX 1+4 was continuing, with the pulsar being at its longest period ever measured (about 138.7 s). At the late stage of the campaign, the source entered an extended faint state, when its X-ray (2-60 keV) flux decreased significantly to an average level of $\sim 3 \times 10^{-10} ergs cm^{-2} s^{-1}$. It was highly variable in the faint state; the flux dropped to as low as $\sim 3 \times 10^{-11} ergs cm^{-2} s^{-1}$. In several observations during this period, the X-ray pulsation became undetectable. We can, therefore, conclude conservatively that the pulsed fraction, which is normally $\gtrsim$ 70% (peak-to-peak), must have decreased drastically in those cases. This is very similar to what was observed of GX 1+4 in 1996 when it became similarly faint in X-ray. In fact, the flux at which the cessation of X-ray pulsation first occurred is nearly the same as it was in 1996. We suggest that we have, once again, observed the propeller effect in GX 1+4, a phenomenon that is predicted by theoretical models of accreting X-ray pulsars.Comment: 13 pages, 9 figures (available at http://www.physics.purdue.edu/~cui/ftp/cuifigs.tar.gz). To appear in Ap

Linear and nonlinear time series analysis of the black hole candidate Cygnus X-1

We analyze the variability in the X-ray lightcurves of the black hole candidate Cygnus X-1 by linear and nonlinear time series analysis methods. While a linear model describes the over-all second order properties of the observed data well, surrogate data analysis reveals a significant deviation from linearity. We discuss the relation between shot noise models usually applied to analyze these data and linear stochastic autoregressive models. We debate statistical and interpretational issues of surrogate data testing for the present context. Finally, we suggest a combination of tools from linear andnonlinear time series analysis methods as a procedure to test the predictions of astrophysical models on observed data.Comment: 15 pages, to appear in Phys. Rev.

Phase synchronization from noisy univariate signals

We present methods for detecting phase synchronization of two unidirectionally coupled, self-sustained noisy oscillators from a signal of the driven oscillator alone. One method detects soft, another hard phase locking. Both are applied to the problem of detecting phase synchronization in von Karman vortex flow meters.Comment: 4 pages, 4 figure

Disorder-Induced Shift of Condensation Temperature for Dilute Trapped Bose Gases

We determine the leading shift of the Bose-Einstein condensation temperature for an ultracold dilute atomic gas in a harmonic trap due to weak disorder by treating both a Gaussian and a Lorentzian spatial correlation for the quenched disorder potential. Increasing the correlation length from values much smaller than the geometric mean of the trap scale and the mean particle distance to much larger values leads first to an increase of the positive shift to a maximum at this critical length scale and then to a decrease.Comment: Author information under http://www.theo-phys.uni-essen.de/tp/ags/pelster_di

Correlated X-ray and Optical Variability in Mkn 509

We present results of a 3 year monitoring campaign of the Seyfert 1 galaxy Markarian 509, using X-ray data from the Rossi X-ray Timing Explorer (RXTE) and optical data taken by the SMARTS consortium. Both light curves show significant variations, and are strongly correlated with the optical flux leading the X-ray flux by 15 days. The X-ray power spectrum shows a steep high-frequency slope of -2.0, breaking to a slope of -1.0 at at timescale of 34 days. The lag from optical to X-ray emission is most likely caused by variations in the accretion disk propagating inward.Comment: 13 pages, 3 figures. Accepted for publication in the Astrophysical Journa

Talking quiescence: a rigorous theory that supports parallel composition, action hiding and determinisation

The notion of quiescence - the absence of outputs - is vital in both behavioural modelling and testing theory. Although the need for quiescence was already recognised in the 90s, it has only been treated as a second-class citizen thus far. This paper moves quiescence into the foreground and introduces the notion of quiescent transition systems (QTSs): an extension of regular input-output transition systems (IOTSs) in which quiescence is represented explicitly, via quiescent transitions. Four carefully crafted rules on the use of quiescent transitions ensure that our QTSs naturally capture quiescent behaviour. We present the building blocks for a comprehensive theory on QTSs supporting parallel composition, action hiding and determinisation. In particular, we prove that these operations preserve all the aforementioned rules. Additionally, we provide a way to transform existing IOTSs into QTSs, allowing even IOTSs as input that already contain some quiescent transitions. As an important application, we show how our QTS framework simplifies the fundamental model-based testing theory formalised around ioco.Comment: In Proceedings MBT 2012, arXiv:1202.582

Mass Measurements of AGN from Multi-Lorentzian Models of X-ray Variability. I. Sampling Effects in Theoretical Models of the rms^2-M_BH Correlation

Recent X-ray variability studies suggest that the log of the square of the fractional rms variability amplitude, rms^2, seems to correlate with the log of the AGN black-hole mass, M_BH, with larger black holes being less variable for a fixed time interval. This has motivated the theoretical modeling of the rms^2-M_BH correlation with the aim of constraining AGN masses based on X-ray variability. A viable approach to addressing this problem is to assume an underlying power spectral density with a suitable mass dependence, derive the functional form of the rms^2-M_BH correlation for a given sampling pattern, and investigate whether the result is consistent with the observations. For simplicity, previous studies, inspired by the similarities shared by the timing properties of AGN and X-ray binaries, have explored model power spectral densities characterized by broken power laws. and ignored, in general, the distorting effects that the particular sampling pattern imprints in the observed power spectral density. Motivated by the latest timing results from X-ray binaries, obtained with RXTE, we propose that AGN broad-band noise spectra consist of a small number of Lorentzian components. This assumption allows, for the first time, to fully account for sampling effects in theoretical models of X-ray variability in an analytic manner. We show that, neglecting sampling effects when deriving the fractional rms from the model power spectral density can lead to underestimating it by a factor of up to 80% with respect to its true value for the typical sampling patterns used to monitor AGN. We discuss the implications of our results for the derivation of AGN masses using theoretical models of the rms^2-M_BH correlation. (Abridged)Comment: The Astrophysical Journal, in press, 11 pages, 6 figure

Neuron dynamics in the presence of 1/f noise

Interest in understanding the interplay between noise and the response of a non-linear device cuts across disciplinary boundaries. It is as relevant for unmasking the dynamics of neurons in noisy environments as it is for designing reliable nanoscale logic circuit elements and sensors. Most studies of noise in non-linear devices are limited to either time-correlated noise with a Lorentzian spectrum (of which the white noise is a limiting case) or just white noise. We use analytical theory and numerical simulations to study the impact of the more ubiquitous "natural" noise with a 1/f frequency spectrum. Specifically, we study the impact of the 1/f noise on a leaky integrate and fire model of a neuron. The impact of noise is considered on two quantities of interest to neuron function: The spike count Fano factor and the speed of neuron response to a small step-like stimulus. For the perfect (non-leaky) integrate and fire model, we show that the Fano factor can be expressed as an integral over noise spectrum weighted by a (low pass) filter function. This result elucidates the connection between low frequency noise and disorder in neuron dynamics. We compare our results to experimental data of single neurons in vivo, and show how the 1/f noise model provides much better agreement than the usual approximations based on Lorentzian noise. The low frequency noise, however, complicates the case for information coding scheme based on interspike intervals by introducing variability in the neuron response time. On a positive note, the neuron response time to a step stimulus is, remarkably, nearly optimal in the presence of 1/f noise. An explanation of this effect elucidates how the brain can take advantage of noise to prime a subset of the neurons to respond almost instantly to sudden stimuli.Comment: Phys. Rev. E in pres