Issue 
A&A
Volume 556, August 2013



Article Number  A77  
Number of page(s)  12  
Section  Numerical methods and codes  
DOI  https://doi.org/10.1051/00046361/201220339  
Published online  31 July 2013 
Hotspot model for accretion disc variability as random process
II. Mathematics of the powerspectrum break frequency
^{1} Astronomical Institute, Academy of Sciences, Boční II 1401, 14131 Prague, Czech Republic
email: pechacek@ig.cas.cz
^{2} Observatoire Astronomique de Strasbourg, 67000 Strasbourg, France
^{3} Copernicus Astronomical Center, Bartycka 18, 00716 Warsaw, Poland
Received: 5 September 2012
Accepted: 10 June 2013
Context. We study some general properties of accretion disc variability in the context of stationary random processes. In particular, we are interested in mathematical constraints that can be imposed on the functional form of the Fourier powerspectrum density (PSD) that exhibits a multiply broken shape and several local maxima.
Aims. We develop a methodology for determining the regions of the model parameter space that can in principle reproduce a PSD shape with a given number and position of local peaks and breaks of the PSD slope. Given the vast space of possible parameters, it is an important requirement that the method is fast in estimating the PSD shape for a given parameter set of the model.
Methods. We generated and discuss the theoretical PSD profiles of a shotnoisetype random process with exponentially decaying flares. Then we determined conditions under which one, two, or more breaks or local maxima occur in the PSD. We calculated positions of these features and determined the changing slope of the model PSD. Furthermore, we considered the influence of the modulation by the orbital motion for a variability pattern assumed to result from an orbitingspot model.
Results. We suggest that our general methodology can be useful for describing nonmonotonic PSD profiles (such as the trend seen, on different scales, in exemplary cases of the highmass Xray binary Cygnus X1 and the narrowline Seyfert galaxy Ark 564). We adopt a model where these power spectra are reproduced as a superposition of several Lorentzians with varying amplitudes in the Xrayband light curve. Our general approach can help in constraining the model parameters and in determining which parts of the parameter space are accessible under various circumstances.
Key words: accretion, accretion disks / black hole physics / galaxies: active / Xrays: binaries
© ESO, 2013
1. Introduction
Accretion discs are believed to drive the variability of supermassive black holes in active galactic nuclei (AGN) and, on much smaller timescales, also the fluctuating signal from Galactic black holes in accreting compact binaries (Vio et al. 1992; Nowak et al. 1999; McHardy et al. 2006). Observed light curves, f ≡ f(t), exhibit irregular, featureless variations at all frequencies that can be studied (Lawrence et al. 1987; McHardy & Czerny 1987; Gaskell et al. 2006). Nonetheless, accretion is the common agent (Frank et al. 2002).
Simplified models of accretion disc processes do not explain every aspect of variability of different sources. Phenomenological studies also show a considerable complexity of the structure of accretion flows. Accretion discs coexist with their coronae and possibly winds and jets, which produce a highly unsteady contribution, and so the observed signal of the source is determined by the mutual interaction of different components (Done et al. 2007). In this paper we study Xray variability features that can be attributed to stochastic flare events that are connected to the accretion disc. Our aim is to constrain the resulting variability using a very general scheme.
Random processes provide a suitable framework for a mathematical description of the measurements of a physical quantity that is subject to nondeterministic evolution, for example due to noise disturbances or because of the intrinsic nature of the underlying mechanism. In our previous paper (Pecháček et al. 2008, Paper I) we studied the theory of random processes as a tool for describing the fluctuating signal from accreting sources, such as AGN and Galactic black holes observed in Xrays. These objects exhibit a featureless variability on different timescales, probably originating from an accretion disc surface and its corona. In particular, we explored a scenario where the expected signal is generated by an ensemble of spots randomly created on the accretion disc surface, orbiting with Keplerian velocity at each corresponding radius (Galeev et al. 1979; Czerny et al. 2004, and references therein).
Various characteristics can be constructed from the light curve of an astronomical object to study its variability properties (Feigelson & Babu 1992). The standard approach is to analyse the light curve in terms of its power spectral density (PSD), which provides the variability power as a function of frequency (Vaughan & Uttley 2008). The histogram of the flux distribution measures the timescale that the source spends at a given flux level. Furthermore, the rootmeansquare (rms) scatter can be computed for different sections of the light curve that correspond to different flux levels. This establishes the rmsflux relation (Uttley & McHardy 2001).
In this context an important reference to our work is given by Uttley et al. (2005), where the authors presented a model to explain the observed linear rmsflux relations that are derived from Xray observations of accreting black hole systems. The model generates light curves from an exponential applied to a Gaussian noise, which by construction produces a lognormal flux distribution consistent with the observed Xray flux histograms of the Galactic black hole binary CygX1, as well as of other sources. Uttley et al. (2005) concluded that the observation of a lognormal flux distribution and the linear rmsflux relationship imply that the underlying variability process cannot be additive. Indeed, this statement holds for a large family of shotnoise variability models and it was extended to other sources in different spectral states (Arévalo & Uttley 2006), also in the optical band (Gandhi 2009).
As mentioned above, in this paper we concentrate on characteristics of the PSD profile, in particular on the constraints that can be derived from the PSD slope and the occurrence of break frequencies. Because power spectra do not provide complete information, even a perfect agreement between the predicted PSD and the data cannot be considered as a definitive confirmation of the scenario. We do not study additional constraints, such as the rmsflux relation or time lags between different bands, which certainly have a great potential of distinguishing between different schemes. Indeed, these characteristics provide complementary pieces of information that may be found incompatible with the spot model. In other words, the model presented here is testable, at least in principle, and it will be possible to confirm the model or reject it by observations.
Although wellknown ambiguities are inherent to Fourier power spectra, an ongoing debate is held about the shape of the PSD and the dominant features that could reveal important information about the origin of the variability. The broadband PSD has been widely employed to examine the fluctuating Xray light curves, often showing a tendency towards flattening at low frequencies (Lawrence & Papadakis 1993; Mushotzky et al. 1993; Uttley et al. 2002). PSDs of interest for us are characterized by a powerlaw form of the Fourier power spectrum ωS(ω), which decays at the highfrequency part proportionally to the first power of ω. A break occurs at lower frequencies, where the slope of the powerlaw changes. The profile of the power spectrum can be even more complex, showing multiple breaks or even local peaks separated by minima of the PSD curve.
The occurrence of a break frequency has been discussed by various authors (e.g. Nowak et al. 1999; Markowitz et al. 2003) for its obvious relevance for understanding observed PSDs and interpreting the underlying mechanism that causes the slope change. In this context, McHardy et al. (2005) have examined the case of the Seyfert 1 galaxy MCG–63015 with RXTE and XMMNewton data. Very accurate fits suggest a multiLorentzian structure instead of a simple powerlaw. Furthermore, McHardy et al. (2007), combining the XMMNewton, RXTE and ASCA observations, discovered multiple Lorentzian components in the Xray timing properties extending over almost seven decades of frequency (10^{8} ≲ f ≲ 10^{2} Hz) of the narrowline Seyfert 1 galaxy Ark 564. These authors concluded that the evidence points to two discrete, localized regions as the origin of most of the variability. Mushotzky et al. (2011) discussed the AGN optical light curves monitoring by Kepler. They found, rather surprisingly, that the PSD exhibits powerlaw slopes of − 2.6 to − 3.3, i.e., significantly steeper than typically seen in the Xrays. Furthermore, within the category of stellarmass black holes, Pottschmidt et al. (2003) analysed Cygnus X1 PSD from about three years of RXTE monitoring. These authors concluded that the changing form of the power spectrum can be interpreted as a combination of Lorentzians with gradually evolving amplitudes.
In this paper, motivated by this and similar observational evidence, we embark on a mathematical study of PSD that can be fitted either with a broken powerlaw or a doublebending power law, with the variability power ω S(ω) dropping at low and high frequencies, respectively. We consider a simplified (but still nontrivial) description of the intrinsic variability (random flares with idealized light curve profiles of the individual events), which allows us to develop an analytical approach to the resulting observable PSD profile. In particular, we determine conditions under which one, two, or more breaks or local maxima occur in the PSD. We calculate positions of these features and determine the changing slope of the model PSD.
The paper is organised as follows. In Sect. 2 we discuss the PSDs generated by a shotnoiselike random process with exponentially decaying flares. We developed a general approach to describe the morphology of the power spectra and demonstrate the results on a simple, analytically solvable example of a biLorentzian PSD. In Sect. 4 we consider the influence of the modulation by the orbital motion on the PSDs. We summarise our conclusions and give perspectives for the application of this fast, analytical modelling scheme in Sect. 5.
2. Exponential flares
It is well known that the power spectra of stationary random processes must be flat at zero (S(ω) ∝ ω^{0}) and integrable over all frequencies (i.e. decaying faster than ω^{1} for large ω). Moreover, it was demonstrated for the multiflare model (Pecháček et al. 2008) that the highfrequency limit of the PSD is a powerlaw, S(ω) ∝ ω^{γ}, with the slope γ < − 1 determined by the intrinsic shape of the flareemission profile. The low frequency limit depends mainly on the statistics of the flaregenerating process. The form of the intermediate part of the power spectrum is influenced by the interplay of all of these effects. The simplest possible power spectra generated by the flare model are approximately of the broken powerlaw form.
It is natural to assume that the power spectrum breakfrequency is associated with some intrinsic timescale of the process. This heuristic approach is supported by the basic scaling properties of the Fourier transform: rescaling the process in temporal domain by a factor a shifts the break frequency by a^{1}. The powerlaw PSDs are invariant under this rescaling, and the break frequency is the only feature of the power spectra whose position changes. However, we will see that for two reasons it is difficult to go beyond this trivial observation.
Rigorous definition of the break frequency can be a problem. Even for the simplest acceptable featureless power spectra such as a Lorentzian PSD, which are powerlaws to high accuracy for most of frequencies, the change of slope occurs smoothly over a long interval. It is therefore not obvious which frequency should be defined as the break frequency, and even if we decide on some particular definition, various problems of interpretation may emerge.
In this introductory section, we constrain our discussion to mutually independent exponentially decaying flares with emissivity profiles I(t,τ) = I_{0}(τ)exp( − t / τ) θ(t), where θ(t) is the Heaviside function and τ is the lifetime of individual flares. Exponentials are well suited for our investigations because of their simple, featureless power spectra. Moreover, there is a timescale τ naturally and uniquely associated with each flare. This allows us to relate the break frequencies to the ensemble averages over the range of τ.
Fig. 1 Left panel: diagram describing the morphology of the PSD profiles according to Eq. (4) for all possible values of the parameters α and K. Different curves represent the boundaries of subsets of the parameters for which the PSD exhibits a doublefeature. The red curve encloses set C_{M} of the doubly peaked power spectra. The other curves correspond to the sets of power spectra with a nontrivial structure of inflection points C_{I0} (blue), C_{I1} (magenta) and C_{Iq} (green). Right panel: three examples of the PSD profiles. A doublepeaked power spectrum (red) with parameters α = 0.94 and K = 0.01 (i.e., the values within set C_{M}, as denoted by the red point in the left panel), doublybroken power spectrum with a single local maximum (magenta) corresponding to α = 0.85 and K = 0.08 (the magenta point within set C_{I1} and outside of C_{M}) and a power spectrum with single maximum and break (green) with α = 0.5 and K = 0.5 (the green point outside of C_{Iq}). 

Open with DEXTER 
To simplify the problem even more, we do not take into account the relativistic effects in the rest of this section. The power spectrum of a random shotnoiselike process consisting of mutually independent exponentially decaying flares is given by (1)where λ is the mean rate of flares and p(τ) is the probability distribution of τ. In the case of identical emissivity profiles, i.e. with p_{L}(τ) = δ(τ − τ_{0}), the power spectrum is a pure Lorentzian, (2)Even this simple model can reproduce a wide variety of power spectra. In the context of the variability of the Xray sources the model was introduced by Lehto (1989).
It is traditional to plot the PSDs in ω versus ωS(ω) graphs. It can be easily shown that the function ωS_{L}(ω) reaches its maximum at , as one can intuitively expect. In the general case the position of this maximum depends on the actual shape of the functions p(τ) and I_{0}(τ). Since the power spectrum is a nonlinear functional of the signal, the peak frequency differs from both E[τ]^{1} and , where E[.] denotes the averaging operator.
To demonstrate this nonlinear behaviour we will next study a process that consists of exponentials with only two different timescales, i.e. a process with p(τ) = Aδ(τ − τ_{1}) + (1 − A)δ(τ − τ_{2}), where A is some constant from the interval ⟨ 0,1 ⟩ and τ_{1} > τ_{2}. The resulting power spectrum is (3)As long as we are interested only in the process timescales, the PSD normalization is irrelevant. Without loss of generality we can rescale the power spectrum both in normalization and in frequency domain as (4)Both K and α are from the interval ⟨ 0,1 ⟩. The peak frequency ω_{M} is given by the equation (5)which leads to the bicubical equation (6)where .
Fig. 2 Left: comparison of the timescales τ_{M} (red) and E[τ] (magenta). The lines are calculated for α from the set {0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8}, avoiding set C_{M}. Middle: as in the left panel, but for α = 0.9. For low K this corresponds to doublepeaked power spectra. The red curves correspond to 1 over the peak frequency, the green is for the minimum. The blue curve denotes the timescale 1 / E[1 / τ]. Right: comparison of the timescales τ_{M} (red) and 1 / E[1 / τ] (blue). 

Open with DEXTER 
Equation (6) can have either one or three positive real roots. For most of the combinations of parameters α and K the root is unique and the corresponding power spectrum has a single global maximum. Three roots do occur only for parameters within a small subset of the configuration space C_{M}. The corresponding power spectra have two local maxima separated by a local minimum. The boundary of set C_{M} is denoted by the red line in Fig. 1. Note that after rescaling the intrinsic timescales of the two exponentials are 1 and K. The mean timescale and the mean frequency of the process are then given by However, the break timescale τ_{M} = 1 / ω_{M} depends nonlinearly on both α and K. The comparison of these three quantities is plotted in the Fig. 2. We can see that the displacement of the actual peak position and the two linearly averaged timescales can be very significant.
Note that our formulae are expressed and graphs are labelled in arbitrary (dimensionless) units. When the underlying mechanism is interpreted in terms of flaring events in a black hole accretion disc, the meaning of these units can be identified with geometrical units. Corresponding quantities in physical units can be obtained by a simple conversion: (9)for the physical mass in grams, and (10)for the frequency in Hertz. Frequencies scale with the central mass proportionally to M^{1}; their numerical values are therefore converted in the physical units by multiplying by the factor (11)For the black hole in Cyg X1 the current mass estimate (Orosz et al. 2011) reads M^{phys} / M_{⊙} = 14.8 ± 1.
From Figs. 1, 2 we learn that two peaks occur within only a very narrow parameter range. It is even more difficult to produce two peaks of similar height in the ω S(ω) plot. The condition is that two small numbers, K and 1 − α have to be equal. For example, in the hard state of Cyg X1 the two most prominent Lorentzians are separated by about two decades in frequency, and they are of identical height, which means that in such a state K ≈ 0.01 and 1 − α = 0.01. Hence, the two quantities must be strongly correlated. Even if the two Lorentzians arise in different regions, the oscillations have to be physically linked. This mathematical statement is thus important and has to be taken into account in any attempt to explain the nature of the two mechanisms. Naturally, one has to bear in mind that all our conclusions are based on a simplified scenario; a more complicated (astrophysically realistic) model will very likely contain more parameters, which will bring additional degrees of freedom.
We have demonstrated that the power spectra S_{r}(ω) are doublepeaked only for pairs of α and K within a small area of the parameter space. However, this does not mean that singlepeaked power spectra are lacking any internal structure. Unlike the local maximum, it is not obvious how to formalize the presence of the internal structure into equations. The simplest continuation of the ideas above is to calculate inflection points of ωS(ω), (12)Apparently, Eq. (12) has also either one or three positive real solutions.
We have been investigating the local maxima of ωS(ω), because the power spectra S(ω) of a shotnoiselike processes are peaked at zero and because frequency times power versus frequency plots are traditionally used to analyse of astronomical signals. There is, however, no good a priori reason to prefer the inflection points of ωS(ω) over inflection points of S(ω) itself, (13)The regions C_{I0} and C_{I1} denote areas of the parameter space that exhibit more than one inflection point of S_{r}(ω), respectively ωS_{r}(ω). The critical points at their boundaries are connected by blue, and by magenta curve in the Fig. 1. These two areas clearly do not coincide, because the multiplication of the power spectra by ω can in some cases remove or create new inflection points. Inspired by the properties of the log–log plotting of power spectra, we can define the local powerlaw index of the PSD q(ω) and associate the internal structure of the power spectra with the presence of inflection points, The part of the parameter space corresponding to power spectra with more than one inflection point of q(ω) are within set C_{Iq} enclosed by the green curve in Fig. 1, which shows the positions of the inflection points of the three different types for a selected set of αs and a whole range of K.
Fig. 3 Timescales τ_{I0} (left), τ_{I1} (middle) and τ_{Iq} (right) calculated for the PSD given by Eq. (4). We assumed the values of α from the set { 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 }, and K from the interval ⟨ 0,1 ⟩. 

Open with DEXTER 
Fig. 4 Left: function g(x) (Eq. (19)) for the power spectrum (4), calculated for α = 0.95, and K = 0.01 (red) and K = 0.2 (blue), respectively. The green line represents the identity function g(x) = x. Right: the corresponding plot of timescales, similar to the right panel of Fig. 2. The lightblue lines correspond to the limits from the inequality (21). 

Open with DEXTER 
2.1. A more general distribution of flare profiles
We now investigate a more general case with an arbitrary probability distribution p(τ). First we observe that the normalization function I_{0}(τ) has a similar influence on the resulting power spectrum as p(τ) itself. Therefore, we can without loss of generality study the power spectrum (16)where .
Without any loss of generality we can set λ = [S(0)] ^{1}. This rescaling does not change the overall shape of the PSD and it normalises to unity. Differentiating ωS(ω) from Eq. (16) leads to (17)For the peak frequency we find due to linearity of the averaging operator E [.], (18)Now we substitute x_{M} for , define a function g(x) as (19)and observe that the position of the local extreme is determined by the intersection of g(x) with x: (20)It follows from the Jensen inequality that the derivative of g is always nonnegative (see the Appendix). Therefore, the position of x_{M} is constrained to the interval ⟨ g(0),g(∞) ⟩. Straightforwardly, . To calculate the upper limit we observe that 1 / (1 + τ^{2}x) ∝ τ^{2} for τ^{2} ≫ x^{1}. Assuming that there is a nonzero lowest timescale τ_{min} > 0 so that p(τ) = 0 for t < τ_{min}, we can replace the Lorentzian in (19) by τ^{2} and put^{1}. For the break timescale we finally find the inequality (21)Figure 4 illustrates this again in terms of the power spectrum (4). Although g(x) is necessarily a nondecreasing function, the intersection g(x) = x needs not to be unique. The inequality (21) then holds for all local maxima and minima of the PSD.
We now further investigate properties of the function g(x). Equation (20) states that the extrema of the PSD are fixed points of g(x). We can define the nth iteration of g(x) by recurrent relations, (22)It trivially follows from Eq. (20) that the local extrema x_{M} are also fixed points of g^{(n)}(x) for all values of n. Assuming distributions with finite moments g(0) and g(∞), the function g(x) maps the real halfline ⟨ 0,∞ ⟩ into the finite interval ⟨ g(0),g(∞) ⟩. From the monotonicity of g, it then follows that g maps the latter interval within itself.
Fig. 5 Top left: boundaries of approximating sets for the power spectrum S_{r}(ω). The function g(x) was evaluated in L points, evenly placed in between g(0) and g(∞). The nested curves correspond to calculated for L equal to 4, 5, 9, and 49. Top right: the nested sets calculated from the upper limit of g′(x). We have taken x_{+} = g^{(n)}(0) and x_{−} = g^{(n)}(∞) for n from 1 to 4. Bottom left: the same as in the top left panel for sets . Bottom right: the same as in the bottom left panel, this time with a grid of points that samples the area around point 3h(0) more densely but that does not cover the whole interval ⟨ 3h(0),3h(∞) ⟩. The values of L are the same as in the previous examples. The approximating sets cover a broader part of the whole set . However, they omit a small area of high α. This demonstrates that an adaptive grid can perform significantly better than a regular one. 

Open with DEXTER 
By repeated applications of g we obtain an infinite sequence of nested inequalities, Because of the BolzanoWeierstrass theorem, the following limits exist and the same inequality holds for them as well, Both g^{(∞)}(0) and g^{(∞)}(0) are fixed points of g(x) and correspond to the lowest and highest local maxima of ωS(ω), respectively. If they coincide, the power spectrum has a single peak.
The inflection points of the power spectrum can be investigated in a similar way. Defining a function (28)it can be easily proven that As we show in the Appendix, h(x) is also a nondecreasing function. Therefore, h(x) has properties similar to g(x), with h(0) = E [τ^{2}] / E [τ^{4}], h(∞) = E [τ^{4}] / E [τ^{2}] and with a similar structure of nested subintervals, (31)There are several applications of the previous relations. Clearly, finding the fixed points of g and h numerically is as hard as finding the extrema and inflection points of S(ω) directly. But by calculating g(0), h(0), g(∞) and h(∞), we can roughly estimate the position of each feature. Every application of these functions requires calculating two expected values of some functions of τ. It was demonstrated in the case of two Lorentzians that these limits are sometimes too wide. In this case we can always determine a tighter constraint by applying g and h, respectively, on the result at the cost of calculating two additional expected values per each iteration. Moreover, we can use g and h to find a constraint on the numbers of local extrema and inflection points.
In the simple case of two Lorentzians we were able to calculate directly for which subsets of the parameter space of α and K is the PSD doublypeaked and doublybroken. Let us call these sets C_{M}, C_{I0}, C_{I1}, and C_{Iq}. The parameter space of the general case with an arbitrary probability function is infinitedimensional. In analogy with the twodimensional case of S_{r}(ω) we can define a subsets of the space of all probability distribution functions , such that the power spectrum (16) has multiple local extrema x_{M} if, and only if, .
Testing if a particular function is contained in means calculating the power spectrum directly. However, using the function g(x), we can construct approximation sets and representing the necessary and sufficient conditions on to produce a multiply peaked power spectrum, . Using g and h, the approximating sets can be defined by an inequality, or a set of inequalities between some moments of τ. We now present two examples of such constructions and apply them to the example of the PSD (4).
2.2. Examples of the construction procedure
We take x_{+} and x_{−} such that x_{+} < x_{M} < x_{−} for all fixed points g(x_{M}) = x_{M}. By construction, g(x_{+}) > x_{+} and g(x_{−}) < x_{−}. If there is more than one fixed point x_{M}, the function s_{g}(x) = g(x) − x changes its sign in the interval ⟨ x_{+},x_{−} ⟩ more than once. We can pick L values x_{j} from the interval such that x_{+} < x_{1} < ··· < x_{L} < x_{−}. If the sequence of s_{g}(x_{j}) changes sign more than once, the fixed point is not unique. Figure 5 shows the application of the test on the power spectrum (4) for x_{+} = g(0), x_{−} = g(∞) and x_{j} placed uniformly over the whole interval. The results are plotted for L equal to 4, 5, 9 and 50. Note that this is not the only possible choice of the x. All x_{+} = g^{(n)}(0) and x_{−} = g^{(n)}(∞) are admissible, and likewise, x_{j} can be chosen in some irregular or even adaptive way by using first L steps of some numerical rootfinding method (e.g. the regula falsi method). What matters is the final complexity of the test. It produces a set of L inequalities that must be satisfied by to be in .
For set we can use a different approach. The wellknown Banach fixedpoint theorem says that if the function g is a contracting mapping, i.e. if there exists a number 0 ≤ λ < 1 such that  g(x) − g(y)  < λ  x − y  for all admissible x and y, then the fixedpoint x_{M} is unique. Because g(x) is a continuous and nondecreasing function, it follows from the mean value theorem that it is a contracting mapping if the maximum of its derivative is lower than one, g′(x) < 1. Defining a new function ℓ(x) as (32)we can write the derivative of g, (33)Similarly to g and h, also ℓ(x) is a nondecreasing function that can be expressed in terms of the functions g and h, (34)Because the function g maps the whole semiaxis ⟨ 0,∞ ⟩ into ⟨ g(0),g(∞) ⟩ it is sufficient to investigate the derivative g′(x) only on the latter interval. Because g, h, and ℓ are nondecreasing functions, the following inequality holds for all x_{+}, x_{−} and y that satisfy g(0) ≤ x_{+} ≤ y ≤ x_{−} ≤ g(∞), (35)Using this inequality, we can take for the set of all distributions for which D_{max}(x_{+},x_{−}) > 1. Figure 5 demonstrates this on the biLorentzian power spectrum. We employed x_{+} = g^{(n)}(0) and x_{−} = g^{(n)}(∞) with n from 1 to 4. The margins derived from this criterion can be relatively wide, both because D_{max}(x_{+},x_{−}) overestimates the maximum of g′(x), and also because some functions g(x) can have a single fixed point x_{M} and a derivative g′(y) > 1 (y ≠ x_{M}) at the same time. On the other hand, if D_{max}(x_{+},x_{−}) < 1, g(x) surely exhibits a single fixed point.
Unfortunately, the analysis of inflection points of the local powerlaw slope cannot be generalized in the same way. However, we note that q(ω) is related to g by(36)From here we can immediately find that q(0) = 0 and q(∞) = − 2. Furthermore, from (20) we see that q(ω_{M}) = − 1 and conversely, by expressing g in terms of q, we can see that all ω with q(ω) = − 1 are the fixed points of g. This is not surprising since in the vicinity of the local extreme (ω_{M} + y)S(ω_{M} + y) = const. + o(y). It is, however, a good motivation to briefly investigate the behaviour of the function ω^{γ}S(ω) in terms of its local extrema ω_{Mγ} and inflectionpoints ω_{Iγ}, (37)Following the same procedure that led to the equations for x_{M}, x_{I0}, and x_{I0}, we obtain where A_{γ} = (γ − 2)(γ − 3), B_{γ} = 2(γ^{2} − 3γ − 1) and C_{γ} = γ(γ − 1). We see that the solution of the problem is fully determined by the functions g and h.
This generalization brings nothing new for the local extrema x_{Mγ}. The constraints for the number of fixed points x_{Mγ} and methods for approximating their positions can be obtained from the corresponding methods for γ = 1 by replacing g(x) with g_{γ}(x) = γ(2 − γ)^{1}g(x) and the local slope at the extremal points is q(ω_{Mγ}) = − γ, as expected. However, the problem of the inflection points is more complicated with the exceptions of γ = 0 and γ = 1. A possible generalization of h(x) for γ ∈ ⟨ 0,1 ⟩ is given by (40)
3. Polynomial flares
In all of the previous considerations, we have assumed that the flare profiles are given by decaying exponentials and the distribution is positive and can be normalized to one. No other assumptions on were imposed. Therefore, the resulting inequalities are very general. We now approach the case of flares of the form (41)where P_{k} is a polynomial of kth order, and I_{0}(τ) is an arbitrary nonnegative and quadratically integrable function. As a motivation we took I_{0}(τ) = 1 / τ, k = 1 and P_{1}(t / τ) = t / τ and calculated a power spectrum of a mixture of these profiles with the probability distribution p(τ), (42)It can be easily proven that the term S(ω  τ) from the previous equation satisfies a relation (43)Using this relation, we can rewrite Eq. (42) by integration by parts, (44)We observe that the power spectrum can be rewritten in the form If p(τ_{min}) = 0 and p(τ) − τp′(τ) ≥ 0 for all τ ∈ ⟨ τ_{min},τ_{max} ⟩, p_{2}(τ) is behaving well, can be normalized to unity, and the power spectrum can be studied in terms of the functions g and h, as described previously, with the only difference that all averages must be calculated using p_{2}(τ) instead of p(τ).
It is not apparent on first sight how to interpret these average values. Therefore, we now study the general case of an arbitrary polynomial (42) assuming that the power spectrum can be reduced to the pureexponential case. The PSD of a single profile (42) can be written as (47)where Q_{k}(z) is a kth order polynomial, different than (but related to) P_{k}(t). We define by (48)The operator is constructed entirely from τ, derivatives over τ, and from the coefficients of P_{k} (see the Appendix).
The generalization of Eq. (44) is (49)where is the Hermitian conjugate of . For p_{Q}(τ) positive over the interval of all admissible τs, we can calculate the function g(x), (50)where E_{Q}[.] is an averaging operator with respect to the distribution p_{Q}(τ). We note that we do not have to compute the function p_{Q}(τ).
As operates with τ only, it also commutes with the derivative in ω. Therefore, we can easily calculate the numerator and denominator of (50), The value of g(0) is related to the mean moment profiles as The function E_{I}(τ) can be interpreted as the total energy emitted by the flare and W_{I}(τ) is a measure of the width of the flare in the temporal domain. Equation (55) is formally identical with the prescription for the standard deviation. For the highfrequency limit we find (56)where q_{l} are the coefficients of the polynomial . The situation of h(x) is completely analogical. We can use the commutation properties of and express h(ω^{2}) as (57)The asymptotic slope of the power spectra S(ω  τ) is q(∞) = − 2(l + 1), where l is the order of the lowest nonzero coefficient of the polynomial P_{k}(t). Apparently, such a case can not be transformed onto the purely exponential case, because its asymptotical slope (36) is always q(∞) = − 2. However, we can easily repeat the whole analysis for flares of the form I(t,τ) = (t^{k} / τ^{k + 1})exp( − t / τ) θ(t), leading to the power spectrum (58)For every k we can define the functions g_{k} and h_{k}, It is easy to prove by direct calculation that the extrema and inflection points of ωS_{k}(ω) and S_{k}(ω) satisfy the relations Both g_{k} and h_{k} are nondecreasing (see the Appendix) and can therefore be used analogously to g and h. Apparently, g_{k}(0) = g(0) and h_{k}(0) = h(0), for the upper limits we find, (63)Finally, the local slope of S_{k}(ω) is related to g_{k}(x) by the formula(64)
3.1. Alternative forms of the function g
In the previous sections we described the properties of the function g. By construction, gvalues are in every point directly related to the power spectrum and its derivatives; see Eqs. (36) and (50)–(52). However, in all previous applications we have required only that g is a nondecreasing function, g(0) and g(∞) are positive finite numbers, and its fixed points g(x_{M}) = x_{M} correspond to the local extrema of ωS(ω). Clearly, for given S(ω), these conditions do not fix the function uniquely. For instance, every member of the sequence g^{(n)}(x) has the required properties. It is therefore natural to ask whether there is an alternative, easily calculable form of g.
A simple set of solutions to this problem can be found by adding a zero to Eq. (17) to obtain (65)where f(ω^{2}  τ) is a function of ω^{2} and τ. Properties of f(ω^{2}  τ) are specified below. Due to linearity of the averaging operator we can define a new function g_{[f]}(x) as (66)By construction, the functions g_{[f]}(x) and g(x) have identical fixed points irrespective of the choice of f(x  τ). The limits of are given by where φ(τ) is the limit (69)Both f(0  τ) and φ(τ) must be finite and chosen such that 0 < g_{[f]}(0) ≤ g_{[f]}(∞) < ∞. Unlike the previous cases, the positivity of the derivative of does not follow from any theorem and must be checked separately for every particular choice of f.
Fig. 6 Left: boundary of set C_{M} of doublypeaked power spectra ωS_{rD}(ω). Right: timescales τ_{M} calculated for β from the set { 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 } and for Ω_{s} from the interval ⟨ 0,10 ⟩. The blue line corresponds to . Again, the red lines denote the τ_{M} of the peaks, and the green lines denote the local minimum. 

Open with DEXTER 
4. Exponential flares with periodic modulation
In analogy with the previous section, we start the investigation with the simplest case of identical exponential flares modulated by sine function, (70)Without loss of generality we can put τ = 1 and rescale the power spectrum to the form (71)where β is taken from the interval ⟨ 0,1 ⟩. Figure 6 shows the boundary of set of the doubly peaked ω S_{rD}(ω) in the parameter space and the break timescales τ_{M} for some choices of β and Ω_{s}. The structure of the set C_{M} corresponds again to the cusp catastrophe. However, the structure of inflection points, shown in Fig. 7, is more rich.
Fig. 7 Left panel: boundary of the set C_{I0} (red) for the PSD (71). Parameters inside this area lead to PSD with a more than one inflection point. Unlike the power spectrum (4) S_{rD}(ω) can have a global maximum at ω ≈ Ω_{s}. C_{I1} (middle) and C_{Iq} (right) show the internal structure of the set. The common feature of the three panels are the critical curves (shown by other colours), where the number of inflection points changes. 

Open with DEXTER 
Unmodulated PSDs of the form (16) always satisfy the conditions S′′(ω) > 0 for ω → ∞ and S′′(0) = − 2E [τ^{2}] < 0, and hence they have an odd number of inflection points. However, the power spectrum (71) can be convex both at zero and in the limit of high frequencies and therefore it can have an even number of inflection points. These two cases are separated by a curve β_{crit}(Ω_{s}) given by the condition . By direct calculation we find (72)
4.1. General periodic modulation
We assumed that the exponentially decaying flares are at the same time orbiting in the equatorial plane of an accretion disc and that the observed signal is periodically modulated by the Doppler effect and abberation. It has been shown (Paper I) that the resulting power spectrum is described by the formula (73)where Ω(r) is the Keplerian orbital frequency, c_{n}(r) are the Fourier coefficients of the periodic modulation, and p(τ,r) is the probability density of finding a flare with the lifetime τ at the radius r. The formula (73) is far too complicated for calculations. Apparently, the factor can be again absorbed into the probability distribution. Integration over r and summation over n can be transformed into a single integral by appropriate change of the variables, r → Ω(r) / n. Finally, we can rewrite the Eq. (73) as (74)The function contains contributions from all harmonics of the orbital frequencies Ω(r) and is by construction symmetrical in the second variable . The distribution (75)leads to the PSD (71).
Fig. 8 Left panel: graph of versus calculated for the PSD from Eq. (4). The deformed grid of contour lines corresponds to curves of α = const. and K = const. in the parameter space of the model, where ω_{∞} ≥ ω_{0} by construction. It follows that the image of the entire parameter space lies above the green diagonal line of ω_{∞} = ω_{0}. Middle panel: the upperleft rectangular sector of acceptable pairs of ω_{0} and ω_{∞} from the left panel is projected back onto the (α,K) parameter space. Assuming the firstorder boundaries of and , the pairs of α vs. K outside of the filled (red) area lead to the power spectrum, which can not have any local extrema at ω_{M}. These parameter values are therefore ruled out. Right panel: analogical to the middle panel, but employing tighter (secondorder) boundaries and ; in this case, the parameter constraints are more stringent (the filled area is smaller). 

Open with DEXTER 
We now repeat the procedures which, applied to Eq. (16), led to the definition of the functions g and h. We begin with the local extrema of ωS(ω). By differentiating (74), we find (76)The term 1 + τ^{2}Ω^{2} is always positive. Therefore, we can in analogy to Eq. (19) define the function g_{Ω}(x) as (77)and observe that the position of the local extrema is again given by the fixed point (78)The function g_{Ω} is positive and both g_{Ω}(0) and g_{Ω}(∞) are finite. Unfortunately, in a general case, g_{Ω}(x) is not a nondecreasing function. To be able to use the techniques developed in Sect. 2 we have to define a perturbed version of g_{Ω}(x) analogical to (66) as (79)where L(ω  τ,Ω) = ^{(}1 + τ^{2}(ω − Ω)^{2}^{)}^{1} is the elementary Lorentzian term. The perturbation f(ω^{2}  τ,Ω) has to be finite at ω = 0 and to decay faster than ω^{1} to ensure the finiteness of g_{[f]Ω}(0) and g_{[f]Ω}(∞). Again, the positivity of the derivative of has to be tested for every particular choice f. For the power spectrum (71) it can be shown that (80)leads to a nondecreasing and positive function g_{[f]Ω}(ω^{2}).
A general calculation of the points of inflection is even more problematic. One can proceed in the following way. Analogically to Eqs. (39) and (40), the function h_{Ω}(ω) is given by the solution of the following equations: The positivity of the derivative of h_{Ω} has to be ensured by the perturbation procedure.
5. Discussion and conclusions
We have described a general formalism that can be used to characterize the overall form of a power spectrum, namely, we determined the constraints on the functional form of the PSD shape that follow from the occurrence of local peaks. In particular, our approach allowed us to distinguish between PSDs that have the form of multiple powerlaw profiles with different numbers of local maxima. Our methodology is useful in the context of nonmonotonic PSD profiles. In fact, Figs. 1 and 2 demonstrate that the occurrence of separate peaks of the PSD can put tight constraints on the model. The correct interpretation of these strict constraints still remains to be found. Naturally, a possible line of interpretation suggests that the spot/flare scenario is not generic and needs some kind of finetuning of the model parameters to reproduce observations. However, this calls for a more thorough investigation; in the present paper we only explored a highly idealized scheme in which strong assumptions were imposed. More complicated models (for instance, involving avalanches) also exhibit a more varied behaviour.
A potential application of our approach is illustrated in Fig. 8, where we constructed a mesh of twodimensional contourlines in a (α,K) parameter space of the model. Assuming that the exemplary power spectrum has a local maximum (or minimum) at frequency ω_{M}, it follows directly from Eq. (27) that the three frequencies must satisfy the relation ω_{0} ≤ ω_{M} ≤ ω_{∞}. Only the parameters pairs α and K whose images are within the upperleft sector (Fig. 8, left panel), as described by the inequality, can produce a power spectrum with the required feature at ω = ω_{M}.
An example for this is Cyg X1, which has been described as a superposition of several Lorentzians in the Xray band light curve (Pottschmidt et al. 2003). These authors have demonstrated that the change of the timing parameters of the source is mainly caused by a strong decrease in the amplitude of one of the Lorentzian components present in the PSD. This characteristic is closely connected with the normalization of the model components. During the state transition of the source one of the Lorentzians becomes suppressed relative to the other. It was suggested that this behaviour is associated with the accretion disc corona, which is believed to be responsible for the hardstate spectrum. The evolution of the PSD form can be studied in terms of our method, which allowed us to discuss the entire class of multiplepowerlaw PSDs within a uniform systematic scheme. Although it is a versatile approach, its practical use can be illustrated in a very simple way.
The hardstate Xray spectrum is very likely related with the presence of soft photons from the accretion disc, which are Compton upscattered in a hot electron gas. The state transitions are then caused by the disappearance of the Comptonizing medium. Such a change in the coronal configuration could explain the change of the mutual normalization of the Lorentzian components, namely, the disappearance or recurrence of local peaks in the PSD. In the idea of coronal flares modulating the accretion disc variability of the source, which seems to be relevant for Cyg X1 as well, the position of the source in Fig. 8 will constrain possible parameter values of the model.
Finally, we have checked that a certain proportionality between the light curve rms and the flux does exist in our model as well. This is an interesting fact by itself, however, it is not clear at present whether the slope and the scatter of the rmsflux relation are in reasonable agreement with the actual data, and how generic the model is. More detailed investigations are needed to clarify whether the spot model could satisfy the constraints arising beyond the PSD profiles. Moreover, it remains to be seen if our model requires some kind of special finetuning of the parameters (such as the inclination angle), which would make this explanation less likely (work in progress).
Acknowledgments
The research leading to these results has received funding from the Czech Science Foundation and Deutsche Forschungsgemeinschaft collaboration project (VK, GACRDFG 1300070J). We also acknowledge the Polish grant NN 203 380136 (B.C.) and the French GdR PCHE (R.G.). Part of the work was supported by the European Union Seventh Framework Programme under the grant agreement No. 312789 (M.D., B.C., R.G.). The Astronomical Institute has been operated under the program RVO:67985815 in the Czech Republic (TP).
References
 Arévalo, P., & Uttley, P. 2006, MNRAS, 367, 801 [NASA ADS] [CrossRef] [Google Scholar]
 Czerny, B., Różańska, A., Dovčiak, M., Karas, V., & Dumont, A.M. 2004, A&A, 420, 1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Done, C., Gierliński, M., & Kubota, A. 2007, A&ARv, 15, 1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Feigelson, E. D., & Babu, G. J. 1992, Statistical Challenges in Modern Astronomy (Berlin: Springer) [Google Scholar]
 Frank, J., King, A., & Raine, D. J. 2002, Accretion Power in Astrophysics (Cambridge: Cambridge University Press) [Google Scholar]
 Galeev, A. A., Rosner, R., & Vaiana, G. S. 1979, ApJ, 229, 318 [NASA ADS] [CrossRef] [Google Scholar]
 Gandhi, P. 2009, ApJ, 697, L167 [NASA ADS] [CrossRef] [Google Scholar]
 Gaskell, C. M., McHardy, I. M., Peterson, B. M., & Sergeev, S. G. 2006, AGN Variability from XRays to Radio Waves, ASP Conf. Ser., 360 [Google Scholar]
 Lawrence, A., & Papadakis, I. 1993, ApJ, 414, L85 [NASA ADS] [CrossRef] [Google Scholar]
 Lawrence, A., Watson, M. G., Pounds, K. A., & Elvis, M. 1987, Nature, 325, 694 [NASA ADS] [CrossRef] [Google Scholar]
 Lehto, H. J. 1989, in Two Topics in XRay Astronomy, Volume 1: X Ray Binaries. Volume 2: AGN and the X Ray Background, eds. J. Hunt, & B. Battrick, ESA SP, 296, 499 [Google Scholar]
 Markowitz, A., Edelson, R., Vaughan, S., et al. 2003, ApJ, 593, 96 [NASA ADS] [CrossRef] [Google Scholar]
 McHardy, I., & Czerny, B. 1987, Nature, 325, 696 [NASA ADS] [CrossRef] [Google Scholar]
 McHardy, I. M., Gunn, K. F., Uttley, P., & Goad, M. R. 2005, MNRAS, 359, 1469 [NASA ADS] [CrossRef] [Google Scholar]
 McHardy, I. M., Koerding, E., Knigge, C., Uttley, P., & Fender, R. P. 2006, Nature, 444, 730 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
 McHardy, I. M., Arévalo, P., Uttley, P., et al. 2007, MNRAS, 382, 985 [NASA ADS] [CrossRef] [Google Scholar]
 Mushotzky, R. F., Done, C., & Pounds, K. A. 1993, ARA&A, 31, 717 [NASA ADS] [CrossRef] [Google Scholar]
 Mushotzky, R. F., Edelson, R., Baumgartner, W., & Gandhi, P. 2011, ApJ, 743, L12 [NASA ADS] [CrossRef] [Google Scholar]
 Nowak, M. A., Vaughan, B. A., Wilms, J., Dove, J. B., & Begelman, M. C. 1999, ApJ, 510, 874 [NASA ADS] [CrossRef] [Google Scholar]
 Orosz, J. A., McClintock, J. E., Aufdenberg, J. P., et al. 2011, ApJ, 742, 84 [NASA ADS] [CrossRef] [Google Scholar]
 Pecháček, T., Karas, V., & Czerny, B. 2008, A&A, 487, 815 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Pottschmidt, K., Wilms, J., Nowak, M. A., et al. 2003, A&A, 407, 1039 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Uttley, P., & McHardy, I. M. 2001, MNRAS, 323, L26 [NASA ADS] [CrossRef] [Google Scholar]
 Uttley, P., McHardy, I. M., & Papadakis, I. E. 2002, MNRAS, 332, 231 [NASA ADS] [CrossRef] [Google Scholar]
 Uttley, P., McHardy, I. M., & Vaughan, S. 2005, MNRAS, 359, 345 [NASA ADS] [CrossRef] [Google Scholar]
 Vaughan, S., & Uttley, P. 2008, in Noise and Fluctuations, Proc. SPIE, 6603 [arXiv:0802.0391] [Google Scholar]
 Vio, R., Cristiani, S., Lessi, O., & Provenzale, A. 1992, ApJ, 391, 518 [NASA ADS] [CrossRef] [Google Scholar]
Appendix A: Monotonicity of the functions g and h
We prove that the derivatives of g_{k}(x) and h_{k}(x) are nonnegative. We define an operator F_{x,k} [.] by its action on an arbitrary function f(τ) as (A.1)where N_{x,k} is a normalization constant ensuring that F_{x,k} [1] = 1 for all k and x. For a fixed value of x it acts as an mean value operator with a probability distribution p_{x}(τ) = p(τ) / ^{(}1 + τ^{2}ω^{2}^{)}^{− k − 3}. Using this operator, we can express the derivatives as The denominators in both equations are always nonnegative. Furthermore, nonnegativity of the numerator of (A.2) follows from the Jenssen theorem. The numerator of (A.3) is a determinant of a matrix of the following quadratic form: (A.4)Because the form is positively (semi)definite, the determinant must be a nonnegative function.
Appendix B: Operator associated with the polynomial Q
We give an explicit formula for the operator . We define operators and as (B.1)It can be proven by direct calculation that From these relations it follows, for arbitrary l and m, (B.4)Assuming that , we can define the operator as (B.5)The polynomials Q_{k} and P_{k} are mutually related by the formula (B.6)where p_{l} are the coefficients of P_{k}.
All Figures
Fig. 1 Left panel: diagram describing the morphology of the PSD profiles according to Eq. (4) for all possible values of the parameters α and K. Different curves represent the boundaries of subsets of the parameters for which the PSD exhibits a doublefeature. The red curve encloses set C_{M} of the doubly peaked power spectra. The other curves correspond to the sets of power spectra with a nontrivial structure of inflection points C_{I0} (blue), C_{I1} (magenta) and C_{Iq} (green). Right panel: three examples of the PSD profiles. A doublepeaked power spectrum (red) with parameters α = 0.94 and K = 0.01 (i.e., the values within set C_{M}, as denoted by the red point in the left panel), doublybroken power spectrum with a single local maximum (magenta) corresponding to α = 0.85 and K = 0.08 (the magenta point within set C_{I1} and outside of C_{M}) and a power spectrum with single maximum and break (green) with α = 0.5 and K = 0.5 (the green point outside of C_{Iq}). 

Open with DEXTER  
In the text 
Fig. 2 Left: comparison of the timescales τ_{M} (red) and E[τ] (magenta). The lines are calculated for α from the set {0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8}, avoiding set C_{M}. Middle: as in the left panel, but for α = 0.9. For low K this corresponds to doublepeaked power spectra. The red curves correspond to 1 over the peak frequency, the green is for the minimum. The blue curve denotes the timescale 1 / E[1 / τ]. Right: comparison of the timescales τ_{M} (red) and 1 / E[1 / τ] (blue). 

Open with DEXTER  
In the text 
Fig. 3 Timescales τ_{I0} (left), τ_{I1} (middle) and τ_{Iq} (right) calculated for the PSD given by Eq. (4). We assumed the values of α from the set { 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 }, and K from the interval ⟨ 0,1 ⟩. 

Open with DEXTER  
In the text 
Fig. 4 Left: function g(x) (Eq. (19)) for the power spectrum (4), calculated for α = 0.95, and K = 0.01 (red) and K = 0.2 (blue), respectively. The green line represents the identity function g(x) = x. Right: the corresponding plot of timescales, similar to the right panel of Fig. 2. The lightblue lines correspond to the limits from the inequality (21). 

Open with DEXTER  
In the text 
Fig. 5 Top left: boundaries of approximating sets for the power spectrum S_{r}(ω). The function g(x) was evaluated in L points, evenly placed in between g(0) and g(∞). The nested curves correspond to calculated for L equal to 4, 5, 9, and 49. Top right: the nested sets calculated from the upper limit of g′(x). We have taken x_{+} = g^{(n)}(0) and x_{−} = g^{(n)}(∞) for n from 1 to 4. Bottom left: the same as in the top left panel for sets . Bottom right: the same as in the bottom left panel, this time with a grid of points that samples the area around point 3h(0) more densely but that does not cover the whole interval ⟨ 3h(0),3h(∞) ⟩. The values of L are the same as in the previous examples. The approximating sets cover a broader part of the whole set . However, they omit a small area of high α. This demonstrates that an adaptive grid can perform significantly better than a regular one. 

Open with DEXTER  
In the text 
Fig. 6 Left: boundary of set C_{M} of doublypeaked power spectra ωS_{rD}(ω). Right: timescales τ_{M} calculated for β from the set { 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 } and for Ω_{s} from the interval ⟨ 0,10 ⟩. The blue line corresponds to . Again, the red lines denote the τ_{M} of the peaks, and the green lines denote the local minimum. 

Open with DEXTER  
In the text 
Fig. 7 Left panel: boundary of the set C_{I0} (red) for the PSD (71). Parameters inside this area lead to PSD with a more than one inflection point. Unlike the power spectrum (4) S_{rD}(ω) can have a global maximum at ω ≈ Ω_{s}. C_{I1} (middle) and C_{Iq} (right) show the internal structure of the set. The common feature of the three panels are the critical curves (shown by other colours), where the number of inflection points changes. 

Open with DEXTER  
In the text 
Fig. 8 Left panel: graph of versus calculated for the PSD from Eq. (4). The deformed grid of contour lines corresponds to curves of α = const. and K = const. in the parameter space of the model, where ω_{∞} ≥ ω_{0} by construction. It follows that the image of the entire parameter space lies above the green diagonal line of ω_{∞} = ω_{0}. Middle panel: the upperleft rectangular sector of acceptable pairs of ω_{0} and ω_{∞} from the left panel is projected back onto the (α,K) parameter space. Assuming the firstorder boundaries of and , the pairs of α vs. K outside of the filled (red) area lead to the power spectrum, which can not have any local extrema at ω_{M}. These parameter values are therefore ruled out. Right panel: analogical to the middle panel, but employing tighter (secondorder) boundaries and ; in this case, the parameter constraints are more stringent (the filled area is smaller). 

Open with DEXTER  
In the text 
Current usage metrics show cumulative count of Article Views (fulltext article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 4896 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.