Applying the Horizontal Visibility Graph Method to Study Irreversibility of Electromagnetic Turbulence in Non-Thermal Plasmas

Acosta-Tripailao, Belén; Pastén, Denisse; Moya, P. S.

doi:10.3390/e23040470

Cited by 20 publications

(12 citation statements)

References 64 publications

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“…As already mentioned, these non-thermal features can be modeled by non-extensive distributions. There are many works that relate turbulence with the representation of the system in terms of distributions functions different than the Maxwellian profile representing thermodynamic equilibrium 31 , 32 . Besides an ad-hoc fit, there is a strong relation between the shape of the VDF and the microscopic and macroscopic dynamics governing the system.…”

Section: Introductionmentioning

confidence: 99%

Langevin based turbulence model and its relationship with Kappa distributions

Gallo-Méndez

Moya

2022

Sci Rep

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Kappa distributions (or $$\kappa $$ κ -like distributions) represent a robust framework to characterize and understand complex phenomena with high degrees of freedom, as turbulent systems, using non-extensive statistical mechanics. Here we consider a coupled map lattice Langevin based model to analyze the relation of a turbulent flow, with its spatial scale dynamic, and $$\kappa $$ κ -like distributions. We generate the steady-state velocity distribution of the fluid at each scale, and show that the generated distributions are well fitted by $$\kappa $$ κ -like distributions. We observe a robust relation between the $$\kappa $$ κ parameter, the scale, and the Reynolds number of the system, Re. In particular, our results show that there is a closed scaling relation between the level of turbulence and the $$\kappa $$ κ parameter; namely $$\kappa \sim \text {Re}\,k^{-5/3}$$ κ ∼ Re k - 5 / 3 . We expect these results to be useful to characterize turbulence in different contexts, and our numerical predictions to be tested by observations and experimental setups.

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Section: Introductionmentioning

confidence: 99%

Langevin based turbulence model and its relationship with Kappa distributions

Gallo-Méndez

Moya

2022

Sci Rep

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“…With the already constructed the DHVG, we can extract the undirected version (UHVG), just considering a total degree k ud = k in + k out . More details about geometrical criteria in Acosta-Tripailao et al [3]. Thus, by counting the frequency of occurrence of each degree we obtain degrees distribution or probability distributions in the form P(k) = n k /n, where n is the number of data points in the time series and n k the number of nodes having degree k. Namely, P = P(k ud ) for UHVG and P in = P(k in ) with P out = P(k out ) for DHVG.…”

Section: Horizontal Visibility Algorithmmentioning

confidence: 99%

Assigning degrees of stochasticity to blazar light curves in the radio band using complex networks

Acosta-Tripailao¹,

Max-Moerbeck²,

Pastén³

et al. 2022

Preprint

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We focus on characterizing the high-energy emission mechanisms of blazars by analyzing the variability in the radio band of the light curves of more than a thousand sources. We are interested in assigning complexity parameters to these sources, modeling the time series of the light curves with the method of the Horizontal Visibility Graph (HVG), which allows us to obtain properties from degree distributions, such as a characteristic exponent to describe its stochasticity and the Kullback-Leibler Divergence (KLD), presenting a new perspective to the methods commonly used to study Active Galactic Nuclei (AGN). We contrast these parameters with the excess variance, an astronomical measurement of variability in light curves, at the same time we use the spectral classification of the sources. While it is not possible to find significant correlations with the excess variance, the degree distributions extracted from the network are detecting differences related to the spectral classification of blazars. These differences suggest a chaotic behavior in the time series for the BL Lac sources and a correlated stochastic behavior in the time series for the FSRQ sources. Our results show that complex networks may be a valuable alternative tool to study AGNs according to the variability of their energy output.

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“…Here, we are interested in modeling time series with the techniques from the family of visibility algorithms [ 2 ]. In one of the first approaches to astrophysical systems through the use of Horizontal Visibility Graph (HVG), we have shown that this method is able to detect differences in particle velocity distributions in plasma simulations [ 3 ]. Moreover, the HVG has proved to be a robust method to characterize the solar wind plasma and has been used to study turbulent magnetic field [ 4 ], velocity fluctuations [ 5 ] and light curves of pulsating variable stars [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

“…Luque et al [ 23 ] has demonstrated that the method we apply here efficiently discriminates randomness and not only uncorrelated randomness from chaos, but also more complicated stochastic processes in time series can be identified, such as fractional Brownian motion. KLD is sensitive to non-evident characteristics of time series [ 3 ]. The HVG is a method that allows us to analyze time series and its irreversibility through the calculation of the Kullback–Leibler divergence (KLD).…”

Section: Introductionmentioning

confidence: 99%

Assigning Degrees of Stochasticity to Blazar Light Curves in the Radio Band Using Complex Networks

Acosta-Tripailao

Max-Moerbeck²,

Pastén³

et al. 2022

Entropy

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View full text Add to dashboard Cite

We focus on characterizing the high-energy emission mechanisms of blazars by analyzing the variability in the radio band of the light curves of more than a thousand sources. We are interested in assigning complexity parameters to these sources, modeling the time series of the light curves with the method of the Horizontal Visibility Graph (HVG), which allows us to obtain properties from degree distributions, such as a characteristic exponent to describe its stochasticity and the Kullback–Leibler Divergence (KLD), presenting a new perspective to the methods commonly used to study Active Galactic Nuclei (AGN). We contrast these parameters with the excess variance, which is an astronomical measurement of variability in light curves; at the same time, we use the spectral classification of the sources. While it is not possible to find significant correlations with the excess variance, the degree distributions extracted from the network are detecting differences related to the spectral classification of blazars. These differences suggest a chaotic behavior in the time series for the BL Lac sources and a correlated stochastic behavior in the time series for the FSRQ sources. Our results show that complex networks may be a valuable alternative tool to study AGNs according to the variability of their energy output.

show abstract

Applying the Horizontal Visibility Graph Method to Study Irreversibility of Electromagnetic Turbulence in Non-Thermal Plasmas

Cited by 20 publications

References 64 publications

Langevin based turbulence model and its relationship with Kappa distributions

Langevin based turbulence model and its relationship with Kappa distributions

Assigning degrees of stochasticity to blazar light curves in the radio band using complex networks

Assigning Degrees of Stochasticity to Blazar Light Curves in the Radio Band Using Complex Networks

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