Analysis of pulsating variable stars using the visibility graph algorithm

Muñoz, Vı́ctor; Garcés, N. Elizabeth

doi:10.1371/journal.pone.0259735

Cited by 8 publications

(5 citation statements)

References 28 publications

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“…Thus, here, we have focused on the use of the HVG as a method to distinguish the different light curves, and to tackle this purpose, we have used the HVG and analyzed the distribution of the values of the

. In addition, it is known that when using the HVG, any random series results in a network with a degree distribution of exponential type, and it has been suggested that this is a universal feature [ 6 ]. If exponential forms are not obtained, the series are related to non-randomness [ 23 ].…”

Section: Discussionmentioning

confidence: 99%

“…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 ]. Being the closest star to Earth, the Sun and the solar wind are arguably the most studied astrophysical systems, corresponding to a valuable laboratory of natural plasma physics.…”

Section: Introductionmentioning

confidence: 99%

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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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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.

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Section: Discussionmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…Thus, here we have focused on the use of the HVG as a method to distinguish the different light curves, and to tackle this purpose we have used the HVG and analyzed the distribution of the values of the γ. In addition, it is known that when using the HVG, any random series results in a network with a degree distribution of exponential type, and it has been suggested that this is a universal feature [6]. If exponential forms are not obtained, the series are related to non-randomness [23].…”

Section: Discussionmentioning

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

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, 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

“…This allows for the correlation information within the time series to be inferred from the topological properties of the resulting network [ 37 , 39 , 40 ]. A simplified version of the VG, known as the horizontal visibility graph (HVG), has demonstrated higher efficiency in practical applications [ 41 – 47 ]. Manshour et al [ 48 ] calculated three topological parameters (TPs) of HVG-mapped complex networks and observed monotonic relationships between these TPs and long-term correlations (LTC), suggesting the potential to infer LTC strength in idealized fractional time series from complex network analysis.…”

Section: Introductionmentioning

confidence: 99%

Multiple serial correlations in global air temperature anomaly time series

Gao,

Fang,

et al. 2024

PLoS ONE

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Serial correlations within temperature time series serve as indicators of the temporal consistency of climate events. This study delves into the serial correlations embedded in global surface air temperature (SAT) data. Initially, we preprocess the SAT time series to eradicate seasonal patterns and linear trends, resulting in the SAT anomaly time series, which encapsulates the inherent variability of Earth’s climate system. Employing diverse statistical techniques, we identify three distinct types of serial correlations: short-term, long-term, and nonlinear. To identify short-term correlations, we utilize the first-order autoregressive model, AR(1), revealing a global pattern that can be partially attributed to atmospheric Rossby waves in extratropical regions and the Eastern Pacific warm pool. For long-term correlations, we adopt the standard detrended fluctuation analysis, finding that the global pattern aligns with long-term climate variability, such as the El Niño-Southern Oscillation (ENSO) over the Eastern Pacific. Furthermore, we apply the horizontal visibility graph (HVG) algorithm to transform the SAT anomaly time series into complex networks. The topological parameters of these networks aptly capture the long-term correlations present in the data. Additionally, we introduce a novel topological parameter, Δσ, to detect nonlinear correlations. The statistical significance of this parameter is rigorously tested using the Monte Carlo method, simulating fractional Brownian motion and fractional Gaussian noise processes with a predefined DFA exponent to estimate confidence intervals. In conclusion, serial correlations are universal in global SAT time series and the presence of these serial correlations should be considered carefully in climate sciences.

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Analysis of pulsating variable stars using the visibility graph algorithm

Cited by 8 publications

References 28 publications

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

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

Multiple serial correlations in global air temperature anomaly time series

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