Multifractality signatures in quasars time series – I. 3C 273
The presence of multifractality in a time series shows different correlations for different timescales as well as intermittent behaviour that cannot be captured by a single scaling exponent. The identification of a multifractal nature allows for a characterization of the dynamics and of the interm...
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| Principais autores: | , , , , , |
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| Formato: | article |
| Idioma: | English |
| Publicado em: |
Oxford University Press (OUP)
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| Assuntos: | |
| Endereço do item: | https://repositorio.ufrn.br/jspui/handle/123456789/29407 http://dx.doi.org/10.1093/mnras/sty1316 |
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| Resumo: | The presence of multifractality in a time series shows different correlations for different timescales as well as intermittent behaviour that cannot be captured by a single scaling exponent.
The identification of a multifractal nature allows for a characterization of the dynamics and
of the intermittency of the fluctuations in non-linear and complex systems. In this study, we
search for a possible multifractal structure (multifractality signature) of the flux variability
in the quasar 3C 273 time series for all electromagnetic wavebands at different observation
points, and the origins for the observed multifractality. This study is intended to highlight how
the scaling behaves across the different bands of the selected candidate, which can be used
as an additional new technique to group quasars based on the fractal signature observed in
their time series and determine whether quasars are non-linear physical systems or not. The
multifractal detrended moving average algorithm (MFDMA) has been used to study the scaling
in non-linear, complex, and dynamic systems. To achieve this goal, we applied the backward
(θ = 0) MFDMA method for one-dimensional signals. We observe weak multifractal (close
to monofractal) behaviour in some of the time series of our candidate except in the mm,
UV and X-ray bands. The non-linear temporal correlation is the main source of the observed
multifractality in the time series whereas the heaviness of the distribution contributes less. |
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