Gamma-Ray Astrophysics in the Time Domain
Abstract
:1. Introduction
2. Timing Characteristics
2.1. Rapid Variability at VHE Energies
2.2. PDF Shape and Log-Normality
2.3. PSD and Power-Law Noise
2.4. Quasi-Periodic Variability
3. A Possible Example: PKS 2155-304
4. Conclusions
Funding
Acknowledgments
Conflicts of Interest
References
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1 | There are two common approaches to deal with irregular sampling in stochastic light curves, i.e., by means of extensive Monte Carlo simulations of artificial light curves [29,30] or by means of likelihood-based approaches using special parameterized stochastic models in the time domain such as, e.g., the first-order continuous-time autoregressive (Ornstein–Uhlenbeck) process (e.g., [31,32]). The latter offer an efficient means to extract information from large time-domain datasets, in particular the power spectral density (PSD). While autoregressive models are becoming increasingly popular (cf. [33,34]), general caveats concern the limitations of linear (e.g., ARMA, CARMA) models to extract physical information from (very probably) non-linear systems (e.g., [35]); see also Section 2.2. |
2 | A random variable X is log-normally distributed if obeys a normal (Gaussian) distribution. The PDF of such a random variable is of the form , where is the mean and the standard deviation. |
3 | Defined as the square-root of the light curve variance and related to the square-root of the integral of the PSD, see below, over the observable frequency range, i.e., . |
4 | For a stationary stochastic process X that results from a multiplication of N random subprocesses , , the logarithm of X is equivalent to the sum of the logarithm of the individual , i.e., . By the central limit theorem, this sum must approach a normal (Gaussian) distribution for . Astrophysically, N does not have to be large to achieve a good log-normal distribution [45]. |
5 | The widely employed Timmer and König algorithm [29] to simulate light curves assumes a normal (Gaussian) stochastic process, and thus potentially introduces errors in parameter estimation for log-normal processes [30]. It remains to be studied how this affects the results given the quality of available gamma-ray data. |
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Rieger, F.M. Gamma-Ray Astrophysics in the Time Domain. Galaxies 2019, 7, 28. https://doi.org/10.3390/galaxies7010028
Rieger FM. Gamma-Ray Astrophysics in the Time Domain. Galaxies. 2019; 7(1):28. https://doi.org/10.3390/galaxies7010028
Chicago/Turabian StyleRieger, Frank M. 2019. "Gamma-Ray Astrophysics in the Time Domain" Galaxies 7, no. 1: 28. https://doi.org/10.3390/galaxies7010028
APA StyleRieger, F. M. (2019). Gamma-Ray Astrophysics in the Time Domain. Galaxies, 7(1), 28. https://doi.org/10.3390/galaxies7010028