Speaker
Description
We present results of Fermi-Large Area Telescope (LAT) light curve (LC) modelling
of selected Fermi blazars. All objects possess densely sampled and long-term LCs. For
each blazar we generated three LCs with 7, 10, and 14 days binning, using the latest
4FGL catalogue and binned analysis provided within the fermipy package.
The LCs were modelled with several tools: the Fourier transformation, the Lomb-
Scargle periodogram (LSP), the autoregressive moving average (ARMA), the fractional
autoregressive integrated moving average, the continuous-time autoregressive moving av-
erage (CARMA) processes, the Hurst exponents (H), the A-T plane, and the wavelet
scalogram.
Power law indices β calculated from the Fourier and LSP modelling are consistent
with each other. Many objects yield β≈1, with PKS 2155-304 even flatter, but some are
significantly steeper, e.g. Mrk 501 and B2 1520+31. A power law power spectral density
(PSD) is indicative of a self-affine stochastic process characterised by H, underlying the
observed variability. Several algorithms for the H estimation are employed. For some
objects we observe H>0.5, indicating long-term memory. The ARMA results give in
general higher orders for 7 days binned LCs and lower orders for 10 and 14 days binned
LCs, implying temporal variations in the LCs are consistently captured by the fitted
models. CARMA fits lead to featureless PSDs. The recently introduced A-T plane
allows to successfully classify the PSDs based on the LCs alone.
