Blazars are a special class of active galactic nuclei (AGN) known for their high variability, which makes them ideal for studying these compact systems. This variability is determined by characteristic timescales. However, real observational light curves suffer from issues such as noise and gaps. If these issues are not addressed, they could significantly impact our understanding of these objects' behavior.

In a new study published in Monthly Notices of the Royal astronomical Societyon November 6, Ashutosh Tripathi from the Xinjiang Astronomical Observatory of the Chinese Academy of Sciences and his collaborators applied the advanced time-series analysis methods to one year of data from the Transiting Exoplanet Survey Satellite (TESS) of the radio blazar 3C 371.

The researchers analyzed the TESS light curve using multiple complementary approaches, including power spectrum density, structure function, and weighted wavelet Z-transform to estimate the characteristic timescales. They used continuous auto-regressive moving averages, Barlett's periodogram, and wavelet decomposition methods to mitigate the effects of gaps and noise in the light.

Their results showed that when the observational gaps are small, the different analysis methods yield consistent timescale estimates. However, they diverge markedly as the gaps increase. Reducing noise increases the significance of a signal present in the data.

The researchers also discovered a quasi-periodic signal of approximately five days with 50 cycles using these advanced methods. This signal may be related to sub-structures within the jets or the temporal evolution of plasma-driven kink instabilities.

This study provides a methodological framework for subsequent AGN variability studies with TESS and other multi-wavelength observations, as well as for reducing the effects of noise and gaps in observations.

Timing analysis results for epoch A. The light curve for epoch A is plotted in panel (a) along with the C-ARMA generated light curve and a sine fit with a period of 6 d. Panels (b) and (c), respectively, show the PSD and SF calculated directly from the observations and from the C-ARMA fit. Panel (d) shows the GLSP results along with the 4σ significance curve. The brown dotted and brown dashed curves, respectively, correspond to a possible quasi-periodic and a variability frequency. The corresponding WWZ colour–colour diagram is plotted in panel (e). The QPO and variability frequencies are denoted by black dotted and brown dashed curves, respectively. (Image by Ashutosh Tripathi)