Application of time-series analysis methods to a multiple-sector TESS observations: the case of the radio-loud blazar 3C 371

Abstract

We present various time series analysis methods to analyze multiple-sector observations of bright AGN from the Transiting Exoplanet Survey Satellite (TESS) and examine whether issues such as gaps and noise in these data can be mitigated. We determine variability timescales and search for quasi-periodicity using these methods and assess any differences. In this paper, we present an analysis of the $\approx$300-day TESS observation of a blazar 3C 371 using power spectrum density, structure-function, and weighted wavelet Z-transform approaches. To reduce the effect of gaps and noise, Continuous auto-regressive moving averages, Bartlett periodogram, and wavelet decomposition methods are used. We have also used recurrence analysis to account for the nonlinearity present in the data and to quantify variability or periodicity as the recurrent state. Considering the entirety of the TESS observations, we derive the variability timescale to be around 4.5 days. Sector-wise analysis found variability timescales in the range of 3.0--7.0 days, values that are found to be consistent using different methods. When analyzing multiple sectors together, significant variability, which could be quasi-periodic oscillations (QPOs), of duration 3--6 days in individual segments, is detected. These may be attributed to the kink instabilities developed in the jet or the existence of mini-jets inside a jet undergoing precession. We find that these methods, when applied appropriately, can be used to study the variability in TESS data. The noise present in these TESS observations can be minimized using Bartlett's periodogram and wavelet decomposition to recover the real stochastic variability.

Figures (13)

• Figure 1: Cycle 2 TESS observations of 3C 371 using simple (top) and fully (bottom) hybrid methods. The data is binned to 0.3 days for visual clarity. The entire Cycle 2 observation is divided into various sectors (by dashed vertical lines) whose numbers are indicated in the plot. In our analysis, we also divide the light curve into 3 epochs, A, B, and C, denoted by magenta, blue, and green, respectively.
• Figure 2: Flux distribution of each sector of Cycle 2 TESS observations of 3C 371. Fits with Gaussian (blue) and bimodal Gaussian (red) distributions are also plotted.
• Figure 3: Periodogram estimations for each sector of the Cycle 2 TESS observations of 3C 371. The blue and orange curves correspond to the Lomb-Scargle periodogram and the periodogram obtained from C-ARMA modeling respectively. The vertical dashed lines mark the bending frequencies $\nu_b$ where the spectral index changes, corresponding to a variability timescale present in the observation.
• Figure 4: Structure Function (SF) calculations for each sector (numbered in each panel) of Cycle 2 TESS observations of 3C 371. The blue curve is the SF curve estimated directly from the observation and the orange curve corresponds to the SF calculated by C-ARMA modeling. The first dip in the SF corresponds to the timescale of variability present in the observation and is denoted by the vertical dashed line.
• Figure 5: Recurrence plots of the individual sectors of Cycle 2 observations, in the order 14, 15, 16, 17 (top row), 18, 20, 21, 22 (middle row), 23, 24, 25, and 26 (bottom row). The black dots in the plots correspond to the value of 1 in the recurrence matrix whereas the white dots correspond to a zero entry in the recurrence matrix.