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

TL;DR

This study applies various time-series analysis techniques to TESS observations of the blazar 3C 371, demonstrating how to mitigate noise and gaps to identify variability timescales and potential quasi-periodic oscillations in the data.

Contribution

It introduces a comprehensive approach combining multiple time-series methods to analyze TESS AGN data, addressing noise and gaps effectively for variability detection.

Findings

Variability timescale around 4.5 days for 3C 371.

Detected quasi-periodic oscillations of 3--6 days in individual segments.

Effective noise reduction using Bartlett's periodogram and wavelet decomposition.

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.