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Variability Study and Searching for QPOs with day-like periods in the blazar S5 0716+714 with TESS

Shubham Kishore, Alok C. Gupta, Paul J. Wiita

Abstract

Using an unprecedented cadence of 30 minutes provided by the Transiting Exoplanet Survey Satellite (TESS), we have examined the optical light curves (LCs) of the blazar S5 0716+714 obtained from its Sectors 40, 47, and 53 over a period of about 75 days. This source exhibited flux variability in each of those sectors, reaching a maximum variability amplitude of 5.6%. The power spectral density (PSD) shapes were tested with a simple power law and two distinct bending power laws and were found to be better fit by bending power laws than simple power laws for all but one of the segments. To look for any periodicities in these LCs, we used weighted wavelet Z (WWZ) transform analysis and generalized Lomb-Scargle periodograms (LSPs). We identified one possible quasi-periodic oscillation (QPO) signature in a portion of sector 40 (period of ~6.5 h), having ~95% global significance. A statistical approach to assess the light curves involving continuous autoregressive moving average (CARMA) was implemented, and the light curves were found to follow more complex processes than the simplest and typical damped random walk process. We briefly discuss the statistical properties of the light curves along with the general variability features and physical processes that could cause these types of fluctuations.

Variability Study and Searching for QPOs with day-like periods in the blazar S5 0716+714 with TESS

Abstract

Using an unprecedented cadence of 30 minutes provided by the Transiting Exoplanet Survey Satellite (TESS), we have examined the optical light curves (LCs) of the blazar S5 0716+714 obtained from its Sectors 40, 47, and 53 over a period of about 75 days. This source exhibited flux variability in each of those sectors, reaching a maximum variability amplitude of 5.6%. The power spectral density (PSD) shapes were tested with a simple power law and two distinct bending power laws and were found to be better fit by bending power laws than simple power laws for all but one of the segments. To look for any periodicities in these LCs, we used weighted wavelet Z (WWZ) transform analysis and generalized Lomb-Scargle periodograms (LSPs). We identified one possible quasi-periodic oscillation (QPO) signature in a portion of sector 40 (period of ~6.5 h), having ~95% global significance. A statistical approach to assess the light curves involving continuous autoregressive moving average (CARMA) was implemented, and the light curves were found to follow more complex processes than the simplest and typical damped random walk process. We briefly discuss the statistical properties of the light curves along with the general variability features and physical processes that could cause these types of fluctuations.
Paper Structure (10 sections, 9 equations, 4 figures, 2 tables)

This paper contains 10 sections, 9 equations, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Data Acquisition and Reduction
  3. Data Analysis and Results
  4. Excess variance
  5. Periodogram Analysis
  6. Weighted Wavelet Z (WWZ) Analysis
  7. Stochastic Modeling
  8. Discussion
  9. Light curves and periodogram properties
  10. Possible causes of aperiodic erratic variability

Figures (4)

  • Figure 1: Light curves of S5 0716+714 corresponding to Sectors 40 (top), 47 (middle), and 53(bottom)
  • Figure 2: LSPs and the TAPs obtained with the WWZ transforms for different indicated sectors. The three rows correspond to Sectors 40, 47, and 53, while in each row, the two plots are for the two segments for each mentioned sector. The $99.73\%$ significance levels are the local ones and correspond to the most preferred power law model considered via the BIC coefficient.
  • Figure 3: WWZ analysis of the segmented light curve. The three rows correspond to Sectors 40, 47, and 53, while in each row, the two plots are for the two segments for each mentioned sector.
  • Figure 4: Upper left panel: Comparision of LSP fittings with the log-likelihood method and another by simulating $10^5$ lightcurves with DELCgen.py; Upper right panel: Comparison of $\chi^2$ ratio CDF with the theoretical distribution of two degrees of freedom; Lower panel: PSD fitting via REDFIT fit