Multiwavelength Variability Study of the Classical BL Lac Object PKS 0735+178 on Timescales Ranging from Decades to Minutes

Abstract

We present the results of our power spectral analysis for the BL Lac object PKS 0735+178 utilizing the Fermi-LAT survey at high-energy γ-rays, several ground-based optical telescopes, and single-dish radio telescopes operating at GHz frequencies. The novelty of our approach is that, by combining long-term and densely sampled intra-night light curves in the optical regime, we were able to construct for the first time the optical power spectrum of the blazar for a time domain extending from 23 years down to minutes. Our analysis reveals that: (i) the optical variability is consistent with a pure red noise, for which the power spectral density can well be approximated by a single power-law throughout the entire time domain probed; (ii) the slope of power spectral density at high-energy γ-rays (∼1), is significantly flatter than that found at radio and optical frequencies (∼2) within the corresponding time variability range; (iii) for the derived power spectra we did not detect any low-frequency flattening, nor do we see any evidence for cut-offs at the highest frequencies down to the noise floor levels due to measurement uncertainties. We interpret our findings in terms of a model where the blazar variability is generated by the underlying single stochastic process (at radio and optical frequencies), or a linear superposition of such processes (in the γ-ray regime). Along with the detailed PSD analysis, we also present the results of our extended (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015) intra-night optical monitoring program and newly acquired optical photo-polarimetric data for the source.

Blazar variability power spectra from radio up to TeV photon energies: Mrk 421 and PKS 2155−304

“…Based on our PSD analyses of multi-band (GHz band radio, R-band, and Fermi−LAT) light curves of the two LBLs, namely PKS 0735+178 and OJ 287 (Goyal et al 2017(Goyal et al , 2018, we have argued that the observed broad-band emission of the blazars is generated in the extended volumes of structured, highly turbulent jets (as opposed to the scenarios postulating a well-defined single "emission zone"). That is because the Fourier decomposition of the analysed light curves did not reveal any single well-defined variability timescale, but instead a wide range of such, manifesting as the colored-noise type of the derived power spectra.…”

“…Therefore, in order to perform the DFT of the observed light curves, we obtained the regular sampling only by linearly interpolating between the two consecutive observed data points on timescales typically 5-7 times smaller than the original observed sampling interval (see Table 1). The distortions in the PSDs due to the discrete sampling and the finite duration of the light curve are described in detail in the Appendix of Goyal et al (2017) and the references therein. In our analysis, the resulting PSDs are generated down to the typical (mean) Nyquist sampling frequency of the observed light curves using the 'Hanning' window function (see also Max-Moerbeck et al 2014b).…”

“…According to Papadakis & Lawrence (1993), averaging about 20 data points in logarithmic space constrains the PSD estimate within 1σ confidence interval. However, following this method would essentially remove a large number of data points from lower frequencies, instead, we bin the 'raw' periodograms by averaging with a constant factor of 1.6 in frequency range (i.e., with a fixed step in logarithmic space) and evaluating the mean power at the representative frequency taken as the geometric mean of each bin, following Isobe et al (2015) and Goyal et al (2017). This value is chosen to have at least two periodograms in each frequency bin except for the first bin.…”

Signature of inverse Compton emission from blazars

“…This indicates that the inferred variability here probes only the noise floor as opposed to intrinsic source based variability which often leads to indices in the range −1.5 − −2.5 (e.g. Mohan et al 2014, 2015, Agarwal et al 2017Goyal et al 2017 and references therein). The bending power law is the best fit model in the light curves of PKS 0235+164: 10-02-2002 and PKS 0528+134: 14-09-2009.…”

“…The periodogram is evaluated using the algorithm presented in Press & Rybicki (1989) in order to achieve fast computational speeds. We performed a linear interpolation of the light curves to populate small (< 200 -300 s; 2-3 points) data gaps and sample the light curves at regular intervals ∆t = 100 s. The LSP is the Fourier periodogram for an evenly sampled light curve which leads to a proper estimation of the underlying PSD as noted in Goyal et al (2017), where it was inferred that the LSP of unevenly sampled light curves tended to be dominated by higher temporal frequencies thus yielding systematically flatter PSD slopes. We use two competing parametric models to infer the PSD shape, a power law model…”