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Study of variability in long-term multiwavelength optical lightcurves of blazar AO 0235+164

Abhradeep Roy Department of High Energy Physics, Tata Institute of Fundamental Research, Homi Bhabha Road, Mumbai-400005, India Alok C. Gupta Aryabhatta Research Institute of Observational Sciences (ARIES), Manora Peak, Nainital 263001, India Key Laboratory for Research in Galaxies and Cosmology, Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China Varsha R. Chitnis Department of High Energy Physics, Tata Institute of Fundamental Research, Homi Bhabha Road, Mumbai-400005, India Sergio A. Cellone Complejo Astronómico El Leoncito (CASLEO, CONICET-UNLP-UNC-UNSJ), San Juan, Argentina Facultad de Ciencias Astronómicas y Geofísicas, Universidad Nacional de La Plata, La Plata, Buenos Aires, Argentina Claudia M. Raiteri INAF-Osservatorio Astrofisico di Torino, Via Osservatorio 20, I-10025 Pino Torinese, Italy Gustavo E. Romero Instituto Argentino de Radioastronomía (CCT-La Plata, CONICET; CICPBA; UNLP), Buenos Aires, Argentina Facultad de Ciencias Astronómicas y Geofísicas, Universidad Nacional de La Plata, La Plata, Buenos Aires, Argentina Paul J. Wiita Department of Physics, The College of New Jersey, 2000 Pennington Rd., Ewing, NJ 08628-0718, USA Anshu Chatterjee Department of High Energy Physics, Tata Institute of Fundamental Research, Homi Bhabha Road, Mumbai-400005, India Jorge A. Combi Facultad de Ciencias Astronómicas y Geofísicas, Universidad Nacional de La Plata, La Plata, Buenos Aires, Argentina Instituto Argentino de Radioastronomía (CCT-La Plata, CONICET; CICPBA; UNLP), Buenos Aires, Argentina Deptamento de Ingeniería Mecánica y Minera, Universidad de Jaén, Campus Las Lagunillas s/n Ed. A3 Jaén, 23071, Spain Mai Liao CAS Key Laboratory for Researches in Galaxies and Cosmology, Department of Astronomy, University of Science and Technology of China, Hefei, Anhui 230026, China School of Astronomy and Space Science, University of Science and Technology of China, Hefei, Anhui 230026, China Arkadipta Sarkar Deutsches Elektronen-Synchrotron, Platanenallee 6, D-15738 Zeuthen, Germany Massimo Villata INAF-Osservatorio Astrofisico di Torino, Via Osservatorio 20, I-10025 Pino Torinese, Italy
Abstract

We present a long-term and intraday variability study on optical multiwaveband (UBVRIU\!BV\!RI) data from the blazar AO 0235+164 collected by various telescopes for \sim44 years (1975–2019). The blazar was found to be significantly variable over the years in all wavebands with a variation of about six magnitudes between its low and active states. The variations in the different wavebands are highly correlated without any time-lag. We did not observe any significant trend in color variation with time, but we observed a bluer-when-brighter trend between the BIB-I color index and the RR-magnitude. Optical BVRBV\!R-band spectral energy distributions always show a convex shape. Significant intraday variability was frequently seen in the quasi-simultaneous observations of AO 0235+164 made on 22 nights in RR and VV-bands by the CASLEO and CAHA telescopes during 1999–2019. We also estimated the central supermassive black-hole mass of 7.9×107M7.9\times 10^{7}M_{\odot} by analyzing the broad Mg II emission line in AO 0235+164’s spectrum. We briefly explore the probable physical scenarios responsible for the observed variability.

galaxies: active – BL Lacertae objects: general – quasars: individual – BL Lacertae objects: individual: AO 0235+164
journal: ApJSfacilities: WEBT, SMARTS, Bok, SO:Kuiper, MMT, CASLEO:JST, CAO:2.2msoftware: Astropy (Astropy Collaboration et al., 2013), DAOPHOT (Stetson, 1987), IRAF (Tody, 1986)

1 Introduction

Refer to caption
Figure 1: Long-term multiwavelength optical (UU, BB, VV, RR, II) lightcurves of AO 0235+164 observed from multiple ground-based telescopes between JD 2442689 (1975 October 3) and JD 2458835 (2019 December 17).

Blazars belong to the radio-loud (RL) class of active galactic nuclei (AGNs). This extremely variable class is the union of BL Lacertae objects (BL Lacs) and flat spectrum radio quasars (FSRQs). Blazars host a large-scale relativistic jet of plasma pointing very close to the observer’s line of sight (Urry & Padovani, 1995). The jet is launched from the very near vicinity of the supermassive black hole (SMBH) of mass 106 – 1010 M at the center of the AGN (e.g., Woo & Urry, 2002). Blazars are characterized by highly variable emission throughout the whole electromagnetic (EM) spectrum, from radio to γ\gamma-rays, and their spectral energy distributions (SEDs) are characterized by two broad humps (Fossati et al., 1998). Blazars display high and variable polarization from radio to optical bands, and emit predominately non-thermal emission in the entire EM spectrum. The low-energy hump is ascribed to synchrotron radiation from relativistic leptons, whereas the high-energy hump arises from inverse Compton (IC) processes and sometimes from hadronic processes (e.g., Marscher, 1983; Mücke et al., 2003; Romero et al., 2017, and references therein).

Blazars display flux variability on diverse timescales ranging from a few minutes to several years. Blazar variability has often been divided into three categories, depending on the cadence of the observations: (i) microvariability (Miller et al., 1989), or intraday variability (IDV) (Wagner & Witzel, 1995), or intra-night variability (INV) (Sagar et al., 2004), focusing on the variability over a day or less; (ii) short-term variability (STV), focusing on variability over days to weeks, (iii) and long-term variability (LTV), focusing on timescales of months to years (e.g. Gupta et al., 2004).

The BL Lac object AO 0235+164 is at redshift z=z= 0.94 (Cohen et al., 1987). Optical spectroscopic and photometric observations of the object have discovered two foreground-absorbing systems at z=z= 0.524 and z=z= 0.851 (Cohen et al., 1987; Nilsson et al., 1996; Raiteri et al., 2007). The flux of the source can be both absorbed and contaminated by these foreground systems, and the stars in them may act as gravitational micro-lenses that could contribute to the observed variability. Abraham et al. (1993) did deep CFHT imaging of AO 0235+164 and reported that the source is weakly amplified by macrolensing / microlensing by stars in the foreground.

AO 0235+164 has been extensively observed in the past from radio to γ\gamma-ray bands either in individual EM bands or quasi-simultaneously in multiple EM bands and has shown variations in all those bands on diverse timescales (e.g., Madejski et al., 1996; Rabbette et al., 1996; Takalo et al., 1998; Qian et al., 2000; Webb et al., 2000; Romero et al., 2000; Raiteri et al., 2006, 2008; Hagen-Thorn et al., 2008; Gupta et al., 2008; Agudo et al., 2011; Ackermann et al., 2012; Fan et al., 2017; Kutkin et al., 2018; Wang & Jiang, 2020, and references therein). It is one of the blazars which has displayed very high and variable optical/NIR polarization up to \sim45 percent (e.g., Impey et al., 1982; Stickel et al., 1993; Fan & Lin, 1999; Cellone et al., 2007; Ikejiri et al., 2011; Itoh et al., 2016, and references therein). In the Hamburg quasar monitoring program (HQM) this source was observed in the optical R band during 1988–1993, during which a 2.36±\pm0.25 magnitude variation was detected; a particularly strong brightening in the source of \sim1.6 magnitude was reported during February 20–22, 1989 (Schramm et al., 1994). In six nights of optical BB and VV bands observations during 21–27 September 1992, the blazar was found in an unusually bright state and IDV was detected in both BB and VV bands (Rabbette et al., 1996). On another occasion, 6 nights of quasi-simultaneous VV and RR band observations in November 1999, revealed IDV with an amplitude of \sim100 percent over timescales of a day, while 0.5 magnitude changes were reported in both bands on a single night (Romero et al., 2000). In multicolor optical/NIR photometric (BVRIJHKBV\!RIJHK) and RR-band optical polarimetric observations of AO  0235+164 during its 2006 December outburst, variability on IDV timescales was detected, with increasing minimum timescale of variability from optical to NIR wavelengths; such variations were even detected in the optical polarization (Hagen-Thorn et al., 2008). In three nights of optical observations of the blazar in January – March 2007, IDV and STV were detected (Gupta et al., 2008).

In quasi-simultaneous optical (VV and RR bands) and radio (22 GHz) observations of AO 0235+164 during 1993–1996, the variability in optical bands showed amplitudes up to 1.5 magnitudes on STV timescales; although the radio variability is less dramatic, in general, it followed the optical behavior (Takalo et al., 1998). For the 1997 AO 0235+164 outburst, quasi-simultaneous multi-wavelength (MW) (radio, optical, NIR, and X-ray) observations were carried out. It was found that the source varied nearly simultaneously over 6 decades in frequency during the outburst and this result was explained in terms of a microlensing event (Webb et al., 2000).

An analysis of this source’s variability over \sim25 years led to the suggestion of a \sim5.7 years quasi-periodicity of the main radio and optical flares (Raiteri et al., 2001); however, the putative next outburst, predicted to peak around February–March 2004, did not occur, and a new analysis of the optical light curves on a longer time span revealed a characteristic variability timescale of \sim8 years, which was also present in the radio data (Raiteri et al., 2006). Recently, optical RR band photometric data taken during 1982–2019 showed 5 cycles of double-peaked periodicity of \sim8.13 years with a secondary peak following the primary one by \sim(1.5–2.0) years (Roy et al., 2022). In another MW campaign from radio to UV bands in 2006–2007, a huge NIR-optical-UV outburst with brightness increase of \sim5 magnitudes during February 19 – 21, 2007 was detected (Raiteri et al., 2008). During a major outburst seen in 2009, changes in radio, optical, X-ray, and γ\gamma-ray bands were found to be strongly associated (Agudo et al., 2011). In another simultaneous MW observing campaign of this blazar between 2008 September and 2009 February, γ\gamma-ray activity was found to be well correlated with a series of NIR/optical flares, accompanied by an increase in the optical degree of polarization; the X-ray light curve showed a different 20-day high state of an unusually soft spectrum which did not match the extrapolation of the optical/UV synchrotron spectrum (Ackermann et al., 2012).

AO 0235+164 is one of the sources that often used to be called OVV (optically violently variable). There are several such objects, like 3C 279, 3C 454.3, 4C 29.45, CTA 102, BL Lacertae, etc. Long-term achromaticity and zero lags have widely been found for these sources (Bonning et al., 2012; Zhang et al., 2021; Fan et al., 2006; Raiteri et al., 2017; Guo et al., 2015). AO 0235+164 is peculiar because it is commonly considered a BL Lac, one of the furthest known, but it shares properties with FSRQs. It is also a complex source because its light is contaminated by the southern AGN, ELISA, and absorbed by an intervening galaxy. This paper has undertaken a detailed analysis of the source’s optical brightness and spectral variability over a very long time span (\sim5 decades) as well as an investigation of its central engine. Our aim is to shed light on the long and short-term behavior of an emblematic BL Lac object through a detailed analysis of what is likely the most massive data set ever assembled for an object of this kind. The paper is organized as follows. In section 2, we provide descriptions of the observations of AO 0235+164. The section 3 gives our data analysis methods and results. We present a discussion and conclusions in section 4 and section 5, respectively.

2 Observations

Most of the optical UBVRIUBVRI observations of AO  0235+164 we have employed in this work are taken from The Whole Earth Blazar Telescope111https://www.oato.inaf.it/blazars/webt (WEBT) (Villata et al., 2002; Raiteri et al., 2017) which is an international collaboration of optical, near-infrared, and radio observers. WEBT has organized several monitoring campaigns on the blazar AO  0235+164, with the participation of many tens of observers and telescopes all around the world. Later, this source was studied by the WEBT and by its GLAST-AGILE Support Program (GASP) (Villata et al., 2008, 2009), which was started in 2007 to record quasi-simultaneous data of various blazars observed by the AGILE and Fermi (formerly GLAST) satellites. WEBT/GASP data on AO  0235+164 were published in Raiteri et al. (2001, 2005, 2006, 2008) and Ackermann et al. (2012). Raiteri et al. (2005) prescribed ways to remove the contribution of the southern galaxy ELISA from the observed optical flux densities and estimated the amount of absorption towards the source in excess of that from our Galaxy in X-ray, ultraviolet, optical, and near-infrared bands.

The WEBT and GASP data were calibrated following a common prescription, i.e., with the same photometry for the same reference stars. For calibration of the AO  0235+164 observations, the adopted photometric sequence includes stars 1, 2, and 3 from Smith et al. (1985). To build a reliable lightcurve for further analysis, clear outliers were removed and minor systematic offsets between various datasets were corrected.

AO  0235+164 was also observed with the 2.2 m telescope of Calar Alto Astronomical Observatory (CAHA, Spain) in November – December 2005, using the CAFOS instrument in imaging polarimetry mode, and photometric data were obtained by adding up the ordinary and extraordinary fluxes from each individual image (Cellone et al., 2007). Photometric data were also obtained with the 2.15 m telescope at Complejo Astronómico El Leoncito (CASLEO, Argentina) along several runs in November 1999, December 2000, August 2004, and January 2005. Results from these data were published in Romero et al. (1999, 2000, 2002) and in two papers by the WEBT collaboration focused on this blazar (Raiteri et al., 2005, 2006). Data from a more recent (December 2019) observing run with the same telescope were used in Roy et al. (2022). Magnitude calibration to the standard system was done using our own photometry of Landolt’s (2009) fields as well as standard stars in the field of AO  0235+164 (Smith et al., 1985; González-Pérez et al., 2001).

We also collected the publicly available optical RR and VV-band data of AO 0235+164, taken at Steward Observatory222http://james.as.arizona.edu/~psmith/Fermi/DATA/Rphotdata.html, University of Arizona. These measurements employed the 2.3 m Bok and 1.54 m Kuiper telescopes between 4 October 2008 and 12 February 2018, using the SPOL CCD Imaging/Spectropolarimeter attached to those two telescopes. Details about the instrument, observation, and data analysis are given in Smith et al. (2009). In addition, we included the optical-BVRBVR data from the Small and Moderate Aperture Research Telescope System (SMARTS) public archive333http://www.astro.yale.edu/smarts/glast/home.php#. The SMARTS consortium is part of the Cerro Tololo Inter-American Observatory (CTIO), Chile, and has been observing Fermi-Large Area Telescope (LAT)-monitored blazars in the optical BB, VV, RR and NIR JJ and KK bands. Details about the SMARTS instruments, observations, and data analysis procedures are given in Bonning et al. (2012). These standard magnitudes observed by CASLEO, CAHA, SMARTS, and the Steward observatory were further corrected for the southern galaxy ELISA following Raiteri et al. (2005). We also added other RR-band optical photometric data from the literature (Takalo et al., 1998; Hagen-Thorn et al., 2008).

3 Data analysis methods and results

Refer to caption
Figure 2: 15-minute averaged UBVIUBVI magnitudes versus RR-magnitude plots for correlation study. UU, BB, VV, and II-band observations show high linear correlation with RR-band data. All the plots are fitted with straight lines.
Refer to caption
Figure 3: Results of discrete cross-correlation analysis of UU, BB, VV, and II-band with respect to RR-band in the full time range.
Figure 4: (a) Color variation with time. (b) Color variation with optical RR magnitude. The red line in each panel represents the straight line fit. Fit parameters are given in Table 2 and Table 3 respectively.

We combined all the optical UU, BB, VV, RR, II band data to plot the long term (1974–2020) MW lightcurves of blazar AO 0235+164 (Figure 1). We removed the observations with errors of more than 0.1 magnitudes and studied long-term and intraday variability, color variation, spectral properties, and inter-band correlations.

3.1 Flux variability studies

We use different tools on the observed optical magnitudes to quantify the variability timescales and the corresponding significance in multiple optical wavebands.

Table 1: Result of flux variability on optical UBVRIU\!BV\!RI long-term lightcurves of AO 0235+164
Optical Total χred.2\chi^{2}_{\mathrm{red.}} χ0.999,red.2\chi^{2}_{0.999,\mathrm{red.}} Status Variability
filter Obs. amplitude (%)
UU 109 904.5 1.47 V 548.8
BB 894 3246.7 1.15 V 590.9
VV 1403 5968.4 1.12 V 589.0
RR 5675 8715.5 1.06 V 718.8
II 1173 3555.2 1.13 V 567.5

Note. — In the fourth column ’V/NV’ represents variable/non-variable status.

3.1.1 The χ2\chi^{2}test

For a time series of flux density observations, the χ2\chi^{2} is defined as,

χ2=i=1N(i¯)2εi2\chi^{2}=\sum_{i=1}^{N}\frac{(\mathcal{M}_{i}-\mathcal{\bar{M}})^{2}}{\varepsilon^{2}_{i}} (1)

where i\mathcal{M}_{i} is the magnitude obtained at the ithi^{\text{th}} observation, εi\varepsilon_{i} is the corresponding error in measurement, and ¯\mathcal{\bar{M}} is the average magnitude. If the obtained χ2\chi^{2} value is higher than the critical χ2\chi^{2} value at 99.9 per cent significance level, we consider the source as variable. The critical value (χ0.999,d2\chi^{2}_{0.999,d}) depends on the degrees of freedom (dd) of the dataset. The reduced χ2\chi^{2} values listed in Table 1 indicate that the source exhibits significant flux variations in all the optical wavebands.

3.1.2 Variability amplitude

According to the relation given by Heidt & Wagner (1996), we estimated the variability amplitudes (VMV_{M}) in percentage for the lightcurves in different wavelengths using the following formula,

VM=100×(maxmin)22ε¯2(%)V_{M}=100\times\sqrt{(\mathcal{M}_{\mathrm{max}}-\mathcal{M}_{\mathrm{min}})^{2}-2\,\bar{\varepsilon}^{2}}\;(\%) (2)

where max\mathcal{M}_{\mathrm{max}} and min\mathcal{M}_{\mathrm{min}} are the maximum and minimum observed magnitude in a lightcurve, respectively, while ε¯\bar{\varepsilon} is the average error in magnitude measurements. We list the calculated variability of amplitudes in Table 1.

3.1.3 Correlation study

To study the inter-band correlations, we first generated 15-minute binned optical UBVRIU\!BV\!RI lightcurves, and plotted the average UU, BB, VV, and II-magnitudes against the average RR-magnitudes for the time bins when the source was observed at both the wavebands (Figure 2). The magnitude-vs-magnitude plots show very good linear correlations. To take the uncertainty of magnitude measurements into account, we simulated 10000 datasets assuming that each magnitude measurement is Gaussian distributed. Then we calculated the mean and standard deviation of the Pearson correlation coefficients of all simulated datasets. We obtained high correlations (>0.9>0.9) with small uncertainties (<0.003<0.003) between all wavebands.

Moreover, to find any time lag between the correlated optical lightcurves we computed the discrete correlation function (DCF) from the unbinned multiwavelength light curves, as the light curves consist of discrete data points. Following the method of Edelson & Krolik (1988), we computed the unbinned DCF (UDCF) between the ithi^{\rm th} data point in one waveband (aa) and the jthj^{\rm th} data point in another (bb) as

UDCFij=(aia¯)(bjb¯)σaσb,{\rm UDCF}_{ij}=\frac{(a_{i}-\bar{a})(b_{j}-\bar{b})}{\sigma_{a}\sigma_{b}}, (3)

where a¯\bar{a} and b¯\bar{b} are the mean of the observed magnitudes, and σa\sigma_{a} and σb\sigma_{b} are the standard deviations of the corresponding datasets. Next, we calculated the discrete correlation function (DCF) at a certain time lag τ\tau by averaging the UDCFij{\rm UDCF}_{ij}s whose corresponding time lags Δtij=tiatjb\Delta t_{ij}=t^{a}_{i}-t^{b}_{j} lie within the range [τΔτ2\tau-\frac{\Delta\tau}{2}, τ+Δτ2\tau+\frac{\Delta\tau}{2}] (Δτ\Delta\tau is the time lag bin width), such that,

DCF(τ)=1nUDCFij(τ).{\rm DCF}(\tau)=\frac{1}{n}\sum{\rm UDCF}_{ij}(\tau). (4)

Following the suggestion of White & Peterson (1994), we computed the mean magnitudes (a¯\bar{a} and b¯\bar{b}) and the standard deviations (σa\sigma_{a} and σb\sigma_{b}) in Equation 3 using only those data points who fall within a given time lag bin, as the mean and standard deviation keep on changing for a time series originated from a stochastic process such as blazar emission. The error in the DCF(τ\tau) computation in each bin is calculated as

σDCF(τ)=1M1k=1M(UDCFkDCF(τ))2.\sigma_{\rm DCF}(\tau)=\frac{1}{M-1}\sqrt{\sum_{k=1}^{M}({\rm UDCF}_{k}-{\rm DCF}(\tau))^{2}}. (5)

Figure 3 shows the DCFs of UBVIUBVI bands with respect to the RR-band observations. In all cases, the DCFs peak at zero time lag, except the UU-band vs RR-band DCF due to poor data sampling in the UU-band. This explains the strong linearity in Figure 2 and implies that the emission at all optical wavebands are coming from the same region in the jet and are produced from the same radiation mechanism.

3.1.4 Color Variations

Refer to caption
Figure 5: An example frame of the AO 0235+164 optical SED animation that is available in the HTML version of this article. The duration of the animation is 1 minute and it contains a total of 360 one-day averaged optical SEDs, having 6 SEDs per frame. The observation dates of the SEDs are given in the plot legend.
Refer to caption
Figure 6: Examples of AO 0235+164 optical intraday SEDs during three different states of brightness: (i) the green lines represent SED during quiescent states (ν\nuFν (erg cm-2 s-1) << 10-12), (ii) the blue lines show SED during moderately bright states (10-12 << ν\nuFν (erg cm-2 s-1) << 3×\times10-11), (iii) the red lines show SED during outbursts (ν\nuFν (erg cm-2 s-1) >> 5×\times10-11). The black lines are examples of SED with spectral hardening on JD 2446763 and JD 2453230.
Figure 7: (a) Variation of spectral index (αVR\alpha_{VR}) with RR-band magnitude. (b) Variation of αVR\alpha_{VR} with time. The red line at each panel represents the linear fit.
Table 2: Color variation with time in optical UBVRIU\!BV\!RI long-term lightcurves of AO 0235+164
CI mm cc ρ\rho pp
UBU-B -1.52E-05 3.74E+01 -2.06E-01 8.28E-02
BVB-V 6.58E-06 -1.52E+01 1.42E-01 4.79E-03
VRV-R -5.34E-06 1.38E+01 -9.19E-02 1.39E-02
RIR-I 1.83E-05 -4.40E+01 2.85E-01 1.74E-08
UIU-I 5.63E-05 -1.35E+02 4.03E-01 1.88E-03
BIB-I 4.16E-05 -9.92E+01 4.50E-01 3.41E-11

Note. — In the column headings: CI: color indices; mm = slope; cc = intercept; ρ\rho = Pearson coefficient; pp = null hypothesis probability for Figure 4a

Table 3: Color variation with RR-band magnitude in optical UBVRIU\!BV\!RI long-term lightcurves of AO 0235+164
CI mm cc ρ\rho pp
UBU-B -1.36E-01 2.37E+00 -5.37E-01 3.35E-05
BVB-V 1.62E-02 7.04E-01 1.41E-01 7.41E-03
VRV-R -3.54E-03 7.98E-01 -2.58E-02 4.92E-01
RIR-I 1.62E-02 7.00E-01 1.37E-01 7.59E-03
UIU-I -6.47E-02 3.85E+00 -2.07E-01 1.30E-01
BIB-I 6.23E-02 1.66E+00 3.66E-01 1.69E-07

Note. — In the column headings: CI: color indices; mm = slope; cc = intercept; ρ\rho = Pearson coefficient; pp = null hypothesis probability for Figure 4b

The term ‘color’ denotes the magnitude difference between two quasi-simultaneous observations at two different wavebands. We plotted the variation of optical colors (UBU-B, BVB-V, VRV-R, RIR-I, and BIB-I) with time and RR-magnitude in Figure 4. We listed the results of a straight line (Y=mX+cY=mX+c) fitting to all these plots in Table 2 and Table 3. The linear fits of the color versus time plots do not show any trend, except for the rather sparsely sampled (BIB-I) color, which has a high slope (4.16×\times10-5) in Figure 4a, along with the highest Pearson correlation coefficient (0.45), and the lowest null hypothesis probability (3.41×\times10-11). Among the color versus magnitude relations, the strongest relationship is between (BIB-I) and RR (Figure 4b), having a positive slope (6.23×\times10-2) with the highest Pearson coefficient (0.37) and the lowest pp-value (1.69×\times10-7) (Table 3), indicates a bluer-when-brighter (BWB) trend when the widest range of the available colors is considered.

Table 4: Spetral index variation with RR-band magnitude and time in optical UBVRIU\!BV\!RI long-term lightcurves of AO 0235+164
Dependency mm cc ρ\rho pp
αVR\alpha_{VR} vs RR -2.01E-02 3.30E+00 -2.58E-02 4.92E-01
αVR\alpha_{VR} vs JD -3.03E-05 7.74E+01 -9.19E-02 1.39E-02

Note. — In the column headings: mm = slope; cc = intercept; ρ\rho = Pearson coefficient; pp = null hypothesis probability for Figure 7.

Table 5: Equivalence between internal field star numbering in the CASLEO/CAHA data used in the IDV analyses and field-star numbering in other standard star charts during different observation seasons
Season CASLEO/CAHA Heidelberga GKM2001b
1999–2001 12 18 10
(CASLEO) 14 C1 19
15 16 11
17 11
18 13
10 18
12 16
2004–2005 2 18 10
(CASLEO) 4 C1 19
5 16 11
6 18
7 17
2005 12 8 10
(CAHA) 11 C1 19
12 11
13 13
14 17
15 18
16 6 11
17 16
2018–2019 2 18 10
(CASLEO) 4 C1 19
5 16 11
6 18
7 17
8 16
Table 6: Result of scaled C-criterion and F-test for IDV on AO 0235+164 differential lightcurves from CASLEO and CAHA
Date JD Band No. of S1, S2 Γ\Gamma CΓC_{\Gamma} FΓF_{\Gamma} Fc0.005F^{0.005}_{c} Status Final
obs. Status
1999 Nov 2 2451485 VV 23 2,3 0.8886 11.3640 129.1405 3.1246 V V
2,6 1.0867 12.9184 166.8856 3.1912 V
2,10 1.6876 8.1627 66.6298 3.1246 V
2,11 0.7431 13.4002 179.5650 3.1246 V
1999 Nov 3 2451486 VV 22 2,3 1.0707 5.6976 32.4624 3.1347 V V
2,11 0.8841 6.0726 36.8768 3.1347 V
1999 Nov 4 2451487 RR 30 2,3 1.0059 8.4058 70.6582 2.6737 V V
2,11 0.6639 9.8857 97.7278 2.6737 V
VV 30 2,3 0.9994 8.9281 79.7104 2.6737 V V
2,11 0.8286 9.6683 93.4770 2.6737 V
1999 Nov 5 2451488 RR 23 2,3 1.4994 1.5631 2.4433 3.1246 NV NV
2,11 0.9852 1.9303 3.7260 3.1246 NV
VV 22 2,3 1.4403 3.0342 9.2064 3.1347 V V
1999 Nov 6 2451489 RR 30 2,3 0.8471 17.5775 308.9682 2.6737 V V
2,6 0.9769 12.3281 151.9824 2.6737 V
2,7 1.3573 9.9373 98.7501 2.7048 V
2,8 1.3805 9.8381 96.7876 2.7048 V
2,10 1.6936 6.8657 47.1376 2.6737 V
2,11 0.5616 15.4338 238.2019 2.6737 V
VV 29 2,3 0.8485 18.1892 330.8486 2.7233 V V
2,6 1.0013 11.7527 138.1254 2.7233 V
2,7 1.3527 12.5480 157.4516 2.7397 V
2,8 1.4133 13.4172 180.0214 2.7397 V
2,10 1.5626 17.6674 312.1376 2.7233 V
2,11 0.7018 17.9948 323.8145 2.7233 V
1999 Nov 7 2451490 RR 11 2,3 0.9562 3.5930 12.9095 5.8479 V PV
2,4 1.9798 2.2801 5.1990 5.8479 NV
2,6 1.1143 4.3903 19.2751 5.8479 V
2,10 1.9703 1.7073 2.9148 5.8479 NV
2,11 0.6197 2.9496 8.7003 5.8479 V
VV 12 2,3 0.9382 2.9304 8.5871 5.3191 V PV
2,4 1.7807 1.9342 3.7410 5.3191 NV
2,6 1.1169 2.8931 8.3701 5.3191 V
2,10 1.7653 2.1046 4.4292 5.3191 NV
2,11 0.7772 4.3359 18.7997 5.3191 V

Note. — S1 and S2 are the comparison and control star numbers, respectively, used for the IDV tests. Star numbers follow the star maps shown in Table 5.

Table 6: Result of scaled C-test and F-test for IDV on AO 0235+164 differential lightcurves from CASLEO and CAHA (continued…)
Date JD Band No. of S1, S2 Γ\Gamma CΓC_{\Gamma} FΓF_{\Gamma} Fc0.005F^{0.005}_{c} Status Final
obs. status
2000 Dec 21 2451900 RR 10 2,3 0.9446 2.3638 5.5876 6.5402 NV PV
2,6 1.0793 4.9877 24.8767 6.5402 V
2,7 1.5020 2.0187 4.0753 6.5402 NV
2,8 1.5289 1.9985 3.9939 6.5402 NV
2,9 0.8790 2.5120 6.3100 6.5402 NV
2,11 0.6246 7.4168 55.0085 6.5402 V
VV 10 2,3 0.9509 3.4671 12.0208 6.5402 V PV
2,6 1.1202 2.4789 6.1449 6.5402 NV
2,7 1.5357 1.8729 3.5079 6.5402 NV
2,8 1.6064 2.0031 4.0124 6.5402 NV
2,9 1.0966 3.7299 13.9120 6.5402 V
2,11 0.7842 1.5920 2.5343 6.5402 NV
2000 Dec 23 2451902 RR 10 2,3 0.8588 4.4475 19.7803 6.5402 V V
2,6 0.9890 5.1629 26.6559 6.5402 V
2,7 1.3855 3.5919 12.9020 6.5402 V
2,8 1.4091 2.8222 7.9646 6.5402 V
2,9 0.8000 4.6739 21.8451 6.5402 V
2,11 0.5664 5.3690 28.8267 6.5402 V
2,13 1.7083 3.0181 9.1089 6.5402 V
VV 11 2,3 0.8509 6.5241 42.5634 5.8479 V PV
2,6 1.0031 5.4277 29.4602 5.8479 V
2,7 1.3714 5.0139 25.1395 5.8479 V
2,8 1.4341 5.2879 27.9619 5.8479 V
2,9 0.9797 1.4805 2.1919 5.8479 NV
2,11 0.7013 5.1765 26.7965 5.8479 V
2,13 1.5668 4.3770 19.1586 5.8479 V

Note. — S1 and S2 are the comparison and control star numbers respectively used for the IDV tests. Star numbers follow the star maps shown in Table 5.

Table 6: Result of scaled C-test and F-test for IDV on AO 0235+164 differential lightcurves from CASLEO and CAHA (continued…)
Date JD Band No. of S1, S2 Γ\Gamma CΓC_{\Gamma} FΓF_{\Gamma} Fc0.005F^{0.005}_{c} Status Final
obs. status
2001 Nov 9 2452223 RR 12 2,11 1.2042 4.6476 21.5998 5.3191 V V
VV 12 2,3 1.8778 2.5035 6.2675 5.3191 NV NV
2,4 3.6191 1.1450 1.3111 5.3191 NV
2,9 2.2039 1.2380 1.5326 5.4171 NV
2,10 3.5871 2.0056 4.0226 5.3191 NV
2,11 1.5366 1.7566 3.0857 5.3191 NV
2001 Nov 10 2452224 RR 10 2,3 2.3728 1.0570 1.1172 6.5402 NV NV
2,9 2.2429 1.1058 1.2229 6.5402 NV
2,11 1.5595 0.9395 0.8826 6.5402 NV
VV 10 2,3 2.3876 1.0788 1.1637 6.5402 NV NV
2,9 2.7847 1.3860 1.9209 6.5402 NV
2,11 1.9713 0.9038 0.8168 6.5402 NV
2001 Nov 11 2452225 RR 14 2,3 2.0291 1.4125 1.9951 4.5724 NV NV
2,9 1.5447 1.2505 1.5638 4.6425 NV
2,11 1.3171 1.6860 2.8427 4.5724 NV
VV 14 2,3 2.0291 1.4125 1.9951 4.5724 NV NV
2,9 1.5447 1.2505 1.5638 4.6425 NV
2,11 1.3171 1.6860 2.8427 4.5724 NV
2001 Nov 12 2452226 RR 12 2,3 1.8479 1.5819 2.5025 5.3191 NV PV
2,11 1.2074 3.0203 9.1222 5.3191 V
VV 12 2,3 1.8704 1.9230 3.6980 5.3191 NV NV
2,4 3.5981 1.0281 1.0571 5.3191 NV
2,10 3.5672 2.3374 5.4634 5.3191 NV
2,11 1.5330 1.5642 2.4468 5.3191 NV
2001 Nov 13 2452227 RR 11 3,4 2.0213 1.1434 1.3073 5.8479 NV NV
VV 11 3,4 1.1840 0.6858 0.4703 5.8479 NV NV

Note. — S1 and S2 are the comparison and control star numbers respectively used for the IDV tests. Star numbers follow the star maps shown in Table 5.

Table 6: Result of scaled C-test and F-test for IDV on AO 0235+164 differential lightcurves from CASLEO and CAHA (continued…)
Date JD Band No. of S1, S2 Γ\Gamma CΓC_{\Gamma} FΓF_{\Gamma} Fc0.005F^{0.005}_{c} Status Final
obs. status
2005 Jan 16 2453387 RR 11 2,3 1.5238 3.8074 14.4962 5.8479 V V
2,4 3.3465 2.7388 7.5010 5.8479 V
2,6 3.3316 2.7442 7.5308 5.8479 V
2,7 1.4051 3.4058 11.5996 5.8479 V
2005 Nov 2 2453677 RR 32 2,3 1.2848 6.4237 41.2636 2.5846 V V
2,4 0.8959 4.5227 20.4545 2.5846 V
2,5 0.2571 4.3013 18.5013 2.5846 V
2,6 0.5453 3.9310 15.4528 2.5846 V
2,7 0.5844 4.9283 24.2884 2.5846 V
2,8 0.4738 7.0560 49.7865 2.5846 V
2,9 0.3415 6.5374 42.7373 2.5846 V
2,10 0.3397 4.0828 16.6695 2.5846 V
2005 Nov 4 2453679 RR 12 2,3 0.8534 4.3059 18.5409 5.3191 V V
2,4 0.5599 3.7978 14.4235 5.3191 V
2,5 0.1421 5.0805 25.8111 5.3191 V
2,6 0.3029 5.3341 28.4524 5.3191 V
2,7 0.3534 7.6846 59.0525 5.3191 V
2,8 0.2839 4.3153 18.6220 5.3191 V
2,9 0.1914 11.0664 122.4647 5.3191 V
2,10 0.1875 5.4019 29.1804 5.3191 V
2005 Nov 5 2453680 RR 44 2,3 0.9749 10.6766 113.9894 2.2266 V V
2,4 0.6398 9.3431 87.2939 2.2266 V
2,5 0.1942 11.0439 121.9674 2.2266 V
2,6 0.3721 10.7338 115.2142 2.2266 V
2,7 0.4059 10.2100 104.2433 2.2266 V
2,8 0.3427 8.3494 69.7127 2.2266 V
2,9 0.2399 12.1459 147.5239 2.2266 V
2,10 0.2340 8.5775 73.5744 2.2341 V
2005 Nov 6 2453681 RR 40 2,3 1.0022 7.8517 61.6495 2.3212 V V
2,4 0.6946 8.8524 78.3645 2.3212 V
2,5 0.2051 6.6830 44.6620 2.3212 V
2,6 0.4022 7.6630 58.7211 2.3212 V
2,8 0.3694 5.9489 35.3890 2.3212 V
2,9 0.2576 6.8684 47.1751 2.3212 V
2,10 0.2563 5.1520 26.5433 2.3212 V

Note. — S1 and S2 are the comparison and control star numbers respectively used for the IDV tests. Star numbers follow the star maps shown in Table 5.

Table 6: Result of scaled C-test and F-test for IDV on AO 0235+164 differential lightcurves from CASLEO and CAHA (continued…)
Date JD Band No. of S1, S2 Γ\Gamma CΓC_{\Gamma} FΓF_{\Gamma} Fc0.005F^{0.005}_{c} Status Final
obs. status
2005 Nov 8 2453683 RR 28 2,3 0.9329 2.4256 5.8834 2.7940 NV NV
2,4 0.6336 2.3363 5.4585 2.7770 NV
2,5 0.1788 1.7843 3.1836 2.7770 NV
2,6 0.3598 2.2163 4.9120 2.7770 NV
2,7 0.4059 1.4945 2.2335 2.7770 NV
2,8 0.3451 2.1606 4.6682 2.9002 NV
2,9 0.2318 1.3895 1.9307 2.7770 NV
2005 Dec 5 2453710 RR 20 2,3 1.4796 1.4053 1.9748 3.4317 NV NV
2,4 1.0247 0.7240 0.5242 3.4317 NV
2,5 0.3133 0.9355 0.8752 3.4317 NV
2,6 0.6030 1.1896 1.4151 3.4317 NV
2,8 0.5634 1.0332 1.0674 3.4317 NV
2,9 0.3979 1.0716 1.1482 3.4317 NV
2,10 0.3915 0.8994 0.8089 3.4317 NV
2005 Dec 6 2453711 RR 16 2,3 1.4092 2.1709 4.7129 4.0698 NV PV
2,4 0.9785 3.7432 14.0118 4.0698 V
2,5 0.2848 2.1323 4.5467 4.0698 NV
2,6 0.5570 2.7562 7.5967 4.0698 V
2,7 0.6157 1.8489 3.4186 4.0698 NV
2,8 0.5266 1.3717 1.8815 4.0698 NV
2,8 0.5266 1.3717 1.8815 4.0698 NV
2,9 0.3691 2.4056 5.7869 4.0698 NV
2,10 0.3688 2.0671 4.2727 4.0698 NV
2019 Dec 17 2458835 RR 30 9,10 1.3151 1.2773 1.6315 2.6740 NV NV
9,11 0.7377 1.3155 1.7307 2.6740 NV
9,12 1.0425 1.0698 1.1445 2.6740 NV

Note. — S1 and S2 are the comparison and control star numbers respectively used for the IDV tests. Star numbers follow the star maps shown in Table 5.

Table 7: Result of power enhanced F-test and nested ANOVA test for IDV on AO 0235+164 differential lightcurves from CASLEO and CAHA
Obs. Band No. of Power enhanced F-test Nested ANOVA test Status Variability doubling
date Obs. Comp. amplitude(%) timescale
star DOF(ν1\nu_{1},ν2\nu_{2}) FenhF_{\text{enh}} Fc0.005F^{0.005}_{c} DOF(ν1\nu_{1},ν2\nu_{2}) FF Fc0.005F^{0.005}_{c} (days)
1999 Nov 2 VV 23 2 (22, 87) 116.132 2.209 (5, 17) 58.924 5.075 V 43.99 0.103
1999 Nov 3 VV 22 2 (21, 42) 34.529 2.540 (5, 16) 10.920 5.212 V 24.47 0.145
1999 Nov 4 VV 30 2 (29, 58) 86.046 2.216 (7, 22) 38.922 4.109 V 34.48 0.106
RR 30 (29, 58) 82.016 2.216 (7, 22) 40.356 4.109 V 32.59 0.083
1999 Nov 5 VV 22 2 (21, 21) 9.207 3.216 (5, 16) 4.426 5.212 NV 10.94 0.140
RR 23 (22, 44) 2.951 2.487 (5, 17) 9.426 5.075 V 9.03 0.335
1999 Nov 6 VV 29 2 (28, 166) 211.363 1.960 (7, 21) 58.114 4.179 V 36.79 0.092
RR 30 (29, 170) 107.913 1.941 (7, 22) 74.686 4.109 V 37.90 0.085
1999 Nov 7 VV 12 2 (11, 55) 6.392 2.854 PV 9.13 0.170
RR 11 (10, 50) 6.413 2.988 PV 5.36 0.244
2000 Dec 21 VV 10 2 (9, 54) 4.813 3.055 PV 6.95 0.275
RR 10 (9, 54) 6.73 3.055 PV 7.67 0.428
2000 Dec 23 VV 11 2 (10, 70) 10.314 2.846 PV 20.58 0.200
RR 10 (9, 63) 14.542 2.989 PV 14.18 0.180
2001 Nov 9 VV 12 2 (11, 54) 2.345 2.863 NV 12.13 0.372
RR 12 (11, 22) 5.91 3.612 PV 12.73 0.441
2001 Nov 10 VV 10 2 (9, 27) 1.152 3.557 NV 8.49 0.227
RR 10 (9, 27) 1.054 3.557 NV 5.64 0.660
2001 Nov 11 VV 14 2 (13, 38) 2.02 2.923 NV 9.63 0.364
RR 14 (13, 38) 2.02 2.923 NV 9.63 0.364
2001 Nov 12 VV 12 2 (11, 44) 2.212 2.969 NV 12.06 0.539
RR 12 2 (11, 22) 3.928 3.612 PV 11.74 0.856
2001 Nov 13 VV 11 3 (10, 10) 0.470 5.847 NV 10.81 0.160
RR 11 3 (10, 10) 1.307 5.847 NV 10.19 0.178
2005 Jan 16 RR 11 2 (10, 40) 9.842 3.117 PV 32.92 0.095
2005 Nov 2 RR 32 2 (31, 247) 27.709 1.868 (7, 24) 37.156 3.991 V 8.98 0.189
2005 Nov 4 RR 12 2 (11, 88) 31.995 2.689 V 6.59 0.166
2005 Nov 5 RR 44 2 (43, 343) 124.459 1.713 (10, 33) 16.301 3.26 V 13.60 0.146
2005 Nov 6 RR 40 2 (39, 273) 57.755 1.767 (9, 30) 87.95 3.45 V 9.79 0.227
2005 Nov 8 RR 28 2 (27, 182) 4.371 1.965 (6, 21) 0.449 4.393 PV 3.18 0.365
2005 Dec 5 RR 20 2 (19, 133) 1.067 2.200 (4, 15) 14.394 5.803 PV 2.61 0.391
2005 Dec 6 RR 16 2 (15, 120) 4.863 2.373 PV 3.53 0.746
2019 Dec 17 RR 30 9 (29, 87) 1.453 2.075 (7, 22) 2.341 4.109 NV 7.74 0.038

Note. — Comparison star numbers follow the star maps shown in Table 5.

3.1.5 Spectral Variations and SEDs

We plotted the optical (BVRBV\!R) spectral energy distributions for the nights where observations were taken at all of these three filters. Following the prescription of Raiteri et al. (2005), we took into account the total absorption by the Milky Way galaxy and the foreground absorber at z=0.524z=0.524, and subtracted the extinction magnitudes (AU=2.519A_{U}=2.519, AB=1.904A_{B}=1.904, AV=1.473A_{V}=1.473, AR=1.260A_{R}=1.260, AI=0.902A_{I}=0.902) from the calibrated magnitudes of respective wavebands and then converted them into extinction-corrected flux densities, FνF_{\nu}. The accompanying video contains one-day averaged optical SEDs for those 360 nights (An example frame is shown in Figure 5). Figure 6 shows a few examples of SEDs of low, moderate, and high flux states, plotted in (νFν\nu F_{\nu}ν\nu) format. Mostly, the SEDs have a declining shape following a power law. However, there are evidences of spectral hardening on several nights (e.g., JD 2445337, JD 2445721, JD 2448889, JD 2452901, JD 2453230).

From the one-day binned multiwavelength lightcurves we calculated the spectral indices (αVR\alpha_{VR}) for all the days when the source was observed in both VV and RR bands, using the formula given by Wierzcholska et al. (2015) on extinction corrected magnitudes, as

αVR=0.4(VR)log(νV/νR),\alpha_{VR}=\frac{0.4(V-R)}{\log(\nu_{V}/\nu_{R})}\,, (6)

where νV\nu_{V} and νR\nu_{R} respectively represent the effective frequencies of VV and RR band filters (Bessell, 2005). We plotted the variation of spectral indices with time and RR-band magnitude (Figure 7) and listed the results of linear fits, Pearson coefficient, and null hypothesis probability in Table 4. We do not find any significant long-term variation of the spectral index with time, nor is there a correlation with RR-magnitude.

3.2 Intraday Variability

Refer to caption
Figure 8: Some intraday lightcurves of AO 0235+164 on nights when the source showed different states of variability. S1 and S2 represent the comparison and control star respectively. In some panels, the differential lightcurve of the control star is shifted to bring it into the same frame of the blazar DLC for better visual comparison of variability.

We applied four frequently used statistical tests for IDV: scaled CC-criterion, scaled FF-test, the power-enhanced FF-test, and the nested analysis of variance (ANOVA) test (de Diego, 2014; de Diego et al., 2015; Zibecchi et al., 2017, 2020) to detect statistically significant intraday flux variability in AO 0235+164 lightcurves observed by CASLEO and CAHA telescopes. These tests mainly compare the variations in blazar magnitudes with the variations in magnitudes of one or more stars within the field-of-view of the blazar and have different advantages and disadvantages. We collected data from multiple field stars along with the blazar data (Table 5). We applied the first three methods on the intraday differential lightcurves of AO 0235+164 where at least 10 observations were recorded per night with at least one optical filter between 1999 November 2 to 2019 December 17. We employed the nested ANOVA test only on lightcurves having at least 20 observations per night.

Refer to caption
Figure 9: Spectral fitting of AO 0235+164, where the black line is the original spectrum while the green line is the single power law for the fitted continuum. The inset shows Mg II line fitting where the blue, green, and red lines are the narrow, broad, and total components, respectively.

3.2.1 Scaled C-criterion

Differential photometry, where the blazar magnitudes are compared to one or more stars in the same field of view, is the usual technique for obtaining blazar lightcurves free from the effects of any non-astrophysical fluctuations. The simplest differential photometry involves a single comparison star, while a second star, whose magnitudes are measured against the same comparison star, is used for a stability check. We denote B, S1, and S2 as the blazar, comparison, and control star, respectively. The variability test requires two differential lightcurves (DLC): (blazar–comparison star) and (control star–comparison star). The latter is believed to be affected only by instrumental fluctuations as any known or suspected variable star can be discarded.

Jang & Miller (1997) and Romero et al. (1999) introduced a parameter CC defined as C=σBS1/σS2S1C=\sigma_{\rm B-S1}/\sigma_{\rm S2-S1}, where σBS1\sigma_{\rm B-S1} and σS2S1\sigma_{\rm S2-S1} are the standard deviations in blazar DLC and control star DLC, respectively. The blazar is considered to be variable with 99.5 per cent confidence level if CC is greater than a critical value of 2.576.

Howell et al. (1988) pointed out that it is important to select non-variable stars with magnitudes close to the blazar magnitude as comparison and control stars. Otherwise, even if the blazar is non-variable, there will be difference between σBS1\sigma_{\rm B-S1} and σS2S1\sigma_{\rm S2-S1} due to differences in photon statistics and other random-noise terms (sky, read-out noise). To use field stars with different magnitude levels, Howell et al. (1988) suggests calculating a correction factor Γ\Gamma to scale σS2S1\sigma_{\rm S2-S1} to the instrumental level of σBS1\sigma_{\rm B-S1} for proper comparison. Γ\Gamma can be estimated using the following formula:

Γ2=(NS2NB)2[NS12(NB+P)+NB2(NS1+P)NS22(NS1+P)+NS12(NS2+P)]\Gamma^{2}=\left(\frac{N_{\rm S2}}{N_{B}}\right)^{2}\left[\frac{N_{\rm S1}^{2}(N_{B}+P)+N_{B}^{2}(N_{\rm S1}+P)}{N_{\rm S2}^{2}(N_{\rm S1}+P)+N_{\rm S1}^{2}(N_{\rm S2}+P)}\right] (7)

where NN is the total (sky-subtracted) counts within the aperture, while the sub-indices B, S1 and S2 correspond to NN of the blazar, comparison star and control star, respectively. The factor PP contains the common noise-terms, as P=npix(Nsky+NRON2)P=n_{\rm pix}(N_{\rm sky}+N^{2}_{\rm RON}), where npixn_{\rm pix} is the number of pixels within the aperture, NskyN_{\rm sky} is the sky level and NRONN_{\rm RON} is the read-out noise. We used the median values of NN of the objects and sky for calculating Γ\Gamma. Thus, the scaled CC parameter (CΓC_{\Gamma}) is defined as

CΓ=CΓ=1Γ(σBS1σS2S1).C_{\Gamma}=\frac{C}{\Gamma}=\frac{1}{\Gamma}\left(\frac{\sigma_{\rm B-S1}}{\sigma_{\rm S2-S1}}\right). (8)

The source is considered variable if CΓ2.576C_{\Gamma}\geq 2.576. Even though the C parameter is not a proper statistic, it remains a useful indicator of stability (de Diego, 2014; de Diego et al., 2015; Zibecchi et al., 2017, 2020).

3.2.2 Scaled F-test

The standard F-statistics parameter is F=σBS12/σS2S12F=\sigma_{\rm B-S1}^{2}/\sigma_{\rm S2-S1}^{2}, where σBS12\sigma_{\rm B-S1}^{2} and σS2S12\sigma_{\rm S2-S1}^{2} are the variances in blazar DLC and a control star DLC respectively. The scaled F-statistics FΓF_{\Gamma} is given as

FΓ=FΓ2=1Γ2(σBS12σS2S12).F_{\Gamma}=\frac{F}{\Gamma^{2}}=\frac{1}{\Gamma^{2}}\left(\frac{\sigma_{\rm B-S1}^{2}}{\sigma_{\rm S2-S1}^{2}}\right).

The F-statistic assumes that the uncertainties in the observations are normally distributed. If n(BS1)n_{\rm(B-S1)} and n(S2S1)n_{\rm(S2-S1)} are the sizes of the blazar and control star DLC respectively, the number of degrees of freedom in the numerator and denominator of the F-statistic are ν1=n(BS1)1\nu_{1}=n_{\rm(B-S1)}-1 and ν2=n(S2S1)1\nu_{2}=n_{\rm(S2-S1)}-1, respectively. We calculated FΓF_{\Gamma} and considered the blazar to be variable with 99.5 per cent confidence if FΓF_{\Gamma} was greater than the critical value Fcα(ν1,ν2)F_{c}^{\alpha}(\nu_{1},\nu_{2}) at α=0.005\alpha=0.005 (Zibecchi et al., 2017, 2020).

3.2.3 Power-enhanced F-test

The power-enhanced F -test (PEF) has been used in various recent blazar IDV studies (Pandey et al., 2019, 2020, and references therein). The power-enhanced F-statistic has the advantage of comparing the blazar variance to the combined variance of multiple field stars and is given as (de Diego, 2014)

Fenh=sblz2sc2,F_{\rm enh}=\frac{s_{\rm blz}^{2}}{s_{c}^{2}}, (9)

where sblz2s_{\rm blz}^{2} is the variance of the DLC of the blazar with respect to a reference star, and sc2s_{c}^{2} is the combined variance of the comparison stars’ DLCs with respect to the reference star. Thus, sc2s_{c}^{2} is given as

sc2=1(j=1knj)kj=1ki=1njsj,i2.s_{c}^{2}=\frac{1}{\left(\sum_{j=1}^{k}n_{j}\right)-k}\sum_{j=1}^{k}\sum_{i=1}^{n_{j}}s_{j,i}^{2}. (10)

Here, kk is the total number of available comparison stars in the DLC, njn_{j} is the number of observations of the jthj^{\rm th} comparison star, and sj,i2s_{j,i}^{2} is the scaled square deviation of the ithi^{\rm th} observation of the jthj^{\rm th} comparison star given as

sj,i2=Γj(mj,imj¯)2.s_{j,i}^{2}=\Gamma_{j}(m_{j,i}-\bar{m_{j}})^{2}. (11)

Here Γj\Gamma_{j} is the scale factor of the jthj^{\rm th} comparison star DLC computed following Equation 7.

Using the data of the field stars, we first checked the star–star DLCs to identify any spikes due to instrumental errors or improper removal of cosmic rays, and removed them iteratively if they were more than 3 standard deviations from the mean magnitude. We considered a “well-behaved” star with low fluctuations and an average magnitude close to the blazar as the reference star. The number of degrees of freedom in the numerator and denominator of the F-statistics are ν1=nblz1\nu_{1}=n_{\rm blz}-1 and ν2=(j=1knj)k\nu_{2}=\left(\sum_{j=1}^{k}n_{j}\right)-k, respectively. We calculated FenhF_{\rm enh}, and considered the blazar to be variable (V) with 99.5 percent confidence if FenhF_{\rm enh} was greater than the critical value Fc(ν1,ν2)F_{c}(\nu_{1},\nu_{2}) at α=0.005\alpha=0.005.

3.2.4 Nested ANOVA test

In the nested analysis of variance (ANOVA) test, DLCs of the blazar are generated with respect to all the comparison stars used as reference stars. The details of this method are given in de Diego et al. (2015). The nested ANOVA test needs a large number of points in the light curves, strongly limiting its application to densely populated DLCs. We divided the DLCs with at least 20 observations into groups such that each group contains 4 observations. Equation (4) of de Diego et al. (2015) considers an ideal set of lightcurves where the total number of observations are divisible by the group size. In most of the DLCs in this work, the total number of observations was not an integral multiple of the group size of 4. So, in those cases, the last group contained less than 4 observations, and we calculated the degrees of freedom accordingly to compute the mean square due to groups (MSG) and mean square due to the nested observations in groups (MSO(G)). The ANOVA F-statistic is given as, F=MSG/MSO(G)F=\text{MS}_{G}/\text{MS}_{O(G)}. For a significance level of α=0.005\alpha=0.005, if the F -statistic is greater than the critical value (FcF_{c}), the blazar is taken as variable (V), otherwise as non-variable (NV) with 99.5 per cent confidence.

We have listed the results of the scaled C-criterion and scaled F-test in Table 6 and those of power enhanced F-test and the nested ANOVA test in Table 7. In the case of scaled C-criterion and F-test, we fixed one particular star as the comparison star for each dataset. The source is declared variable with respect to one comparison-control star pair if both scaled C-statistics and F-statistics cross their respective critical values. We declare the final variability status of the blazar as variable/non-variable (V/NV) if it is variable/non-variable against all control stars. If the blazar is variable against some of the control stars, we call it probably variable (PV). We did not carry out the nested ANOVA test in a few datasets containing less than 20 observations. In the case of the power-enhanced F-test in absence of the corresponding nested ANOVA test, we call the blazar probably variable (PV) even if the F-statistic crosses the critical value, as the F-test is more prone to give a false positive result (Zibecchi et al., 2017, 2020). If nested ANOVA is present and both the tests cross the critical values, we call the blazar variable (V). Otherwise, we declare the source non-variable (NV). We list the summary of the IDV tests in Table 8. We give a final verdict on the variability status of the source after comparing the results of the combination of the C-test and F-test (C&F) from Table 6 and results of the combination of the power-enhance F-test and nested ANOVA test (P&N) from Table 7. If the results from both combinations were the same, we kept that result. If C&F declared “V” and P&N declared “PV” due to the absence of nested ANOVA, we finally consider the source variable (V). We considered variability on 2005 November 8 as “NV” because both C-test and nested ANOVA resulted in non-variability. Despite being variable in nested ANOVA, we consider the 2005 December 5 lightcurve “NV” as the F-test and PEF-test detected no variability. A few examples of DLCs of AO 0235+164 having different variability characteristics (V/PV/NV) are shown in Figure 8.

3.2.5 Doubling timescale

A flux doubling/halving timescale gives an estimate of the variability timescale (τvar\tau_{\mathrm{var}}) of a source. We calculate the flux doubling/halving timescale (τd\tau_{d}) between two consecutive observations and its corresponding significance (σ\sigma) as

(ti+1)=(ti)2(ti+1ti)/τdσ=|(ti+1)(ti)|/εi,\begin{split}\mathcal{F}(t_{i+1})=\mathcal{F}(t_{i})*2^{(t_{i+1}-t_{i})/\tau_{d}}\\ \sigma=|\mathcal{F}(t_{i+1})-\mathcal{F}(t_{i})|/\varepsilon_{i},\end{split} (12)

where (ti)\mathcal{F}(t_{i}) and εi\varepsilon_{i} are the flux observed at time tit_{i} and the corresponding measurement uncertainty, respectively. We consider the fastest doubling timescale (τdmin\tau_{d}^{\mathrm{min}}) with a higher significance than 3σ3\sigma as an estimate for τvar\tau_{\mathrm{var}}. We obtained τdmin<1\tau_{d}^{\mathrm{min}}<1 day for all the nights when the source showed significant IDV both in scaled F-test and nested ANOVA test. This further strengthens our claims for the frequent presence of IDV. Following Equation 2 we computed the variability amplitudes on the same nights. All these results are listed in Table 7.

Table 8: Summary of statistical tests for IDV on AO 0235+164 differential lightcurves from CASLEO and CAHA
Obs. Band Combined variability status Final
date (CC & FF-test)a (PEF & status
N-ANOVA)b
1999 Nov 2 VV V V V
1999 Nov 3 VV V V V
1999 Nov 4 VV V V V
RR V V V
1999 Nov 5 VV V NV PV
RR NV V PV
1999 Nov 6 VV V V V
RR V V V
1999 Nov 7 VV PV PV PV
RR PV PV PV
2000 Dec 21 VV PV PV PV
RR PV PV PV
2000 Dec 23 VV PV PV PV
RR V PV V
2001 Nov 9 VV NV NV NV
RR V PV V
2001 Nov 10 VV NV NV NV
RR NV NV NV
2001 Nov 11 VV NV NV NV
RR NV NV NV
2001 Nov 12 VV NV NV NV
RR PV PV PV
2001 Nov 13 VV NV NV NV
RR NV NV NV
2005 Jan 16 RR V PV V
2005 Nov 2 RR V V V
2005 Nov 4 RR V V V
2005 Nov 5 RR V V V
2005 Nov 6 RR V V V
2005 Nov 8 RR NV PV NV
2005 Dec 5 RR NV PV NV
2005 Dec 6 RR PV PV PV
2019 Dec 17 RR NV NV NV

Note. — aTable 6, bTable 7, PEF=power-enhanced F-test.

3.2.6 Duty cycle

We calculated the duty cycle (DC) of AO 0235+164 using the definition of Romero et al. (1999), that was used later by multiple authors (e.g., Stalin et al., 2009; Agarwal et al., 2016). The formula for DC for a particular waveband is given as,

DC=100i=1nNi(1/Δti)i=1n(1/Δti)%\text{DC}=100\frac{\sum_{i=1}^{n}N_{i}(1/\Delta t_{i})}{\sum_{i=1}^{n}(1/\Delta t_{i})}\% (13)

where Δti=Δti,obs/(1+z)\Delta t_{i}=\Delta t_{i,obs}/(1+z) (duration of the monitoring session on ithi^{\text{th}} night is Δti,obs\Delta t_{i,obs}). Thus, this formula calculates the duty cycle weighted by the cosmological redshift corrected monitoring duration of each night. We set Ni=1, 0.5, and 0N_{i}=\text{1, 0.5, and 0} for the nights with variability status “V”, “PV”, and “NV” respectively. We obtained the duty cycle of AO 0235+164 to be \sim44 percent in VV-band, and \sim45 percent in RR-band considering the nights where the source was observed for at least 2 hours.

3.3 The mass of the central black hole

We estimate the mass of the SMBH in AO 0235+164 by using its spectrum observed using the CCD Imaging/Spectropolarimeter (SPOL) at the Steward Observatory444http://james.as.arizona.edu/~psmith/Fermi on 2011 January 8 (air mass = 1.12). This spectrum was selected since the blazar was then at its lowest level during the period 2008–2018, and should ensure the best visibility of the emission lines because of the lower continuum contribution from the jet. The observed wavelength range of the spectrum we used is 4000–7550 Å, with a spectral resolution of 4 Å, and it is analyzed by following the procedure given in Liao & Gu (2020). Firstly, it was corrected for Galactic extinction with the reddening map of Schlegel et al. (1998), and then was shifted to the rest-frame wavelength by using the redshift of 0.94.

This spectral coverage meant we could use the Mg II line, which is prominent on the spectrum shown in Figure 9 (focused on the 2400-3100 Å range), to estimate the SMBH mass. We modeled the continuum by applying a single power law (fλλαf_{\lambda}\propto\lambda^{\alpha}) (as Fe II emission is rather weak). A Gaussian profile was then used to fit the Mg II line, centered at the position of 2800 Å, on the continuum-subtracted spectrum. The broad component of Mg II was fitted with a Gaussian with a 1000 kms1\rm km\,s^{-1} lower limit, while a Gaussian with upper limit of 1000 kms1\rm km\,s^{-1} was applied for the narrow component. In order to estimate the corresponding errors of full width at half maximum (FWHM) and flux, we generated 100 mock spectra by adding random Gaussian noise to the original spectrum using the flux density errors, and then took the standard deviation of measurements from those mock spectra as the uncertainties. Here, the flux density errors were the RMS value of the spectrum calculated over the spectral window of (3000-3100) Å, after subtracting a second-order polynomial function. Figure 9 shows the resulting fit to the spectrum. Our best fitting results indicate that the line width of the broad Mg II component is FWHM = 3151 kms1\rm km\,s^{-1}, with log-scale luminosity in ergs1\rm erg~{}s^{-1}, log(LMgII)\log(L_{\rm MgII}) = 42.8.

The line width and the Mg II line luminosity we find are consistent with the range of values FWHM=3100–3500 kms1\rm km\,s^{-1} and log(LMgII)\log(L_{\rm MgII})=42.5–42.8, respectively, which were derived by Raiteri et al. (2007) from one VLT and four TNG spectra of AO 0235+164 acquired in 2003–2004. We use the FWHM and luminosity of the broad Mg II line, not the continuum luminosity, as we are unable to exclude the jet emission contribution, despite the low state spectrum that we could use for this blazar. The black hole mass is derived from the empirical relation used for Mg II (Kong et al., 2006), which is based on measured broad line region sizes in the reverberation-mapping AGN sample of Peterson et al. (2004), as

MBHM=2.9×106(LMgII1042ergs1)0.57±0.12(FWHMMgII103kms1)2\frac{M_{\rm BH}}{\rm M_{\odot}}=2.9\times 10^{6}\left(\frac{L_{\rm MgII}}{10^{42}\ \rm erg\ s^{-1}}\right)^{0.57\pm 0.12}\left(\frac{\rm FWHM_{\rm MgII}}{10^{3}\ \rm km\ s^{-1}}\right)^{2} (14)

Thus, the SMBH mass is log(MBH/M)=7.90±0.25\log(M_{\rm BH}/M_{\odot})=7.90\pm 0.25, where the uncertainty is estimated from the measurement uncertainties of the FWHM and luminosity of Mg II. Using optical spectroscopy data from the SDSS archive, Paliya et al. (2021) reported a somewhat higher mass, log(MBH/M)\log(M_{\rm BH}/M_{\odot}) = 8.58 ±\pm 0.34, and an accretion disk luminosity (in erg s-1), of log(Ldisk)\log(L_{\rm disk}) = 45.30 ±\pm 0.22. Using the method mentioned in Paliya et al. (2021) with log(LMgII)\log(L_{\rm MgII}) = 42.8, we obtained a lower disk luminosity (in erg s-1) of log(Ldisk)\log(L_{\rm disk}) = 45.01 ±\pm 0.20 from the spectrum observed on 2011 January 8.

4 Discussion

Refer to caption
Figure 10: Comparison of the SED of the lowest flux state observed on JD 2452169 and the thermal emission from the accretion disk in the observer’s frame. The thermal emission component is calculated using a multi-temperature disk model with the black hole mass log(MBH/M)\log(M_{\rm BH}/M_{\odot}) = 7.9±\pm0.25, and the log-scale disk luminosity in erg s-1, log(Ldisk)\log(L_{\rm disk}) = 45.01±\pm0.20. The shaded region indicates the uncertainties in the calculation of the disk thermal component.

In this work, we have presented a detailed temporal and spectral study of the highly variable emission from the blazar AO 0235+164 observed at multiple optical wavebands (UBVRIU\!BV\!RI) from October 1975 to December 2019. The lightcurves have highly uneven data sampling due to gaps in observation seasons and non-uniform observation campaigns. Although UU-band data are quite sparsely sampled the BVRIBV\!RI observations have denser sampling when the source was highly active. Multiple long-term studies suggested that AO 0235+164 shows \sim2-year long flaring episodes with multiple sub-flares after intervals of \sim8 years (Raiteri et al., 2006; Fan et al., 2017; Roy et al., 2022). Figure 1 shows a difference of about six magnitudes between the quiescent and outburst states in all optical wavebands, corresponding to an energy flux variation of more than two orders of magnitude (Figure 6). The long-term variability amplitudes at all five wavebands are quite similar (Table 1). Also, we found a strong correlation with zero time-lag between the UBVIU\!BV\!I observations and the RR-band data (Figure 2 and Figure 3), which implies a common radiative process at a single emission zone is responsible for the bulk of the emission at the optical wavebands.

Sometimes during the quiescent states of powerful blazars, the disk thermal emission component becomes visible as a big blue bump on top of the synchrotron emission component from the jet in the optical-UV wavebands (e.g., Roy et al., 2021). As the disk emission is bluer than the jet synchrotron emission, an increase in the jet activity during low flux states displays a redder-when-brighter trend. The enhanced jet activity is observed when the charged particles inside the jet get accelerated to higher energies, and then radiate faster. Thus, the jet synchrotron component tends to get bluer with the increase in flux. If the jet emission completely outshines the disk emission, we expect to see a bluer-when-brighter trend (e.g., Isler et al., 2017). The flux increment can also be attributed to the increase in the jet Doppler factor (e.g., Papadakis et al., 2007), which blueshifts the spectrum and produces a bluer-when-brighter trend because of the convexity of the spectrum. Such a trend is seen in the (BI)(B-I) vs RR magnitude diagram (Figure 4b) and indicates the domination of non-thermal jet emission over the thermal emission component of the accretion disk during both flaring and quiescent states. From the convex shapes of the optical BVRBV\!R SEDs during states ranging from quiescent to flaring (see the accompanying SED video and Figure 6), we may infer that the effect of the disk thermal emission is not significant in optical wavebands even during the low flux states.

This can be explained in terms of the nature of disk thermal emission given the disk luminosity and the central black hole mass computed in subsection 3.3. The primary, and most precise, black hole mass estimation methods are based on stellar and gas kinematics and reverberation mapping (e.g. Vestergaard, 2004). These methods need high spatial resolution spectroscopy data from the host galaxy and/or higher ionization emission lines and are not applicable to most BL Lacertae objects (BL Lacs). But in BL Lacs, if the weak emission lines are present, we can use the empirical methods (Kong et al., 2006) for BH mass estimation. The most common methods used for BH mass estimation for BL Lacs are the shortest variability timescales and periods of QPOs (Gupta et al., 2012). Since BL Lacs are highly variable objects, any BH mass estimation may be treated as an upper limit, and there are possibilities of detection of a shorter variability timescale or shorter QPO period. We obtained a log-scale BH mass of 7.90±\pm0.25 in solar mass unit. The Steward observatory spectrum we used in our analysis had a narrower Mg II emission line (FWHM=3151 km s-1) than those of Raiteri et al. (2007) and Paliya et al. (2021), thus resulting in a lower mass estimate. We considered a multi-temperature blackbody type accretion disk model, where the temperature at any portion of the disk is a function of the disk luminosity and the central black hole mass, to compute the thermal emission component. In Figure 10 we plotted the thermal component along with the optical-UV SED during the lowest activity state of AO 0235+164 observed on JD 2452169. It is evident that, as the thermal emission peaks at far UV frequencies (\sim3.5×1015\times 10^{15} Hz) in the observer’s frame of reference, the jet emission always dominates in BVRIBV\!RI wavebands. We do not see any significant trend in the variation of the (VR)(V-R) spectral index (αVR\alpha_{VR}) (Figure 7). The sudden rise of the UU-band flux in Figure 10 is an indicator of a probable UV-soft X-ray bump as discussed in Raiteri et al. (2005, 2006). According to these studies, the source of the bump is either an additional synchrotron component coming from a separate emission region in the jet or the emission of a continuous inhomogeneous jet is suppressed in near UV region due to a discontinuity in opacity or misalignment of that particular emission region. Ackermann et al. (2012) mentioned that the whole optical-UV spectrum is produced by a single synchrotron emitting zone as the shape of the bump does not change with luminosity. They attributed the UV spectral hardening to an artifact due to the overestimation of extinction by Junkkarinen et al. (2004).
For the detection of any statistically significant intraday variability in 33 lightcurves of AO 0235+164 observed at CASLEO/CAHA, we employed different statistical tests widely used in AGN variability studies. The reliability of each of these tests has been disputed (e.g. de Diego et al., 2015; Zibecchi et al., 2017), so we here employed a comparative approach that could allow us to circumvent the limitations affecting any individual test. In the first place, we used the scaled CC-criterion and the FF-test. The first compares the dispersion of the blazar lightcurve to the dispersion of a field star (control star), while the latter does so with the variances. According to Zibecchi et al. (2017) and Zibecchi et al. (2020), the F-test has a tendency to classify noisy non-variable curves as a variable (i.e., give false positives), while the CC-criterion tends to give false negatives. Even though the CC-criterion (Romero et al., 1999) cannot be considered as an actual statistical test, it may still be a useful parameter to detect variability with high significance. The FF-test, on the other hand, does not always work as expected, because it is particularly sensitive to non-Gaussian errors (“red noise”), which are usually an issue when analyzing blazars DLCs.

We also used the power-enhanced F-test and the nested ANOVA test, which involve multiple field stars. It is expected that the power-enhanced F-test may also suffer from the same drawback of detecting false variability as the (original) FF-test. In the nested ANOVA test, in turn, data grouping may lead to false results if data within a time span larger than the (unknown) variability timescale are grouped. Comparing the results of Table 6 and Table 7, while considering the tendencies of giving false results by the respective tests, we can confirm that the source was significantly variable in 4 out of 13 VV-band lightcurves, and 9 out of 20 RR-band lightcurves. The source seems to be probably variable in 3 VV-band and 4 RR-band lightcurves, and non-variable in the rest. On 1999 November 5, the combination of CC-criterion and FF-test indicates non-variability but the combination of power-enhanced FF-test and nested ANOVA detects variability in the RR-band lightcurve. The results in the VV-band lightcurve on that day are exactly the opposite. Similar situations were observed also on 2001 November 9 and 2001 November 12. A visual inspection of the DLCs of these nights reveals that the blazar DLCs were classified as non-variable when either the control star DLC had higher variability (1999 November 5) or the measurement errors of the blazar DLCs were higher due to its low-flux state (2001 November 9 and 12). Higher measurement errors lead to a lower chance of significant variability detection. These strange results may be an example of the drawbacks of the applied methods when trying to recover low-amplitude variations from DLCs affected by non-Gaussian noise (part of the observations on that night were taken at air mass >2>2 and under non-photometric conditions). Otherwise, the combined results of different methods seem to more or less agree. Alongside the optical SED patterns, such frequent IDV establishes AO 0235+164 as a low-energy peaked BL Lac (LBL) object. High energy peaked BL Lacs (HBL) show significantly less optical intraday variability than the LBLs (Heidt & Wagner, 1998; Romero et al., 1999).

The differences in IDV behavior have been attributed to the strength of magnetic fields present in the jet of HBLs. A higher axial magnetic field (BB) than a critical value (BcB_{c}) may prevent the generation of any bends and Kelvin-Helmhotz instabilities in the jet-base responsible for creating intraday microvariabilities. This indicates the presence of a weaker magnetic field than BcB_{c} in the jet of AO 0235+164. The critical magnetic field (BcB_{c}) is given in Romero (1995) as

Bc=4πnemec2(Γ21)/Γ,B_{c}=\sqrt{4\uppi n_{e}m_{e}c^{2}(\Gamma^{2}-1)}/\Gamma, (15)

where nen_{e} is the electron density in the emission region, mem_{e} is the electron rest mass, and here Γ\Gamma is the bulk Lorentz factor of the jet flow. Considering a typical set of parameters, ne=429n_{e}=429 cm-3 and Γ=20\Gamma=20 (Ackermann et al., 2012), we get Bc0.07B_{c}\simeq 0.07 G.

Table 9: Variation of duty cycle with the duration of observation in RR-band.
Observation No. of Duty
duration (hours) nights cycle (%)
>1>1 20 52
>2>2 19 45
>3>3 17 50
>4>4 14 57
>5>5 13 64
>6>6 8 77

From Table 7 and Figure 8, we can say that the variability amplitudes were higher in the 1999 season when the source was in a fainter state (higher magnitude) than its brighter state in the 2005 season. Marscher (2013) suggested that enhancement of flux can arise from a more uniform flow of particles inside the jet, which in turn decreases the amplitude of microvariability associated with the turbulence inside the jet. Table 9 indicates that the probability of detection of significant variability increases with the duration of observation. Similar results for other blazars were found by Gupta & Joshi (2005), Rani et al. (2010), and Agarwal et al. (2016).

From the flux doubling timescales listed in Table 7, we can estimate the upper limit to the size of the emission region (RmaxR_{\rm max}) using the light travel-time argument given as

Rmax=cδtvar1+zR_{\rm max}=\frac{c\delta t_{\rm var}}{1+z} (16)

where zz is the cosmological redshift of 0.94, tvart_{\rm var} is the variability timescale, and δ\delta is the Doppler boost of the jet. Considering δ=24\delta=24 (Hovatta et al., 2009) and tvart_{\rm var} to be the shortest flux doubling timescale of 0.083 days (when the source was significantly variable), we obtain an emission region size upper limit of 2.6×1015\sim 2.6\times 10^{15} cm. Assuming a conical jet model where the emission region fills up the entire jet cross-section, we can estimate the probable maximum distance (dmaxd_{\rm max}) of the emission region from the central black hole as, dmax=ΓRmax=5.2×1016d_{\rm max}=\Gamma R_{\rm max}=5.2\times 10^{16} cm. To explain the observed strong variability, Marchesini et al. (2016) attempted to apply a swinging jet model that attributes the observed variability to a change in the viewing angle of the emission region with time (i.e. variation in the associated bulk Doppler factor). They reported a high rate of change in viewing angle of about 7-10 arcmin per day, considering a mean viewing angle of 2.3, would be necessary. However, they found that this geometric wiggling-jet scenario was disfavored when considering the observed variation in color index with time. Several earlier studies on AO 0235+164 associated the observed fast optical variability with gravitational microlensing by the foreground absorber at z=0.524z=0.524. Webb et al. (2000) proposed that the 1997 flare resulted due to microlensing because of an observed correlation with zero lag between radio and optical lightcurves following Stickel et al. (1988), but the absence of any correlated flare in the X-ray lightcurve makes this explanation less likely. Abraham et al. (1993) and Raiteri et al. (2007) explained that such microlensing events can produce small amounts of fast flux amplification but are unlikely to dominate the high variability observed in AO 0235+164.

5 conclusions

In this work, we conducted a study of long-term and short-term (intraday) variability in the optical multiwaveband observations of the blazar AO 0235+164. Here we summarize our results and the probable physical scenarios.

  1. 1.

    We observed a variation of about six magnitudes between the quiescent and flaring episodes, or over two orders of magnitude variation in the SEDs.

  2. 2.

    UBVIU\!BV\!I lightcurves are highly correlated with the RR-band lightcurve with zero time lag.

  3. 3.

    A significant bluer-when-brighter trend is observed in the (BI)(B-I) color variation with RR-magnitude.

  4. 4.

    All the optical BVRBV\!R-band SEDs show convexity. These observations indicate that the optical emission is dominated by jet radiation.

  5. 5.

    AO  0235+164 frequently shows statistically significant intraday variability in optical wavebands. This implies that AO 0235+164 is an LBL and probably has a weak magnetic field in the jet environment.

  6. 6.

    From the analysis of a broad Mg II emission line in a spectrum of AO  0235+164 taken at a low state, we estimate a central black-hole mass of 7.9×107M\sim 7.9\times 10^{7}M_{\odot}.

Data from the Steward Observatory spectropolarimetric monitoring project were used. This program is supported by Fermi Guest Investigator grants NNX08AW56G, NNX09AU10G, NNX12AO93G, and NNX15AU81G. This paper has made use of up-to-date SMARTS optical/near-infrared light curves that are available at www.astro.yale.edu/smarts/glast/home.php. This work is partly based on data taken and assembled by the WEBT collaboration and stored in the WEBT archive at the Osservatorio Astrofisico di Torino - INAF (https://www.oato.inaf.it/blazars/webt/). These data are available upon request to the WEBT President Massimo Villata ([email protected]). This work is based on data acquired at Complejo Astronómico El Leoncito, operated under an agreement between the Consejo Nacional de Investigaciones Científicas y Técnicas de la República Argentina and the National Universities of La Plata, Córdoba and San Juan. We thank Anabella Araudo and Ileana Andruchow for help with the observations made with CASLEO and the data analysis.

We thankfully acknowledge the anonymous reviewer for very useful comments which helped us to improve the manuscript. We acknowledge the support of the Department of Atomic Energy, Government of India, under project identification number RTI 4002. ACG is partially supported by Chinese Academy of Sciences (CAS) President’s International Fellowship Initiative (PIFI) (grant no. 2016VMB073). GER acknowledges support from grants PIP 0554 (CONICET), PIP 2021-1639 (CONICET), and grant PID2019-105510GBC31 of the Spanish Ministerio de Ciencia, Innovación y Universidades and through the Center of Excellence Mara de Maeztu 2020-2023 award to the ICCUB (CEX2019-000918-M). JAC is María Zambrano researcher fellow funded by the European Union -NextGenerationEU- (UJAR02MZ), supported by PIP 0113 (CONICET) and PICT-2017-2865 (ANPCyT). JAC was also supported by grant PID2019-105510GB-C32/AEI/10.13039/501100011033 from the Agencia Estatal de Investigación of the Spanish Ministerio de Ciencia, Innovación y Universidades, and by Consejería de Economía, Innovación, Ciencia y Empleo of Junta de Andalucía as research group FQM-322, as well as FEDER funds.

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