Broadband study of OQ 334 during its flaring state
aa r X i v : . [ a s t r o - ph . H E ] F e b MNRAS , 1–15 (0000) Preprint 9 February 2021 Compiled using MNRAS L A TEX style file v3.0
Broadband study of OQ 334 during its flaring state
Raj Prince ⋆ , Rukaiya Khatoon , C. S. Stalin Center for Theoretical Physics, Polish Academy of Sciences, Al.Lotnikov 32/46, 02-668, Warsaw, Poland Tezpur University, Napaam-784028, Assam, India Indian Institute of Astrophysics, Block II, Koramangala, Bangalore - 560034, India
Accepted XXX. Received YYY; in original form ZZZ
ABSTRACT
The blazar OQ 334 displayed a γ -ray flare in 2018, after being in the long quiescent γ -ray state since 2008. Subsequent to the flare, the source was in a higher γ -ray fluxstate and again flared in 2020. We present here the first spectral and timing analysisof the source at its various flaring states. During the higher γ -ray state, we found fourmajor peaks identified as P1, P2, P3 and P4. From timing analysis we found rise anddecay time of the order of hours with the fastest variability time of 9.01 ± γ -ray photon of 77 GeV during P4, which suggests the location ofthe γ -ray emitting region at the outer edge of the broad line region or the inner edgeof the torus. The γ -ray spectral analysis of the source indicates that during P4, the γ -ray spectrum clearly deviates from the power law behaviour. From cross-correlationanalysis of the γ -ray and radio lightcurves, we found that the two emission regionsare separated by about 11 pc. Our broad band spectral energy distribution modelingof the source during quiescent and active phases indicates that more electron andproton power are required to change the source from low flux to high flux state. TheAnderson-Darling test and histogram fitting results suggest that the three days binned γ -ray fluxes follow a lognormal distribution. Key words: galaxies: active; gamma rays: galaxies; individuals: OQ 334
Blazars are a peculiar category of active galactic nuclei(AGN) that have their relativistic jets aligned close tothe line of sight (angles less than ∼ ◦ ) to the ob-server (Urry & Padovani 1995). Their energy output dom-inated by non-thermal emission spans the complete acces-sible electromagnetic spectrum. Blazars are highly lumi-nous, have powerful jets, powered by massive black holes(Lynden-Bell 1969) and dominate the extragalactic γ -raysky (Hartman et al. 1999, Abdo et al. 2010). They showlarge amplitude flux variability in different wavelengthssuch as radio, infrared, optical, X-rays and γ -rays on arange of time scales from minutes to hours to several days(Heidt & Wagner 1996, Ulrich et al. 1997 ) and in somecases to decades (Goyal et al. 2017, 2018; Goyal 2020); Fora review see, Hovatta & Lindfors 2019. The increased ca-pability in the recent years to acquire near simultaneousobservations over different wavelengths have led to the iden-tification of complex variability patterns across wavelengths ⋆ E-mail: [email protected] in blazars. There are instances when the variations in thelow energy optical and high energy γ -rays are correlated andalso instances where uncorrelated variations between opticaland γ -rays are noticed( Chatterjee et al. 2012, Rajput et al.2019, 2020). Also, the short time scale of variations now ob-served in blazars indicate that the variations we observe inthem arise from very small regions in their jets.The broad band spectral energy distribution (SED) ofblazars show two distinct humps. The low energy humppeaks in the UV/X-ray region and is now understood tobe due to synchrotron emission from relativistic electrons intheir jet. The high energy hump peaks in the MeV-GeV en-ergy range and the physical mechanisms responsible for thehigh energy hump is still debated. In the widely used leptonicmodel of emission from blazar jets, the high energy emissionis due to inverse Compton process. The seed photons for theinverse Compton process can be photons internal to the jet(synchrotron self Compton; Sikora et al. 2009) or external tothe jet (external compton; Dermer et al. 1992; Sikora et al.1994). Alternative to the leptonic process is the hadronicprocess. In this model, the high energy emission is explain-able by proton synchrotron process or photo-pion process © (B¨ottcher et al. 2013). Thus, carrying out timing and SEDanalysis of blazars can provide valuable clues to the pro-cesses happening close to the central regions of blazars. Inspite of such studies done on blazars, we yet do not have aclear understanding of the physical processes happening inblazar jets.The observed broad band SED of blazars is complex.For example, SED modelling of 3C 279 using the onezone leptonic emission model during the flares in 2017-2018, favours the γ − ray emission site to be located atthe outer boundary of BLR (Prince 2020). Also, the flareof 2014 March in 3C 279 was not detected in the Veryhigh energy gamma-ray band, and the Fermi observationswere explained in the one-zone leptonic emission modelwith the requirement of seed photons for inverse Comptonscattering from both the BLR and the torus (Paliya et al.2015). Alternatively, during the epoch of the hard gamm-ray flare from 3C 279 in December 2013, the SED was fitby both lepto-hadronic model and two-zone leptonic model(Paliya et al. 2016). This clearly indicates that even in thesame source, different radiative processes contribute at dif-ferent epochs, reflecting the complexities seen in emissionfrom blazar jets. In another FSRQ, Ton 599, the γ − ray emission site is found to be at the outer edge of the BLR(Prince 2019) based on leptonic model fit to the observedSED. Recently, the SED of the BL Lac Mrk 421 wasfound to be equally fit by all the models such as leptonic,hadronic and lepto-hadronic (Cerruti 2020). In the sourceCen A, there are reports that the obsevations are fit byprotron-synchrotron model (Banik et al. 2020). Also, the re-cent detection of neutrions from the blazar TXS 0506+056(IceCube Collaboration et al. 2018) seems to favour lepto-hadronic over leptonic models (Cerruti et al. 2019). It isbelieved that γ − ray flares without counterparts in theoptical band cannot be explained in the one-zone leptonicemission sceanrio and could favour hadronic models. Indeed,from analysis of a sample of FSRQs, Rajput et al. (2019,2020) have found that in a source there are epochs whenthere are optical flares without gamma-ray flares, gamma-ray flares without optical counterparts and correlated opti-cal and gamma-ray flares, all of which are explainable in theleptonic scenario. Thus, recent observations on a handfuldof blazars clearly indicate that the exact reasons for the ori-gin of high energy emission in blazar is complex, still notundestood and possibly future X-ray polarization observa-tions could provide the needed observational constrants onthe high energy emission process in blazars. Given the cur-rent scenario, it is of utmost importance to carry out timingand SED analysis of more and more number of blazars.The blazar OQ 334 is classified as a flat spectrum quasar(FSRQ) in 4FGL (Abdollahi et al. 2020) and it is at a red-shift z = 0.6819 (Hewett & Wild 2010). It has been in thelow γ -ray brightness state since 2008. After a decade it flaredin the γ -ray band in 2018 (Ciprini 2018). Subsequently it wasin a higher γ -ray brightness state and again flared in the γ -ray band in 2020 (Ciprini & Cheung 2020). Observationswere also available in the X-ray and optical/UV bands dur-ing the flaring epochs of the blazar from Swift . This sourcehas not yet been studied for its γ -ray characteristics, how-ever reports on its γ -ray flaring are available (Ciprini 2018,Angioni 2019, & Ciprini & Cheung 2020). Therefore, in thiswork we carried out detailed timing and spectral analysis of the source during both its flaring periods as well as a quies-cent period.This paper is organized as follows. In Section 2, we de-scribe the mulitwavelength data and reduction, the resultsare shown in Section 3, 4, & 5, followed by the summary inthe final Section. The FSRQ OQ 334 was found to show two episodes of flar-ing, one in 2018 and the other in 2020, based on observa-tions with the Large Area Telescope on board the
Fermi
Gamma-ray Space Telescope. To understand the nature ofthe source during its high activity states relative to its qui-escent state necessitates creation of broadband SED, whichin turn requires data at other wavelengths also. Towardsthis we looked into the γ -ray data from Fermi-LAT , X-rayfrom
Swift-XRT and Ultraviolet and optical data from
Swift-UVOT . Fermi -LAT
Fermi-LAT observes the galactic as well as the extragalac-tic sky in γ -ray. It is based on the pair conversion method,and the working energy range is 20 MeV – 500 GeV. It has avast field of view (FoV) of about 2.4 sr (Atwood et al. 2009),which scans 20% of the sky at any time. The total scanningperiod of the entire sky is around three hours. Fermi -LATis continuously monitoring the source OQ 334/B2 1420+32since 2008. LAT data in the energy range 100 MeV to 300GeV were taken for the period December 2008 to February2020. We followed the standard procedure for the reductionof γ -ray data as given in Science Tools . More details aboutthe reduction can be found in Prince et al. (2018). We gen-erated 3 day bin light curve covering a duration of 900 daysfrom MJD 58000 to MJD 58900. This is shown in Figure 1.Each points in the light curve pertains to a test statistics(TS) greater than 9, which corresponds to a 3 σ detection. -The X-ray data used in this work is from Swift/XRT in the energy range of 0.3 - 10 keV. X-ray data from
Swift was not available for most of the duration of the γ -ray lightcurve, however sparsely available during the two flaringperiods of OQ 334. The log of the X-ray observations usedin this work is given in Table 1. For all the observationsgiven in Table 1, we used the task xrtpipeline to generateclean event files. We used the CALDB version 20160609for Swift-XRT analysis. The clean event files were furtherprocessed using xselect in XSPEC (Arnaud 1996). Theevents from the source were selected from a circular regionof radius 12 arcsec. The background was selected from aregion of similar size but away from the source. We modelledthe generated spectra using the simple power law modelF(E) ∝ E Γ . For spectral fit we binned the data to have a https://fermi.gsfc.nasa.gov/ssc/data/analysis/documentation/MNRAS , 1–15 (0000) Time(MJD) F ( − ) ( p h c m − s − ) Q1 F1 F2
Fermi-LAT
Figure 1.
The long-term γ -ray light curve of OQ 334. State Q1 represents the quiescent state, and F1 and F2 are the two flaring states. minimum of 20 counts/bin. For the power law fit, we used afixed galactic absorption column density n H = 1.10 × cm − taken from Kalberla et al. (2005). We generated thespectrum file for each observations and if in a particularflaring period we have more than one observations, wecombined the spectra in addspec . In addspec, we addedthe source spectra along with the redistribution matrixfiles (RMF) and the ancillary response files (ARF) fordifferent observations. Similarly, the background spectrafrom different observations were added in mathpha . Thecombined X-ray spectrum from each period (quiescent-sate,flare-1, and P4) was finally used in the multi-wavelengthSED modeling. UV and Optical -In the optical and ultra-violet (UV) bands, we used datafrom the
Swift-UVOT (Roming et al. 2005) in the filters U,B, V, W1 and W2. For each of these filtes, we added theobservations accumulated over a given period using the taskUVOTIMSUM and we derived the magnitude of the tar-get blazar using UVOTSOURCE. We corrected the magni-tudes for galactic extinction following Schlafly & Finkbeiner(2011) and converted the extinction corrected magnitudes tofluxes using the zero points (Breeveld et al. 2011) and con-version factors given in Larionov et al. (2016).
In the radio band we used the data at 15 GHz from theOwens Valley Radio Observatory (OVRO; Richards et al.2011). The blazar OQ 334 is part of the OVRO monitoringprogram and therefore data at 15 GHz with a time resolutionof about 2 weeks is available for most of the duration of the γ -ray light curves. A detailed temporal and spectral study has been done usingthe multi-wavelength data from the
Fermi -LAT, and the https://heasarc.gsfc.nasa.gov/ftools/caldb/help/addspec.txt https://heasarc.gsfc.nasa.gov/ftools/caldb/help/mathpha.txt Table 1.
Log of the
Swift observations during all the states (Q1,F1, and P4).Observatory Obs-ID Exposure (ks)Q1Swift-XRT/UVOT 00010520001 1.0Swift-XRT/UVOT 00010520002 2.9F1Swift-XRT/UVOT 00010520004 1.8Swift-XRT/UVOT 00010520005 2.1Swift-XRT/UVOT 00010520006 1.9P4Swift-XRT/UVOT 00010520014 2.5Swift-XRT/UVOT 00010520015 1.6Swift-XRT/UVOT 00010520016 1.9Swift-XRT/UVOT 00010520017 2.0Swift-XRT/UVOT 00010520018 1.6Swift-XRT/UVOT 00010520019 1.5Swift-XRT/UVOT 00010520022 1.8Swift-XRT/UVOT 00010520024 1.8Swift-XRT/UVOT 00010520025 1.8Swift-XRT/UVOT 00010520026 2.0Swift-XRT/UVOT 00010520027 1.2Swift-XRT/UVOT 00010520028 1.7Swift-XRT/UVOT 00010520029 2.0Swift-XRT/UVOT 00010520030 2.4
Swift-XRT/UVOT telescope. The archival data from OVROis used to perform the correlation study with the γ -ray toexamine the possible location of their emission regions. γ -ray flux variability We show in Figure 1 the three day binned γ -ray light curveof OQ 334. As can be seen, the source was in a low γ -raybrightness state till 2018, when it first showed a γ -ray flarewith a three day binned γ -ray flux of 4.79 ± × − phcm − s − ) and an average γ -ray photon index of 1.96 ± MNRAS , 1–15 (0000) F ( p h c m − s − ) Pre-flare Flare ×10 −7 Time(MJD) Γ index F ( p h c m − s − )( × − ) P1 P2 P3 P4 Γ Figure 2.
The upper plot shows the γ -ray light curve of state F2 with the spectral index, divided into pre-flare and flare. The lower plotis the one-day binned γ -ray light curve of the flaring period. The color patches describe the various peaks observed in the flaring part. Figure 1 as F1 (Flare-1). The source remained in the lowbrightness state for a few days after the flare and then itshowed a steady increase in the γ -ray brightness level. Su-perimposed on the high γ -ray brightness level, we noticedseveral flares that also includes a major flare in early 2020.From the three day binned light curve, we identified threemajor regions, a quiescent state Q1, a flaring state F1 anda higher brightness state F2. The higher brightness statewas further divided into pre-flare (MJD 58500 - 58631) andflaring period (MJD 58631 - 58887), based on the average flux seen in the 3 day binned γ -ray light curve (Figure 2).For pre-flare and flare periods we found average flux val-ues of 6.09 ± × − and 1.72 ± × − ph cm − s − ) respectively. To substantiate this division based on themean flux values, we also calculated the fractional flux vari-ability (F V ar ; Prince et al. 2018). We found F
V ar values of0.61 ± ± γ -ray light curve. MNRAS , 1–15 (0000)
Table 2.
Details about the various states recongnized in this study.States MJD start MJD end Duration (days)Q1 58133 58177 44F1 58445 58470 25F2/pre-flare 58500 58631 131F2/ flare 58631 58887 256P1 58654 58661 7P2 58669 58683 14P3 58751 58766 15P4 58846 58887 41
From this 1 day binned light curve, we identified four highflux states (with flux exceeding ∼ × − ph cm − s − )denoted as P1, P2, P3 and P4. The colour patches in Figure2 denote the total duration of each of these flux states. Thedetails about all the states and their periods are mentionedin Table 2. Further, we also generated 12 hour binned γ -raylight curve to model the variations in the high flux stateswith a sum of exponential functions. This function is used toestimate the rising and decaying time of the peaks observedwithin the high state periods. The functional form of thesum of exponentials is given below, F ( t ) = 2 F (cid:20) exp (cid:18) t − tT r (cid:19) + exp (cid:18) t − t T d (cid:19)(cid:21) − (1)where T r and T d are the rise and decay times of the peaks,respectively, and the peak amplitude is approximated as F measured at time t . The fits to the lightcurve for all thehigh states (P1, P2, P3, and P4) are shown in Figure 3, andthe corresponding fitted parameters are given in Table 3.Various peaks observed in the high states P1, P2, P3, andP4 in Figure 3 from left to right are denoted in the serialnumber (1,2,3,..etc.) in Table 3. The temporal fitting for thehigh states P1, P2, and P3 was done for the 12 hr binnedlight curve. However, in the case of high state P4, the 12 hrbinned light curve has large error bars, and hence we con-sidered the one-day binned light curve for temporal study.For the first three cases, we also considered the constant fluxstate (shown in grey) while doing the temporal fitting, whichshows the flux level before and after the peaks. A harder when brighter trend is generally seen in the highenergy γ -ray emission from blazars (Ton 599; Prince 2019,3C 279; Prince 2020). To investigate spectral variations ifany, we show in Figure 4, the variation of Γ with the observed γ -ray brightness during the states P1, P2, P3 and P4. We didnot find any significant spectral variations with brightness. In the FSRQ category of AGN, detection of γ -ray photonswith energy >
20 GeV ( Liu & Bai 2006) suggests the lo-cation of the γ -ray emission region outside the broad lineregion (BLR). In such instances, the high energy photons,in the leptonic scenario are the result of inverse Comptonscattering of photons from the dusty torus by the relativisticelectrons in the jet. We estimated the number of high energyphotons during the higher brightness state of the source and Table 3.
The rise and decay times estimated from Equation (1) forall the peaks. The peak flux F is in units of ( × − ) ph cm − s − .Peaks t F T r T d (MJD) (hr) (hr)P11 58655.25 3.12 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± this is shown in Figure 5. We found two instances when γ -rayphotons of energy >
70 GeV with the probability of beingfrom the source is greater than 99%. By comparing Figure5 with the light curve shown in Figure 2, we conclude thatthe photon with energy ∼
72 GeV was detected just beforethe high state P1, while the photon of energy ∼
77 GeV wasdetected during P4. Detection of such high energy photonspoint to the location of the γ -ray emission site just at theboundary of BLR or the inner edge of the torus during P4. γ -ray Spectra We generated the γ -ray spectrum for all the states identi-fied in Figure 1 and Figure 2. The spectra were created us-ing likeSED.py a python code provided by Fermi
ScienceTools. We carried out likelihood analysis on the spectraldata points using power law (PL), log parabola (LP), bro-ken power law (BPL) and power law with exponential cut-off(PELC) models. The γ -ray spectra along with the model fitsare shown in Figure 6 and the corresponding best fit modelparameters are given in Table 4. We calculated TS curve =2(log L(LP/BPL/PLEC) - log L(PL)), where L representsthe likelihood function (Nolan et al. 2012), to arrive at thesuitability of the LP, BPL, and PLEC models over the PLmodel to describe the data. The best spectral model favoursa value larger than TS curve = 16 which is significant at the4 sigma level (Mattox et al. 1996).The TS curve reveals presence of curvature or break inthe spectrum, and which could be caused by the absorp-tion of high energy photons ( >
20 GeV; Liu & Bai 2006) bythe BLR assuming the emitting region is located within theBLR. However, if the emitting region is located outside theBLR a nice power law spectral behavior is expected.
MNRAS , 1–15 (0000)
Time(MJD-58654) F l u x ( − p h c m − s − ) Light curve for OQ 334 (P1) const_state12 hr
Time(MJD-58669) F l u x ( − p h c m − s − ) Light curve for OQ 334 (P2) const_state12 hr
Time(MJD-58751) F l u x ( − p h c m − s − ) Light curve for OQ 334 (P3) const_state12 hr
Time(MJD-58845) F l u x ( − p h c m − s − ) Light curve for OQ 334 (P4)
Figure 3.
Light curve fitting of different flares observed in OQ 334. i n d e x P1 P2 i n d e x P3 P4 Figure 4.
The γ -ray photon spectral index of all high states from 12hr bin light curve, with respect to the observed flux. The brighter-when-harder behavior is not much clear in this case unlike mostof the FSRQ type blazar . The various models and their corresponding parametersare shown in Table 4. The states, Q1, F1, pre-flare, P1, P2,P3 and P4 prefer models deviant from PL behaviour. Thestates where the PLEC best fits the spectra appears to havesimilar cut-off energy between 20-30 GeV, which is also thecut-off put by γ - γ absorption (Liu & Bai 2006) within theBLR, and hence it is likely that the γ -ray emission regionassociated with these states is present at the outer bound-ary of the BLR. However, a recent study of 106 FSRQs byCostamante et al. (2018) suggests that the smooth cut-offseen in the γ -ray spectrum between a few GeV to a few tensGeV is most probably the result of the end of the acceleratedparticle distribution.The PL photon spectral index suggests a significantspectral hardening as the source transits from pre-flare toflare state (P1, P2, P3, & P4) and it is also true whenthe source changes from state Q1 to F1. The cut-off energy(E cutoff ) in the PLEC model is different for different flar-ing states and this is compatible with the detection of highenergy photons in various states (Figure 5). So, E cutoff forP1, P2, P3, & P4 are ∼
10 GeV, ∼
20 GeV, ∼
24 GeV, & ∼ break ) is almost constant (at ∼ MNRAS , 1–15 (0000)
Time (MJD) H i g h e n e r g y P h o t o n ( G e V ) P1 P2 P3 P4
Photons with probability > 99% and energy > 10 GeV
Figure 5.
The arrival of high energy photon with probability >
99% for being from the source are shown here. We have considered photonsof energy greater than 10 GeV. -1 Energy (GeV) -12 -11 -10 E d N / d E ( e r g c m − s − ) OQ 334 (Q1)
PowerLawLogParabolaPLECBroken PowerLaw -1 Energy (GeV) -11 -10 E d N / d E ( e r g c m − s − ) OQ 334 (Flare-1)
PowerLawLogParabolaPLECBroken PowerLaw -1 Energy (GeV) -11 -10 E d N / d E ( e r g c m − s − ) OQ 334 (Pre-flare)
PowerLawLogParabolaPLECBroken PowerLaw -1 Energy (GeV) -11 -10 -9 E d N / d E ( e r g c m − s − ) OQ 334 (P1)
PowerLawLogParabolaPLECBroken PowerLaw -1 Energy (GeV) -11 -10 E d N / d E ( e r g c m − s − ) OQ 334 (P2)
PowerLawLogParabolaPLECBroken PowerLaw -1 Energy (GeV) -11 -10 E d N / d E ( e r g c m − s − ) OQ 334 (P3)
PowerLawLogParabolaPLECBroken PowerLaw -1 Energy (GeV) -11 -10 -9 E d N / d E ( e r g c m − s − ) OQ 334 (P4)
PowerLawLogParabolaPLECBroken PowerLaw
Figure 6. γ -ray spectral energy distributions (SED) for all the four flaring states and one long pre-flare state along with state Q1 and F1.Four different spectral models are used to fit the spectrum.MNRAS , 1–15 (0000) the break in BPL during state Q1 and F1 is different fromall the other states which probably suggests the involvementof different emission regions. To investigate correlation, if any, between flux variationsin the γ -ray band and other wavelengths, we looked intothe archives for the availability of data at multiple wave-lengths. We could find data only in the radio band at 15GHz that overlaps with the γ -ray light curve. The site of γ -ray production in blazars is controversial. Some study sug-gests that γ -rays are generally produced at the sub-parsecscale from the apex of the jet (Dermer & Schlickeiser 1994,Blandford & Levinson 1995, Ghisellini & Madau 1996). Theradio core, which is the source of radio emission in the jetrepresents a transition zone from synchrotron self absorbedregion to optically thin region (Blandford & K¨onigl 1979).It is also believed that the radio core is generally located atpc scales from the central super massive black hole (SMBH).Correlation between γ and radio light curves will help oneto constrain the location of these emission regions. The longterm radio and γ -ray light curves are shown in Figure 7.From Figure 7, it is evident that flux variations in the γ -rayband is more compared to the radio band that suggest that γ -rays are produced close to the base of the jet, while, theradio emission is produced at much farther distance from theSMBH. We correlated the γ -ray and radio light curves fora bin size of 20 days using the discrete correlation functionmethod of Edelson & Krolik (1988). The correlation func-tion is shown in Figure 8. We found a strong correlationwith the correlation coefficient greater than 70% with theradio emission leading the γ -ray emission by 70 days. Thissuggests that the γ -ray and radio emissions are produced atdifferent locations along the jet axis. Similar result on radioleading the γ -ray flux variations is also seen in the blazar 3C84, where Britzen et al. (2019) found the radio emission tolead the γ -ray emission by 300-400 days. From the observedtime lag, following Prince (2019) we estimated the separa-tion between γ -ray and radio emission as 11 pc along thejet axis for an average β app value of 13.98 and a θ of 4.2 ◦ (Liodakis et al. 2017).We also estimated the significance of the DCF peaksobserved in cross-correlation. For that, we simulated 1000 γ -ray light curves by following the method mentioned inEmmanoulopoulos et al. (2013) and incorporated into acode by (Connolly 2016) and available for use . For sim-ulating the γ -ray light curves we used power law as theshape of the power spectral densities (PSD; slope = 1.5) andassumed a lognormal form for the probability density func-tion. This agrees well with the observations as the observeredflux distribution also has a lognormal shape. We also sim-ulated the radio light curve with PSD power law slope 2.0as suggested by Max-Moerbeck et al. (2014) for the signifi-cance estimation. Further, the simulated γ -ray light curvesare cross-correlated with the simulated radio light curve. A2 σ and 3 σ significance for each time lag was estimated andplotted horizontally in cyan and greenyellow color in Figure8. https://github.com/samconnolly/DELightcurveSimulation F ( − ) ( p h c m − s − ) Fermi-LAT
Time(MJD) F l u x ( J y ) OVRO
Figure 7.
The long term γ -ray and radio light curve used for cor-relation study. −200 −150 −100 −50 0 50 100 150 200Time lag (day )0.00.20.40.60.81.0 D C F Gamma-ray v Radio (15 GHz) bin ize=20 day
Figure 8.
Cross-correlation of γ -ray and radio emission. We also looked for the availability of multi-wavelength(MW) data for this source along with the
Fermi -LAT, butapparently, no multi-wavelength observations are availablefor the pre-flare and flare state except state P4. Therefore,we collected the observations from the Swift-XRT/UVOTand analyzed the X-ray and UV/optical data. Though wedo not have a good number of observations in X-ray andUV/optical, the corresponding multi-wavelength light curvefor P4 is shown in Figure 9. The significant peaks ob-served in γ -ray around MJD 58865 seem missing in X-rayand UV/optical because of the unavailability of observa-tional data. The observation before MJD 58865 in X-ray andUV/optical light curve seems to peak around MJD 58865,and the observation after MJD 58865 suggests the decay inthe flux after MJD 58865. Because of the highly sparsed datain X-ray and UV/optical, we could not do correlatio and thetime variability studies. MNRAS , 1–15 (0000)
Table 4.
Results of γ -ray SED analysis for various observed states.Various F . − Luminosity PowerLaw -log(Likelihood) TS curve states (10 − ph cm − s − ) (10 erg s − ) ΓQ1 1.42 ± ± ± ± ± ± ± ± ± ± ± ± ± ± α β Q1 1.14 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± cutoff Γ PLEC [GeV]Q1 1.18 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± break Γ Γ [GeV]Q1 1.23 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± We calculated the fastest γ -ray variability time scaleduring the flaring states of the source using F = F . ( t − t ) /τ d (2)where F and F are the fluxes at consecutive times t and t respectively and τ d is the flux doubling time also known asvariability time. We found the fastest variability time duringP4 with a value of 9.01 ± × − ph cm − s − ) respectively.We also looked for the Swift -XRT/UVOT observationsfor the state Q1 and F1, and we found only few observations(hence the multi-wavelength light curvs are not shown for Q1and F1). The X-ray and optical/UV SED was prepared fromthat for both the states and used in the final MW spectralenergy distribution (SED) modeling in the next section.
Fitting the observed SED of the source during various activ-ity states is an ideal way to constrain the physical processesresponsible for the broadband emission. Towards this, welooked at the availability of multiband data covering UV,optical and X-rays during the various activity periods ofthe source marked in Figure 1. Multi-wavelength data wassparsely available only for Q, F1 and P4 states. The multi-wavelength light curves for the state P4 is shown in Figure 9.Here too, X-ray and UV/optical observations are not avail-able during the peak of the γ -ray flares. We modelled theobserved SEDs during Q, F1 and P4 states using the python based and publicly available time dependent model GAM-ERA (Hahn 2015).Among all the flares observed in 2019 and 2020 (state http://joachimhahn.github.io/GAMERAMNRAS , 1–15 (0000) F γ Mulit-wavelength light curve for Peak P4Fermi-LAT F X − r a y F O p t i c a l U bandB bandV band
Time(MJD-58845) F U V W1 bandM2 bandW2 band
Figure 9.
Multi-wavelength light curves of P4. F γ is in units of10 − ph cm − s − , F X − ray has unit of 10 − erg cm − s − , andF optical and F UV is in units of 10 − erg cm − s − . F2), only P4 has good coverage of simultaneous multi-wavelength data and hence provides an ideal situation to gofor SED modeling. The other states like Q1 and F1 have veryfew observations in X-ray and optical/UV. The MW SEDsfor states Q1 and F1 are also produced and their parametersare compared with the brightest state P4. GAMERA calcu-lates the propagated electron spectrum for a given initial in-jected electron spectrum by solving the transport equationgiven in equation (3), and further estimates the synchrotron,synchrotron self-Compton (SSC), and inverse Compton (IC)emissions. ∂N ( E, t ) ∂t = Q ( E, t ) − ∂∂E (cid:16) b ( E, t ) N ( E, t ) (cid:17) (3)where, N ( E, t ) is the propagated electron spectrum esti-mated at a time ’t’ for the initially injected electron spec-trum ( Q ( E, t )). b ( E, t ) is dedicated to the radiative losscaused by physical processes, viz. synchrotron, SSC, and ICscattering. For the model fits here, we considered a singlezone emission model with a log parabola electron distribu-tion.
Further, we tried to derive few jet parameters for thissource based on our observational results. We can con-strain the Doppler factor from the γ - γ opacity argu-ment (Dondi & Ghisellini 1995, Ackermann et al. 2010) andwhich can be derived numerically by using the highest en-ergy photon detected in γ -ray during the flare. The argu-ment says that if the γ - γ interaction optical depth for the high energy photon is one, the minimum Doppler factor canbe defined as: δ min ∼ = (cid:20) σ T d L (1 + z ) f x ǫ t var m e c (cid:21) / (4)where, σ T is the Thompson scattering cross section(6.65 × − cm ), d L is the luminosity distance (4.175Gpc), f x is the X-ray flux measured in the 0.3 −
10 keV(4.908 × − erg/cm /s), ǫ = E/ m e c , E is the highest pho-ton energy ( ∼
77 GeV), and t var is the the observed variabil-ity time (9.01 ± ∼
12. In gen-eral, blazar has a similar bulk Lorentz and Doppler factorfor the emitting blob, i.e., Γ ∼ δ and which can provide theupper limit on the viewing angle of the jet, θ ≤ δ min =4.8 ◦ . The size and the location of the emission region canbe constrained for the flaring period. The upper limit onsize can be derived from the causality relation R ∼ c t var δ min /(1+z) ∼ × cm. Assuming a conical jet sce-nario where the emission is produced across the entire jetarea suggests the flaring site close to the central engine andthe distance can be estimated as d ∼ var δ min /(1+z) ∼ γ -ray luminosity can also be esti-mated for all the spectral shapes (PL, LP, PLEC, and BPL)by following the relation, L γ = 4 πD L Z E max E min E dNdE dE (5)where E min is the lower energy range of
Fermi -LAT (i.e.100 MeV) and E max is the energy of the highest photondetected during a particular period, dN/dE represents thevarious spectral models and D L is the luminosity distance(4.259 Gpc). The γ -ray luminosity corresponding to eachspectral models in their various states are shown in Table4. The obtained values suggest that the γ -ray luminosity ismore during the higher states P1, P2, P3, and P4 comparedto the Q1 and F1 states. In the leptonic scenario, BLR is believed to be the mainsource of seed photons that get up-scatted in the jet throughthe IC scattering and produces the high energy peak ofthe SED. Considering BLR as a thin spherical shell, makeseasy to estimate the size of the BLR (Ghisellini & Tavecchio2009) and which can be scaled as, R
BLR = 10 L / d, , whereL d, is the disk luminosity in units of 10 erg s − . Thedisk luminosity and mass of the SMBH for this source areestimated by Brotherton et al. (2015), and the values areM SMBH = 3.98 × M ⊙ and L disk = 9.2 × erg s − af-ter bolometric correction from Netzer (2019). After usingthe above value of L disk the R BLR is derived as 3.03 × cm ∼ BLR with the location of theemission region ( ∼ MNRAS , 1–15 (0000) in the comoving frame by, U ′ BLR = Γ η BLR L disk πcR BLR (6)where the η BLR is the fraction of disk emission processed inBLR, and typically it is around 10%, and c is the speed oflight in vacuum.The contribution of direct disk emission as seed pho-tons for IC scattering can not be ignored and hence we alsoestimated the accretion disk photon energy density in thecomoving frame from Dermer & Menon (2009), U ′ disk = 0 . R g l Edd L Edd πcz Γ (7)where, R g , l Edd = L disk /L Edd , and z are the gravitationalradius, the Eddington ratio, and the location of the emissionsite from the SMBH respectively. The gravitational radiusis found to be R g = 5.87 × cm for the black hole mass3.98 × M ⊙ . We did not consider dusty torus as a sourceof external photon field since there is no observational evi-dence. However, the dusty torus contribution could be im-portant if the emission site is outside the BLR.The calculated photon energy density in BLR and diskalong with the BLR temperature (10 K; Peterson 2006) anddisk temperature (1.1 × K; estimated from Eddington ra-tio and BH mass; Panda et al. 2018) were fixed as inputs inGAMERA, and the parameters of the input injected elec-tron distributions were kept free while modeling the multi-wavelength SED. The size of the emitting blob was fixedfrom the variability time calculation, and the jet magneticfield was set free to obtain the good fit value of the multi-wavelength SED.
We carried out the SED modeling for the states Q1, F1 andthe high state P4, and the multi-wavelength SED modelingplots are shown in Figure 10. The best fit model parametersfor SED modeling are shown in Table 5. The low energy peakis successfully constrained by the Synchrotron process andthe γ -ray high energy peak with the IC process. The SSCmechanism well describes the X-ray emission in the highstate P4, whereas in Q1 and F1 it is explained by externalCompton process. The size of the emission region is found tobe a bit more from the modeling than the estimated valuefrom the variability time. The magnetic field in the blobis very much similar to the other FSRQ type blazars likePKS 1510 −
089 (Prince et al. 2019), 3C 279 (Prince 2020),3C 454.3 (Das et al. 2020) etc. The Doppler factor and theLorentz factor were optimized to 20 and 15.5 to obtain thebest fit to the multi-wavelength SED.We also used the derived parameters to estimate the in-dividual jet power in electrons, magnetic field, and protons.The total jet power can be defined as, P jet = πr Γ c ( U e + U B + U P ) (8)where, U e , U B , and U P are the energy density in electrons,magnetic field, and protons. The size of the emitting zoneand its Lorentz factor is denoted by r and Γ. The jet isconsidered as plasma of leptons and protons with the ratio of20:1. The total jet power calculated here is always lesser thanthe total Eddington luminosity of the source. The power calculated for individual components are mentioned in theTable 5. Comparing with the other flaring blazars like PKS1510 −
089 (Prince et al. 2019), 3C 279 (Prince 2020), and 3C454.3 (Das et al. 2020) the total and individual componentspowers are in good agreement. A blazar sample has beenstudied by Ghisellini & Tavecchio (2015), and they foundthat most of the blazars in their sample have L disk /L Edd =0.1 and using this ratio the Eddington luminosity estimatedin our case is 9.2 × erg/s, which is much greater than thetotal jet power estimated here. As we can see in Table 5, thetotal jet power is dominated by the magnetic field and hencepowers the blazar, whereas both the leptons and protonspower do not provide sufficient power and are unable tosupply the energy to the radio lobes. Comparing the valuesobtained during various states suggest that more electronsand protons power are needed to transit the source fromstate Q1 to F1 and P4. Surprisingly the magnetic field andmagnetic power obtained during state Q1 is more comparedto state F1 and P4. A large value of minimum and maximumenergy is required in electrons to make the source transitfrom low state (Q1) to high state (F1 and P4). Analysis of the γ -ray light curves, suggest that manyblazars show lognormal behaviour in their flux distributions(Ackermann et al. 2015; Shah et al. 2018; Romoli et al.2018). Among blazars, such lognormal behaviour wasfirst detected in BL Lac, from RXTE observation(Giebels & Degrange 2009), later it was observed inmany blazars at various energy bands and time scales(H.E.S.S. Collaboration et al. 2010; Tluczykont et al. 2010;Kushwaha et al. 2016; Sinha et al. 2016, 2017, 2018;Khatoon et al. 2020). Lognormal flux distributions wereinitially found in the X-ray emission of black hole bi-nary Cygnus X-1 (Uttley & McHardy 2001; Quilligan et al.2002; Giebels & Degrange 2009), and are generally ex-plained by the fluctuations in the accretion disc, which im-ply multiplicative processes (Uttley et al. 2005; McHardy2010). However, fast (minute time scale) variability inthe blazars’ lightcurves, is difficult to produce in the disc(Narayan & Piran 2012), and supports to originate in thejet. Biteau & Giebels (2012) have shown that log-normalflux distribution can be explained by the multiplicative pro-cesses, however, according to Scargle (2020) log-normality ofmeasured flux values need not imply multiplicative process.On the other hand, Gaussian perturbation in the particleacceleration time scale is capable of producing a lognormalflux distribution (Sinha et al. 2018).We studied the flux distribution property for the sourceOQ334, using three days binned γ -ray flux lightcurve. To se-lect lightcurve with good statistics, we considered flux pointsfor which TS > = 9, and also the flux points detected atgreater than 2- σ level, such that F ∆ F >
2. We performedAnderson-Darling (AD) test, where null hypothesis proba-bility value (p-value) < . × − ,while r-value and p-value for the flux in log-scale are 0.57and 0.13 respectively, which implies that the flux distribu-tion is lognormal. To quantify this, we further fit the nor- MNRAS , 1–15 (0000) ν (Hz) -14 -13 -12 -11 -10 ν F ν ( e r g c m − s − ) Sync SSC ECBLRTotal period = 44 daysQuiescent-state ν (Hz) -14 -13 -12 -11 -10 -9 ν F ν ( e r g c m − s − ) Sync SSC ECBLRTotal period = 25 daysflare-1 ν (Hz) -14 -13 -12 -11 -10 -9 -8 ν F ν ( e r g c m − s − ) Sync SSC ECBLRDiskTotal period = 41 daysP4
Figure 10.
Multi-wavelength SED of states Q1, F1 and the high state P4. malized histogram of the logarithm of flux with the Gaussianand lognormal probability density functions (PDFs) (Figure11), these PDFs are shown by Shah et al. (2018). We foundthat the lognormal PDF significantly fits the distributionbetter with reduced chi-square, χ red ≈ χ red ≈ σ XS = p S − σ err ; where S is the sample variance and σ err represents the mean of the square of the measurementerrors (Vaughan et al. 2003) . The flux-rms plot is shownin Figure 12, where data is binned for a period of 50 daysto obtain sufficient statistics. The scatter plot is well fittedby a linear function with slope 0.43 ± r s )and the correlation probability (prob). The values of r s andprob are found as 0.76 and 2 . × − , indicating a strongcorrelation between flux and excess variance. The observedlognormal behaviour in the flux distribution and the propor- tionality between the average amplitude of variability to theflux, suggest that the variation in flux is log-normal. In this work, we present the first time detailed analysis of the γ -ray spectral and temporal behaviour as well as the broadband SED modelling of the FSRQ OQ 334. We summarizethe main results below,(i) The source was faint in the γ -ray band for about 10years. It showed a bright γ -ray flare during 2017, returnedback to the low brightness state, stayed in the low bright-ness state for few months and again moved to the higher γ -ray brightness state. Supermimposed on the high brightnessstate many small flares were observed, with the brightest γ -ray flare occuring in February 2020. Thus the source hasshown more than one episode of flaring activity in the γ -rayband between 2017 and 2020.(ii) During various brightness state of the source such asP1, P2, P3 and P4 states, no correlation of the γ -ray photonindex with the total flux of the source was found. MNRAS , 1–15 (0000) Table 5.
Multi-wavelength SED modeling results with the best fitted parameters values. The input injected electron distribution isLogParabola with reference energy 60 MeV.high state Parameters Symbols Values PeriodBLR photon density U ′ BLR BLR temperature T ′ BLR × KDisk photon density U ′ disk × − erg/cm Disk temperature T ′ disk × KSize of the emitting zone R 2.6 × cmDoppler factor of the emitting zone δ γ min γ max × Input injected electron spectrum (LP) α β j,e × erg/sJet power in magnetic field P j,B × erg/sJet power in protons P j,P × erg/sTotal jet power P jet × erg/sF1 25 daysMin Lorentz factor of emitting electrons γ min γ max × Input injected electron spectrum (LP) α β j,e × erg/sJet power in magnetic field P j,B × erg/sJet power in protons P j,P × erg/sTotal jet power P jet × erg/sP4 41 daysMin Lorentz factor of emitting electrons γ min γ max × Input injected electron spectrum (LP) α β j,e × erg/sJet power in magnetic field P j,B × erg/sJet power in protons P j,P × erg/sTotal jet power P jet × erg/s (iii) During most of the γ -ray brightness sates of thesource, the γ -ray spectrum was well fit by a PLEC spec-tral model.(iv) We found a time lag between the γ -ray and radioband light curve at the 2 σ significance level with the radiovariation leading variations in the γ -ray band by 70 days.The two emission regions are thus separated by ∼
11 pc.(v) We found the fastest variability time on scales of 9.01 ± × cm and 0.05 pc respec-tively.(vi) The broad band SED modelling indicates that thelocation of the γ -ray emission region is inside the BLR. Theobserved γ -ray emission during Q1, F1, and P4 states arethe combination of SSC and IC scattering. The physical pa-rameters obtained from SED modelling indicates that moreelectron and protron power are needed to transit the sourcefrom Q1 to F1 and further in P4 state. (vii) The flux distribution shows the lognormal behaviourin the source. ACKNOWLEDGEMENTS
We thank the referee for providing stimulating commentsin order to improve the paper. The project was partiallysupported by the Polish Funding Agency National ScienceCentre, project 2017/26/A/ST9/00756 (MAESTRO 9), andMNiSW grant DIR/WK/2018/12. R.P thanks Avik Ku-mar Das for SED modeling discussions. R.P. thanks Prof.Bo˙zena Czerny, Swayamtrupta Panda, and Michal Zajacekfor discussions. This work has made use of public
Fermi data obtained from FSSC. This research has also madeuse of XRT data analysis software (XRTDAS) developedby ASI science data center, Italy. This research has madeuse of radio data from OVRO 40-m monitoring programme(Richards et al. 2011) which is supported in part by NASA
MNRAS , 1–15 (0000) N o r m a li z ed c oun t s log10(F) ph cm -2 s -1 Figure 11.
Histogram of the logarithm of 3 days binned γ -rayfluxes. The red and blue lines represent the Gaussian and lognor-mal PDFs respectively. -2 0 2 4 6 8 10 12 14 16 0 5 10 15 20 25 30 σ XS ( - ) Flux (10 -8 ph cm -2 s -1 ) Figure 12.
Excess variance vs mean flux scatter plot, with the bestfit line (black). grants NNX08AW31G, NNX11A043G, and NNX14AQ89Gand NSF grants AST-0808050 and AST-1109911.
DATA AVAILABILITY
This research has made use of archival data from varioussources e.g.
Fermi , Swift , and OVRO observatory and theirproper links are given in the manuscript. All the models andsoftwares used in this manuscript are also publicly available.
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