Time-correlation between the radio and gamma-ray activity in blazars and the production site of the gamma-ray emission
W. Max-Moerbeck, T. Hovatta, J.L. Richards, O.G. King, T.J. Pearson, A.C.S. Readhead, R. Reeves, M.C. Shepherd, M. A. Stevenson, E. Angelakis, L. Fuhrmann, K.J.B. Grainge, V. Pavlidou, R.W. Romani, J.A. Zensus
aa r X i v : . [ a s t r o - ph . H E ] A ug Mon. Not. R. Astron. Soc. , 1–7 (0000) Printed 26 September 2018 (MN L A TEX style file v2.2)
Time-correlation between the radio and gamma-ray activity inblazars and the production site of the gamma-ray emission
W. Max-Moerbeck , ⋆ , T. Hovatta , , J. L. Richards , O. G. King , T. J. Pearson ,A. C. S. Readhead , R. Reeves , , M. C. Shepherd , M. A. Stevenson , E. Angelakis ,L. Fuhrmann , K. J. B. Grainge , V. Pavlidou , , , R. W. Romani , J. A. Zensus Cahill Center for Astronomy and Astrophysics, California Institute of Technology, Pasadena, CA 91125, USA National Radio Astronomy Observatory (NRAO), P.O. Box 0, Socorro, NM 87801, USA Aalto University Mets¨ahovi Radio Observatory, Mets¨ahovintie 114, 02540 Kylm¨al¨a, Finland Department of Physics, Purdue University, West Lafayette, IN 47907, USA Max-Planck-Institut f¨ur Radioastronomie, Auf dem H¨ugel 69, 53121 Bonn, Germany Department of Physics, University of Crete / Foundation for Research and Technology - Hellas, Heraklion 71003, Greece Jodrell Bank Centre for Astrophysics, School of Physics and Astronomy, The University of Manchester, M13 9PL W. W. Hansen Experimental Physics Laboratory, Kavli Institute for Particle Astrophysics and Cosmology, Department of Physics andSLAC National Accelerator Laboratory, Stanford University, Stanford, CA 94305, USA Departamento de Astronom´ıa, Universidad de Concepci´on, Casilla 160-C, Concepci´on, Chile
Accepted 2014 August 26. Received 2014 August 21; in original form 2014 June 20
ABSTRACT
In order to determine the location of the gamma-ray emission site in blazars, we investigatethe time-domain relationship between their radio and gamma-ray emission. Light-curves forthe brightest detected blazars from the first 3 years of the mission of the
Fermi Gamma-raySpace Telescope are cross-correlated with 4 years of 15 GHz observations from the OVRO40-m monitoring program. The large sample and long light-curve duration enable us to carryout a statistically robust analysis of the significance of the cross-correlations, which is inves-tigated using Monte Carlo simulations including the uneven sampling and noise propertiesof the light-curves. Modeling the light-curves as red noise processes with power-law powerspectral densities, we find that only one of 41 sources with high quality data in both bandsshows correlations with significance larger than 3 σ (AO 0235+164), with only two morelarger than even 2.25 σ (PKS 1502+106 and B2 2308+34). Additionally, we find correlatedvariability in Mrk 421 when including a strong flare that occurred in July-September 2012.These results demonstrate very clearly the difficulty of measuring statistically robust multi-wavelength correlations and the care needed when comparing light-curves even when manyyears of data are used. This should be a caution. In all four sources the radio variations lagthe gamma-ray variations, suggesting that the gamma-ray emission originates upstream ofthe radio emission. Continuous simultaneous monitoring over a longer time period is re-quired to obtain high significance levels in cross-correlations between gamma-ray and radiovariability in most blazars. Key words: galaxies: active — radio continuum: galaxies — gamma rays: galaxies —quasars: general — BL Lacertae objects: general
Blazars are active galactic nuclei with jets closely aligned tothe line of sight (e.g., Blandford & Konigl 1979). They arethe most numerous class of sources detected in the GeV bandby the Large Area Telescope (LAT) on the
Fermi Gamma-raySpace Telescope (Ackermann et al. 2011b). Blazars have double- ⋆ E-mail: [email protected] peaked broad-band spectral energy distributions and show strongvariability from radio to gamma-rays (e.g., von Montigny et al.1995). It is accepted that the low-energy emission is producedby synchrotron radiation from electrons within the jet, whilethe high-energy gamma-ray emission is produced by inverse-Compton scattering of a soft photon field by the same elec-trons (e.g., Jones, O’dell & Stein 1974; Dermer & Schlickeiser1993; Sikora, Begelman & Rees 1994; Bła˙zejowski et al. 2000)or by hadronic processes (e.g., Mannheim & Biermann 1992). c (cid:13) W. Max-Moerbeck et al.
That a common mechanism regulates the luminosity at high andlow energies is demonstrated by the correlation between themean radio flux density and mean gamma-ray flux (Kovalev et al.2009; Mahony et al. 2010; Nieppola et al. 2011). Ackermann et al.(2011a) and Pavlidou et al. (2012) showed that this correlation isnot an effect of distance modulation of the fluxes.The location of the gamma-ray emission site in blazarsis not yet known. Gamma rays may be produced, for exam-ple, in the radio-emitting regions (e.g., Jorstad et al. 2001), ormuch closer to the central engine (e.g., Blandford & Levinson1995). Radio observations with milli-arcsecond resolution haveresolved the radio-emitting regions and measured outflow veloc-ities, but at high energies the angular resolution is insufficientand we must infer the size and location of the emission regionsfrom flux variations. If gamma-ray and radio emission are trig-gered by shocks propagating along a relativistic jet, the time de-lay between flares in the two bands depends on their separa-tion. Several studies have found time-lagged correlation betweenthese two energy bands, but without a large sample with well-sampled light-curves it is difficult to assess the significance ofthe correlations (e.g., Marscher et al. 2008; Abdo et al. 2010a;Agudo et al. 2011a,b). In a statistical study of 183 bright
Fermi -detected sources, Pushkarev, Kovalev & Lister (2010) found that,on average, radio flares occur later than gamma-ray flares. A morerecent investigation using multiple radio frequencies and longerlight-curves (Fuhrmann et al. 2014) also found correlated radioand gamma-ray variability with a frequency dependent radio lag.In comparing multiwavelength light-curves of individualblazars over short time periods claims are often made for corre-lations but the actual significance is rarely computed. To remedythis situation and search for the existence of significant correla-tions and their physical origin we have undertaken a long-termradio monitoring campaign of a large number of blazars. We applyrobust statistical methods to estimate the significance of correla-tions and find that most of the blazars in our sample only showcorrelations below 2.25 σ . Only three out of 41 objects show cor-relations above a 2.25 σ level where we expect to find one randomuncorrelated source to appear, with only one above the 3 σ level ofsignificance. Thus, it is clear that establishing a statistically signif-icant cross-correlation is more difficult than is generally assumed.We also provide a tentative interpretation for the origin of the timelag and the location of the gamma-ray emission site. Through our Owens Valley Radio Observatory (OVRO) 40-m pro-gram, twice per week we observe all sources in the CandidateGamma Ray Blazar Survey (CGRaBS, Healey et al. 2008) andthe blazars detected in the
Fermi -LAT AGN catalogs (Abdo et al.2010b; Ackermann et al. 2011b) north of declination − ◦ at15 GHz. This sample has a total of 1,593 sources, of which 685have gamma-ray detections, with 454 and 634 in the first and sec-ond Fermi -LAT AGN catalogs respectively.Radio observations from 1 January 2008 to 26 February 2012are included in this study. The radio flux density measurementshave a thermal noise floor of ∼ mJy with an additional 2% con-tribution from pointing errors. The flux density scale is determinedfrom regular observations of 3C 286 assuming the Baars et al.(1977) value of 3.44 Jy at 15.0 GHz, giving a 5% overall scale ac-curacy. A detailed discussion of the observing strategy and calibra-tion procedures can be found in Richards et al. (2011). The radio light-curves have different characteristics, with a mean and stan-dard deviation for length ± days, number of data points ± , and average sampling . ± . days. The light-curvesof the cases discussed in this paper are shown in Figure 1. Themonitoring program is ongoing and all the light-curves are madepublic on the program website .The LAT is a pair-conversion gamma-ray telescope, sensi-tive to photon energies from about 20 MeV up to >
300 GeV,that observes the whole sky once every three hours (Atwood et al.2009).
Fermi -LAT light-curves with 7 d time bins from 4 Au-gust 2008 through 12 August 2011 were produced for 86 sourcesdetected in at least 75% of monthly time bins (Nolan et al.2012). We use an unbinned likelihood analysis, with source spec-tral models and positions from Ackermann et al. (2011b). Wefroze the sources spectral parameters (including the target) andlet only the flux vary in sources within 10 ◦ of the target. Weuse Fermi -LAT
ScienceTools-v9r23p1 with P7 V6 sourceevent selection and instrument response functions, diffuse mod-els gal 2yearp7v6 v0.fits and iso p7v6source.txt ,only photons with zenith angle < ◦ and other standard data cutsand filters (e.g., Abdo et al. 2011) . We use a region of interestof 10 ◦ radius and a source region of 15 ◦ radius. Photon integralfluxes from 100 MeV to 200 GeV are reported when the test statis-tic TS > , and σ upper limits when TS < ( ∼ % of thedata). The radio light-curves are sampled unevenly due to weather andother problems. The gamma-ray light-curves are weekly aver-ages, but some measurements are upper limits ( ∼ % of thedata) that are ignored in this analysis. We tested the possibleeffect of ignoring upper limits by using the best flux estimateindependent of TS and the upper limit itself as a flux, obtain-ing comparable results in all cases, thus showing that it is safeto ignore upper limits for this sample of bright sources. Thecross-correlation is measured using the discrete cross-correlationfunction (DCF, Edelson & Krolik 1988), with local normaliza-tion (Welsh 1999), also known as local cross-correlation function(LCCF). We find that the LCCF results in a greater detection ef-ficiency for known correlations injected in simulated data. We es-timate the cross-correlation significance with Monte Carlo sim-ulations that assume a simple power-law power spectral densitymodel for the light-curves ( PSD ∝ /f β ), motivated by previ-ous work (e.g., Hufnagel & Bregman 1992; Edelson et al. 1995;Uttley et al. 2003; Ar´evalo et al. 2008; Chatterjee et al. 2008;Abdo et al. 2010c). We simulate a large number of independent,uncorrelated light-curve pairs that replicate the sampling, mea-surement error distribution, and statistical properties of the ob-servations, using the method of Timmer & Koenig (1995). Fromthe distribution of cross-correlations at each time lag we estimatethe chance probability of obtaining a given correlation value. Themethod is described in detail by Max-Moerbeck et al. (2014) http://astro.caltech.edu/ovroblazars/ Science Tools, LAT data, and diffuse emission mod-els are available from the
Fermi
Science Support Center, http://fermi.gsfc.nasa.gov/ssc The test statistic is a measure of detection significance, defined asTS = 2∆ log(likelihood) between models with and without the source(Mattox et al. 1996). c (cid:13) , 1–7 orrelated variability and high energy emission in blazars For 13 sources where a PSD fit is possible in both bands,we use the best-fitting power-law index values; for the oth-ers, we use population-average values as described below. Wecharacterize the PSDs using a modified implementation ofUttley, McHardy & Papadakis (2002) that uses sampling windowfunctions to reduce red-noise leakage. The effects of uneven sam-pling are incorporated by comparing the observed PSD to thosederived from simulated light-curves. We compute the PSD fromthe data and obtain a mean PSD with scatter from simulated light-curves for several values of the power-law index. The best fit isfound by comparing the PSD from the data with the simulated onesusing a χ test. We find good constraints for the radio PSD power-law index for 43 sources (Table 1). The distribution of indices isclustered around 2.3, with a typical error of 0.4, and is consistentwith a single value equal to the sample mean of . ± . . We adopta value of β radio = 2 . for sources with no fitted radio PSD. Inthe gamma-ray band the PSD power-law index is constrained for29 sources. The distribution has peaks at about 0.5 and 1.6. Thepeak at 1.6 is consistent with results for the brightest sources fromAbdo et al. (2010c) ( . ± . for FSRQs and . ± . for BLLacs) but steeper than found in Ackermann et al. (2011b) (about1.15 for the average PSD of the brightest blazars). For sourceswith no gamma-ray PSD fit, we assume β γ = 1 . which givesconservative estimates of the cross-correlation significance. We estimated the cross-correlation between the radio and gamma-ray light-curves and its significance for 41 of the 86 sources. 23are excluded for being non-variable at the 3 σ level (a χ test ofthe null hypothesis of constant flux shows that the observed vari-ations are consistent with observational noise). We also exclude“noisy” light-curves where more than / of the variance comesfrom observational noise. We also exclude light-curves consistentwith a linear trend in the overlapping section; for such sources,longer light-curves are needed to probe the relevant time scales.These two restrictions eliminate 22 more objects.To include the effects of red-noise leakage and aliasing wesimulate 10-year light-curves with a 1 d time resolution. The cross-correlation is estimated for independent bins of 10 d. In each case,we simulate 20,000 independent light-curve pairs using the appro-priate PSD (Section 3 and Table 1). To eliminate spurious cor-relations we restrict the time lag search interval to ± . timesthe length of the shortest light-curve. For each source the po-sition and significance of the most significant cross-correlationpeak is given in Table 1. The peak position uncertainty is es-timated by “flux randomization” and “random subset selection”(Peterson et al. 1998). The error on the significance is determinedusing a bootstrap method (Max-Moerbeck et al. 2014). We set thesignificance threshold at 97.56% ( . σ ), at which we expect tohave one object with a chance high correlation.At this threshold, three of our 41 sources show interest-ing levels of correlation: AO 0235+164, τ = − ± d with99.99% significance (the only case with significance > σ );PKS 1502+106, τ = − ± d with 97.54% significance ; and This is consistent with the threshold of 97.56% when the 0.13% uncer-tainty is considered as shown in Table 1
B2 2308+34, τ = − ± d with 99.33% significance. The re-sults are presented in Figure 1, where a negative lag indicates thatradio variations occur after gamma-ray variations.Significant correlated variability has been reported byAgudo et al. (2011b) for AO 0235+164, with a delay of about − d using radio data up to MJD 55000. With our longer light-curves, we find a significant correlation at a delay of − d, al-though the cross-correlation peak is broad and there is a secondpeak of comparable amplitude and significance at − d. Thisadds a large uncertainty when considered in the estimation of thelocation of the gamma-ray emission site because our current datacannot discriminate between these two peaks. No significant cross-correlations have been previously reported for PKS 1502+106 orB2 2304+34. A major radio flare was observed from Mrk 421 on 21 Septem-ber 2012, when its 15 GHz flux density reached . ± . Jy,approximately 2.5 times its previous median value (Hovatta et al.2012). On 16 July 2012, the source was detected at its highest levelto date by
Fermi -LAT. Its integrated photon flux for E > MeVwas (1 . ± . × − ph cm − s − , a factor of 8 greater than theaverage in the second Fermi -LAT catalog (D’Ammando & Orienti2012).Mrk 421 does not show significant correlated variability whenanalysed as part of the uniform sample described in Section 2. Fur-thermore, neither the radio nor the gamma-ray PSD can be fit sothe population averages were used as described in Section 3. To in-clude the radio and gamma-ray flares we extended the light-curvesbeyond the period used for the uniform sample (Section 2). Werepeated the analysis using these extended light-curves and found . < β radio < . , with best fit value 1.8, and . < β γ < . ,with best fit value 1.6. The cross-correlation peak at − ± d hasa significance between 96.16% and 99.99% depending on the PSDmodel. The significance obtained using the best-fit PSD models is98.96% (Figure 2). This result should be treated with caution: ex-tending the data set after noticing the flare is “a posteriori” statis-tics, and as such cannot be used to make inferences about the rateat which significant correlations are found in the general blazarpopulation. The duration of the correlated events is typically a few hundreddays and a detailed model is needed to understand the relationshipbetween the lags and the location of the emission regions. Here,we ignore the flare duration and tentatively interpret the delays us-ing a model in which a moving emission region, confined to thejet, produces the radio and gamma-ray activity. This region movesoutward at the bulk jet speed βc (Figure 3), and corresponds tothe moving disturbances observed with very long baseline interfer-ometry (VLBI). The gamma-ray flare becomes observable at dis-tance d γ from the central engine, after crossing the surface of unitgamma-ray opacity (gamma-sphere, Blandford & Levinson 1995).Likewise, the radio flare becomes observable upon crossing thesurface of unit radio opacity (“radio core”), at distance d core fromthe central engine (Blandford & Konigl 1979).The time lag between these wavebands provides an estimateof the interval between the emergence of gamma-ray and radio c (cid:13) , 1–7 W. Max-Moerbeck et al.
Figure 1.
Light-curves (left) and cross-correlation (right) for sources with significant cross-correlation. Contours indicate the cross-correlations significances(red dotted line: σ ; orange dash-dot line: σ ; green dashed line: σ ). The most significant peak for AO 0235+164 is at − ± d with 99.99% significance,for PKS 1502+106 is at − ± d with 98.09% significance for the best fit PSD model and 97.54% for the lower limit, and for B2 2308+34 is at − ± dwith 99.99% significance for the best fit PSD model and 99.33% for the lower limit. The significance lower limit for PKS 1502+106 is above the 97.56%threshold within the error (see Table 1). c (cid:13) , 1–7 orrelated variability and high energy emission in blazars Table 1.
Cross-correlation significance results (see complete table at the end of the document. The journal version has the table in landscape mode.)
Figure 2.
Light-curves (left) and cross-correlation (right) for Mrk 421. The most significant peak is at − ± d with 98.96% significance. Colors and linestyles as in Figure 1. radiation. The distance travelled by the emission region betweenthe peaks in gamma-ray and radio emission is d = Γ D βc ∆ t (1 + z ) , (1)where Γ is the bulk Lorentz factor, D is the Doppler factor, ∆ t is the time lag and z is the redshift (Pushkarev, Kovalev & Lister2010). The apparent jet speed, β app , is determined from VLBImonitoring and the Doppler factor is estimated from the radio vari-ability time scale (Hovatta et al. 2009). Doppler factors from thismethod have a typical 27% scatter for individual flares in a givensource, which we adopt as the uncertainty in D . From D and β app ,we obtain Γ and the jet viewing angle θ (e.g., Hovatta et al. 2009). β app and D are not measured simultaneously with our observa-tions; we assume them constant in our calculations.We estimate d γ = d core − d , where d core is determined fromVLBI measurements of the angular diameter of the radio core, θ core . This, plus the intrinsic opening angle, α int , and redshift,gives d core ∼ ( θ core / d A tan( α int / , (2)where d A is the angular diameter distance, obtained assuminga Λ CDM cosmology with H = 70 km s − Mpc − , Ω m =0 . and Ω Λ = 0 . (Komatsu et al. 2011). Equation 2 is onlyvalid for a conical jet with vertex at the central engine. How-ever, there is observational evidence for collimation in the M87jet, which we use as a prototype for the collimation proper-ties of other sources where no such information is available.Asada & Nakamura (2012) model the jet profile as z jet ∝ r a ,where r is the radius of the jet cross-section at distance z jet fromthe central engine, and found a = 1 . ± . for z jet . . × r s , where r s is the Schwarzschild radius, and a = 0 . ± . at distances outside the collimation zone. Assuming the radio coreis in the collimation zone and setting r and dr/dz jet equal for bothmodels d core (coll) = 1 a d core (cone) . (3)This model reduces our estimate of d core by a factor of 1.73.We thus obtain lower and upper limits on d core using these alter-natives.Detailed distance estimates are provided below forAO 0235+164 the highest significance case and Mrk 421which has the sharpest cross-correlation peak. For PKS 1502+106only the final result is given as a reference, and for B2 2308+34there are no published VLBI results which makes it impossible toprovide a constraint. A summary of the results for AO 0235+164is given in Table 2. Estimation of d : For AO 0235+164 we have D = 24 (Hovatta et al. 2009) but no β app since its jet is unresolved in15 GHz VLBI (Lister et al. 2009). We assume the source is seenat the critical angle, θ cr = θ = 2 . ◦ . We obtain d = 37 . ± . pc for τ = − ± d, the most significant time lag, and d = 7 . ± . pc for the peak at τ = − ± d. For comparison,if we use θ = θ cr / , we obtain d = 20 ± pc ( . ± . pc) forthe peak at − d ( − d). If θ = 0 , we obtain d = 18 ± pc( . ± . pc).For Mrk 421, we use a preliminary variability Doppler fac-tor for the recent flare of D = 4 (Richards et al. 2013). β app isuncertain, with jet components consistent with being stationary(Lico et al. 2012). Assuming θ ∼ ◦ (Lico et al. 2012 estimate2 ◦ –5 ◦ ), then Γ ∼ . and d ∼ . pc. It is difficult to estimate un-certainties because of the limited knowledge of the jet properties. Estimation of d core : The core angular size (FWHM) havebeen measured for AO 0235+164 ( θ core = 0 . ± . mas)(Lister et al. 2009). Here, we have averaged multiple epochs, withuncertainties estimated from their scatter. For Mrk 421 we use θ core = 0 . mas (Kovalev et al. 2005), assuming an error of . mas, the angular resolution of the observations.For the intrinsic opening angle we use α int . . ◦ for c (cid:13) , 1–7 W. Max-Moerbeck et al.
Figure 3.
Model for the interpretation of time lags. The central engine launches a jet in which disturbances propagate at speed βc . A moving disturbance(shaded area) is depicted at two times: t γ at which gamma-ray emission peaks and t R for the peak of radio emission when crossing the radio core. Table 2.
Results of the distance estimates for the different jet components in the most significant case. ( a ) Source d d core (coll) d core (cone) d γ (coll) d γ (cone) [pc] [pc] [pc] [pc] [pc]AO 0235+164, τ = − ± d ± & ± & ± & − ± ( b ) & ± AO 0235+164, τ = − ± d ± & ± & ± & ± & ± (a): Columns: d : distance travelled by the emission region between the peaks in gamma-ray and radio emission. d core (coll / cone) : distance between radio core and central engine with and without collimation. d γ (coll / cone) :Location of the peak of the gamma-ray emission with respect to the central engine.(b): The negative value is an artifact produced by the large measurement errors. AO 0235+164, which is the critical angle upper limit from Sec-tion 6, consistent with what is used by Agudo et al. (2011b). ForMrk 421, we adopt α int = 2 . ◦ , the mean value for BL Lacs fromPushkarev et al. (2009).The estimates of d core for a conical jet are & ± pc forAO 0235+164, and about 2.4 pc for Mrk 421. For a collimated jetwe obtain d core & ± pc for AO 0235+164, and about 1.4 pcfor Mrk 421.Similar estimates can be made for PKS 1502+106, resultingin d γ of ± pc for a conical jet and ± pc for the collimatedjet case. Out of 41 sources for which a detailed correlation analysis is pos-sible, three show correlations with larger than 2.25 σ significance,with only one of those larger than 3 σ . In all cases radio variationslag behind gamma-ray variations, suggesting that the gamma-rayemission originates upstream of the radio emission. We use a sim-ple model to tentatively estimate the distance from the black holeat which the gamma-ray emission is produced. Due to correlationpeak breadth and uncertain jet parameters, these estimates havelarge uncertainties. In particular AO 0235+164 shows two peaksin its cross-correlation with comparable amplitude and equivalentsignificance, leading to a highly uncertain location for the gamma-ray emission site.These results show that correlations between radio andgamma-ray light-curves of blazars are only found in a minority ofthe sources over a four-year period. This could indicate a complexmultiwavelength connection not detectable with the tools and datawe use. A better understanding of this connection requires contin-uation of the OVRO and Fermi monitoring and will benefit from the addition of polarization and other wavebands and methods thatprovide additional information.
ACKNOWLEDGMENTS
We thank Russ Keeney for his support at OVRO. The OVRO pro-gram is supported in part by NASA grants NNX08AW31G andNNX11A043G and NSF grants AST-0808050 and AST-1109911.T.H. was supported by the Jenny and Antti Wihuri foundationand Academy of Finland project number 267324. Support fromMPIfR for upgrading the OVRO 40-m telescope receiver is ac-knowledged. W.M. thanks Jeffrey Scargle, James Chiang, StefanLarsson and Iossif Papadakis for discussions. The National Ra-dio Astronomy Observatory is a facility of the National ScienceFoundation operated under cooperative agreement by AssociatedUniversities, Inc. The
F ermi
LAT Collaboration acknowledgessupport from a number of agencies and institutes for both develop-ment and the operation of the LAT as well as scientific data analy-sis. These include NASA and DOE in the United States, CEA/Irfuand IN2P3/CNRS in France, ASI and INFN in Italy, MEXT, KEK,and JAXA in Japan, and the K. A. Wallenberg Foundation, theSwedish Research Council and the National Space Board in Swe-den. Additional support from INAF in Italy and CNES in Francefor science analysis during the operations phase is also gratefullyacknowledged. We thank the anonymous referee for constructivecomments that greatly improved the presentation of some sectionsof this paper. c (cid:13) , 1–7 orrelated variability and high energy emission in blazars REFERENCES
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Table 1.
Cross-correlation significance results
Source Name Class Class z β bestradio β lowradio β upradio β best γ β low γ β up γ τ DCF Sig. Sig. low
Sig. up Sig. unc
Sig. σ Flagsname 2FGL optical ( a ) SED ( a ) ( a ) ( b ) ( b ) ( b ) ( b ) ( b ) ( b ) [d] ( c ) % ( c ) % ( c ) % ( c ) % ( c ) σ ( c ) ( d )
4C +01.02 J0108.6+0135 FSRQ LSP 2.099 2.3 ... ... 1.6 ... ... − ±
16 0.33 58.64 ... ... 0.49 0.82 ngS2 0109+22 J0112.1+2245 BL Lac ISP 0.265 2.0 1.4 2.4 0.9 0.0 1.8 − ±
13 0.24 59.63 36.05 94.45 0.48 0.84 ...4C 31.03 J0112.8+3208 FSRQ LSP 0.603 2.3 ... ... 1.6 ... ... 190 ±
12 0.36 54.65 ... ... 0.5 0.75 tgOC 457 J0136.9+4751 FSRQ LSP 0.859 1.6 1.4 1.9 1.6 ... ... − ±
14 0.62 95.75 92.92 97.76 0.19 2.03 ...PKS 0215+015 J0217.9+0143 FSRQ LSP 1.721 2.3 ... ... 1.6 ... ... − ±
15 0.38 65.75 ... ... 0.46 0.95 ...S4 0218+35 J0221.0+3555 FSRQ ... 0.944 2.3 ... ... 1.6 ... ... 190 ±
12 0.51 93.08 ... ... 0.25 1.82 ng3C 66A J0222.6+4302 BL Lac ISP ... 1.9 0.4 2.5 0.6 0.2 1.2 460 ±
14 0.27 81.16 60.78 99.96 0.39 1.32 ...4C +28.07 J0237.8+2846 FSRQ LSP 1.206 2.7 2.5 3.0 1.6 ... ... − ±
13 0.63 83.59 81.78 85.31 0.37 1.39 ...
AO 0235+164 J0238.7+1637 BL Lac LSP 0.94 2.3 ... ... 0.1 0.0 1.0 − ± NGC 1275 J0319.8+4130 Radio Gal ... 0.018 2.3 ... ... 1.6 1.1 2.2 − ±
13 0.59 81.54 73.56 93.0 0.39 1.33 ...PKS 0420 −
01 J0423.2 − − ±
16 0.49 76.65 75.2 79.57 0.42 1.19 ...PKS 0440 −
00 J0442.7 − ±
32 0.22 59.27 31.11 78.29 0.47 0.83 ...TXS 0506+056 J0509.4+0542 BL Lac ISP 0.0 2.2 0.6 2.7 1.6 ... ... 450 ±
15 0.25 61.54 58.89 96.86 0.5 0.87 tg, ngB2 0716+33 J0719.3+3306 FSRQ LSP 0.779 2.3 ... ... 1.6 ... ... 140 ± − ±
11 0.37 44.89 39.86 55.97 0.49 0.6 ...4C +14.23 J0725.3+1426 FSRQ LSP 1.038 2.3 ... ... 0.5 0.1 1.0 150 ±
13 0.19 61.5 43.28 76.51 0.48 0.87 ...PKS 0736+01 J0739.2+0138 FSRQ LSP 0.189 2.3 ... ... 1.6 ... ... − ±
15 0.5 79.7 ... ... 0.39 1.27 ngGB6 J0742+5444 J0742.6+5442 FSRQ LSP 0.723 1.9 0.6 2.9 1.6 ... ... − ± −
07 J0808.2 − − ±
16 0.59 99.52 88.86 99.99 0.07 2.82 ...PKS 0829+046 J0831.9+0429 BL Lac LSP 0.174 1.9 0.6 2.3 1.6 ... ... 150 ±
16 0.42 81.09 75.95 99.89 0.4 1.31 ng4C +71.07 J0841.6+7052 FSRQ LSP 2.218 2.3 ... ... 1.6 ... ... 210 ±
11 0.63 91.74 ... ... 0.28 1.74 tg, ngOJ 287 J0854.8+2005 BL Lac ISP 0.306 2.3 ... ... 1.6 ... ... − ±
16 0.38 62.48 ... ... 0.48 0.89 ngPKS 0906+01 J0909.1+0121 FSRQ LSP 1.026 2.3 ... ... 1.6 ... ... 510 ±
16 0.39 68.85 ... ... 0.48 1.01 ...S4 0917+44 J0920.9+4441 FSRQ LSP 2.189 2.3 ... ... 1.6 0.8 2.1 − ±
12 0.49 70.51 62.12 92.86 0.48 1.05 ...MG2 J101241+2439 J1012.6+2440 FSRQ ... 1.805 2.3 ... ... 1.6 ... ... 490 ±
49 0.57 99.51 ... ... 0.07 2.81 tr, tg, nr, ng4C +01.28 J1058.4+0133 BL Lac LSP 0.888 2.3 ... ... 1.6 ... ... 510 ±
15 0.6 93.42 ... ... 0.25 1.84 ngMrk 421 J1104.4+3812 BL Lac HSP 0.031 2.3 ... ... 1.6 ... ... − ±
10 0.39 73.78 ... ... 0.43 1.12 ngPKS 1124 −
186 J1126.6 − ±
11 0.76 97.62 95.88 99.2 0.15 2.26 ...Ton 599 J1159.5+2914 FSRQ LSP 0.725 2.1 1.8 2.6 1.0 0.5 1.6 − ±
18 0.42 79.05 55.61 95.39 0.39 1.25 ...1ES 1215+303 J1217.8+3006 BL Lac HSP 0.13 2.3 ... ... 1.6 ... ... 120 ± − ±
10 0.59 99.78 96.51 99.99 0.05 3.06 ...3C 273 J1229.1+0202 FSRQ LSP 0.158 2.2 0.6 2.8 0.8 0.4 1.1 − ±
16 0.41 86.32 68.08 99.99 0.33 1.49 ...MG1 J123931+0443 J1239.5+0443 FSRQ LSP 1.761 2.3 ... ... 1.6 ... ... − ±
15 0.67 89.01 ... ... 0.3 1.6 ...3C 279 J1256.1 − ±
10 0.47 65.03 54.86 80.94 0.48 0.94 ...OP 313 J1310.6+3222 FSRQ LSP 0.997 2.2 1.9 2.4 1.6 ... ... 500 ±
74 0.32 49.79 47.75 54.35 0.5 0.67 ...GB 1310+487 J1312.8+4828 FSRQ LSP 0.501 2.3 ... ... 0.3 0.0 1.0 − ±
15 0.36 93.09 77.42 96.31 0.25 1.82 ...PKS 1329 −
049 J1332.0 − − ±
15 0.51 99.56 90.36 99.98 0.07 2.85 ...B3 1343+451 J1345.4+4453 FSRQ LSP 2.534 2.1 0.6 2.6 1.6 ... ... 30 ±
13 0.51 67.5 62.53 99.88 0.48 0.98 ...PKS 1424+240 J1427.0+2347 BL Lac HSP 0.0 2.3 ... ... 1.6 ... ... 110 ±
13 0.45 95.59 ... ... 0.2 2.01 tr, tg, nr, ng
PKS 1502+106 J1504.3+1029 FSRQ LSP 1.839 2.5 2.2 2.8 1.6 ... ... − ±
13 0.87 98.09 97.54 98.7 0.13 2.34 ...
PKS 1510 −
08 J1512.8 − − ± ± ±
16 0.38 78.75 ... ... 0.41 1.25 tg, ngPG 1553+113 J1555.7+1111 BL Lac HSP 0.0 2.3 ... ... 1.6 ... ... 530 ±
17 0.43 99.69 ... ... 0.06 2.96 tr, tg, nr, ng4C +38.41 J1635.2+3810 FSRQ LSP 1.813 2.1 1.4 2.9 1.5 1.1 1.8 500 ± − ±
12 0.5 98.11 ... ... 0.13 2.35 tg, ngB3 1708+433 J1709.7+4319 FSRQ LSP 1.027 2.3 ... ... 1.6 ... ... − ±
12 0.59 80.86 ... ... 0.39 1.31 ...PKS 1730 −
13 J1733.1 − − ±
14 0.5 73.67 68.25 83.55 0.44 1.12 tgS4 1749+70 J1748.8+7006 BL Lac ISP 0.77 2.2 1.4 2.7 0.4 0.0 1.1 230 ±
10 0.55 99.48 87.86 99.99 0.07 2.79 ...S5 1803+784 J1800.5+7829 BL Lac LSP 0.68 2.3 ... ... 0.4 0.0 0.9 − ±
11 0.47 98.43 89.17 99.89 0.12 2.42 tg4C +56.27 J1824.0+5650 BL Lac LSP 0.664 1.9 0.4 2.9 1.6 ... ... − ±
11 0.46 80.36 74.26 99.99 0.39 1.29 tg, ngB2 1846+32A J1848.5+3216 FSRQ LSP 0.798 2.2 1.9 2.7 1.6 ... ... − ±
12 0.53 65.95 61.37 69.83 0.47 0.95 ...S4 1849+67 J1849.4+6706 FSRQ LSP 0.657 1.9 0.8 2.5 0.6 0.2 1.2 − ±
10 0.38 90.61 61.42 99.91 0.3 1.68 ...1ES 1959+650 J2000.0+6509 BL Lac HSP 0.047 2.3 ... ... 1.6 ... ... − ±
13 0.41 90.97 ... ... 0.3 1.69 tg, ngPKS 2023 −
07 J2025.6 − ±
12 0.55 72.89 ... ... 0.44 1.1 ...OX 169 J2143.5+1743 FSRQ LSP 0.211 2.3 ... ... 0.0 0.0 0.5 − ±
11 0.34 99.04 89.26 98.96 0.1 2.59 ...BL Lacertae J2202.8+4216 BL Lac ISP 0.069 2.1 0.9 2.7 2.0 1.5 2.4 − ±
14 0.71 85.27 72.37 99.99 0.36 1.45 ...PKS 2201+171 J2203.4+1726 FSRQ LSP 1.076 2.0 1.5 2.3 1.6 ... ... 530 ±
10 0.58 93.29 92.2 97.46 0.26 1.83 ngPKS 2227 −
08 J2229.7 − ±
14 0.51 80.95 79.82 83.36 0.41 1.31 ngCTA 102 J2232.4+1143 FSRQ LSP 1.037 2.4 1.7 2.8 1.6 ... ... − ± ±
14 0.35 63.42 58.81 99.39 0.48 0.9 ...3C 454.3 J2253.9+1609 FSRQ LSP 0.859 2.4 1.9 2.6 1.6 ... ... − ±
18 0.55 71.96 70.12 78.46 0.46 1.08 ...
B2 2308+34 J2311.0+3425 FSRQ LSP 1.817 2.1 0.6 2.7 0.2 0.0 0.9 − ±
14 0.73 99.99 99.33 99.99 ... 3.89 ... (a): Optical class, SED class and redshifts from Ackermann et al. (2011b). z = 0 . indicate that redshift could not be evaluated with available optical spectrum. (b): β best / low / upwaveband : PSD power-law index for given “waveband”: “best” for best fit, “low” for lower limit and “up” for upper limit. τ : radio/gamma-ray time lag, negativevalues indicate radio lags gamma-ray variations. DCF: discrete correlation function estimate. Sig.: Significance of the correlation. Sig low / up / unc /σ : Significance lowerlimit, upper limit, uncertainty and significance in units of standard deviations. (c): τ : radio/gamma-ray time lag, negative values indicate radio lags gamma-ray variations.DCF: discrete correlation function estimate. Sig.: Significance of the correlation. Sig low / up / unc /σ : Significance lower limit, upper limit, uncertainty and significance inunits of standard deviations. (d): Flags are: noisy light curves in radio (nr) and gamma-ray band (ng); trends in radio (tr) and gamma-ray band (tg) (see text).c (cid:13)000