A VLA-GMRT Look at 11 Powerful FR II Quasars
S. Vaddi, P. Kharb, R.A. Daly, C.P. O'Dea, S.A. Baum, D.K. Deo, T.C. Barbusca, C. Murali
MMNRAS , 1–15 (2018) Preprint 27 December 2018 Compiled using MNRAS L A TEX style file v3.0
A VLA − GMRT Look at 11 Powerful FR II Quasars
S. Vaddi, (cid:63) P. Kharb, R. A. Daly, C. P. O’Dea, , S. A. Baum, , D. K. Deo, , T.C. Barbusca, C. Murali , National Centre for Radio Astrophysics - Tata Institute of Fundamental Research, Ganeshkhind, Pune 411007, India Penn State University, Berks Campus, P. O. Box 7009, Reading, PA 19610-6009 Physics and Astronomy, University of Manitoba, Winnipeg, MB R3T 2N2 Canada Rochester Institute of Technology, Rochester, New York, 14623, USA University of Missouri-Kansas City, Kansas City, Missouri, 64110, USA University of Texas at Dallas, Texas, 75080, USA Indian Institute of Astrophysics, II Block, Koramangala, Bangalore 560034, India † Accepted XXX. Received YYY; in original form ZZZ
ABSTRACT
We present results from 1.4 and 5 GHz observations at matched resolution with theKarl G. Jansky Very Large Array (VLA) of 11 powerful 3C FR II quasars. We exam-ine the 11 quasars along with a sample of 13 narrow-line FR II radio galaxies and findthat radio-loud unification largely holds but environmental effects cannot be ignored.The radio core prominence, largest linear size, and axial ratio parameter values indi-cate that quasars are at relatively smaller angles compared to the radio galaxies andthus probe orientation. Lack of correlation between statistical orientation indicatorssuch as misalignment angle and radio core prominence, and larger lobe distortionsin quasars compared to radio galaxies suggest that intrinsic/environment effects arealso at play. Some of 150 MHz observations with the TGSS-GMRT reveal peculiarlobe morphologies in these FR II sources, suggesting complex past lives and possiblyrestarted AGN activity. Using the total 150 MHz flux density we estimate the time-averaged jet kinetic power in these sources and this ranges from ( − ) × erg s − ,with 3C 470 having the highest jet kinetic power. Key words: quasars: general — radio continuum: galaxies
Active galactic nuclei (AGNs) are the centres of a spe-cial class of galaxies that consist of actively accreting cen-tral supermassive black hole (SBH). High-speed collimatedoutflows are present in a small fraction of AGNs. AGNsshow a dichotomy in their radio power - these are re-ferred to as radio-loud (RL) and radio-quiet (RQ) AGNs(Strittmatter et al. 1980; Miller et al. 1993). Historically,AGNs with higher radio-to-optical flux R ≥ where R ≡ S GHz / S B − band are termed as RL AGNs and RQ otherwise(Kellermann et al. 1989). This distinction however, is sug-gested to be valid only for broad-lined, unobscured AGNssince AGN luminosity estimates may be affected by dust,and therefore Padovani (2017) suggested a nomenclaturebased on a fundamental physical difference, namely the pres-ence or absence of relativistic jets.Based on the radio morphology and power, extendedRL AGNs are classified into FR I and FR II (Fanaroff & (cid:63) E-mail: [email protected] † Work towards this project was performed at this institute.
Riley 1974). FR II sources have radio structures consist-ing of a core, collimated jets, lobes, and hotspots located atthe edges of these lobes. FR I s show extended plume-likestructures and tails with no distinct collimated jets or ter-minal hotspots (more detailed differences in Bridle & Perley1984). Based on the optical spectra RL AGNs are classi-fied into Type 1 (have broad and narrow emission lines) andType 2 (have only narrow emission lines). Type 2 AGNscomprise narrow-line radio galaxies (NLRG); Type 1 AGNscomprise broad-line radio galaxies (BLRG) at low luminosityand radio-loud quasars at high luminosity. The latter are fur-ther divided into flat spectrum radio-loud quasars (FSRQ)( α > . ) and steep spectrum radio-loud quasars (SSRQ)( α < . ) (see Urry & Padovani (1995); Tadhunter (2008);Netzer (2015); Tadhunter (2016) for a review). SSRQs tendto have lobe-dominated radio structure while FSRQs oftenhave core-dominated structure. Although we see differenttypes of AGN, it is hypothesised that these are intrinsicallysimilar objects but appear different depending on their ori-entation with respect to the line of sight. This has cometo be known as orientation-based AGN Unification Scheme(Antonucci 1993; Urry & Padovani 1995). © a r X i v : . [ a s t r o - ph . GA ] D ec Vaddi et al.
There is compelling evidence in the literature that isconsistent with the predictions of the unification scheme.Some of the evidences include detection of broad emissionlines in the polarized intensity spectra of Type 2 AGNs in-dicating a hidden broad line region obscured from directview (Antonucci 1984; Antonucci & Miller 1985; Tran et al.1995; Young et al. 1996; Ogle et al. 1997; Cohen et al.1999), detection of ionisation cones of the narrow line re-gion in optical images of Type 2 AGNs (Pogge 1988; Wil-son et al. 1993; Jackson et al. 1998), detection of excessnear-IR emission and polarized light perpendicular to thejet axis (Antonucci & Barvainis 1990), apparently super-luminal velocities in VLBI observations of compact radiosources (Cohen et al. 1977; Kellermann et al. 2007), detec-tion of extended halos around compact sources representingradio lobes viewed face-on (Browne et al. 1982; Antonucci &Ulvestad 1985), and observed anisotropy in the radio struc-ture (Laing et al. 1983; Barthel 1989; Smith & Heckman1990; Mullin et al. 2008). According to unification, FSRQform the beamed counterparts of FR II radio galaxies, andSSRQ (and BLRG) are oriented at angles that are in be-tween NLRG and FSRQ.This paper is the first in a series of papers in our studyof FR II radio-loud AGNs. Here we present the results fromour VLA study of 11 SSRQ that show classical double lobesat GHz frequencies. We have analysed the properties of thesequasars along with a sample of 13 FR II radio galaxies fromKharb et al. (2008). Interestingly, several of these quasarsand radio galaxies reveal extended, winged or asymmetricradio morphologies in the TIFR GMRT Sky Survey (TGSS)at 150 MHz. This is in contrast to the symmetric structuresdetected with the VLA at GHz frequencies.Throughout this paper, we assume a cosmology withH = 73 km s − Mpc − , Ω mat = 0.27, Ω vac = 0.73. Spectralindex, α , is defined such that flux density at frequency ν is S ν ∝ ν α . The 11 quasars in this study were selected to augment thesample of 55 radio sources with spin determinations de-scribed by Daly (2011). The parent population is power-ful FR II sources that are identified as quasars, where theoriginal published optical classification was used to identifythem as quasars, and this identification was confirmed bythe follow-up observations of McLure et al. (2006); these11 sources were identified based on the following criterion:(i) the quasar has a black hole mass determination (fromMcLure et al. (2006)); (ii) at least one side of the sourcedoes not exhibit a radio jet, so that a spectral ageing anal-ysis can be carried out on the non-jetted side; (iii) the non-jetted lobe(s) have angular sizes large enough to allow thespectra of the lobe to be measured at several locations. These 11 quasars were observed with the VLA at 1.4 GHz(L-band) and 5 GHz (C-band) with A and CnB-array config-urations, respectively (Project ID: 10C-178) for a total of 22 hours. 5 GHz observations were not scheduled for 3C 336 .Hence, we used archival VLA data for this source. Also,due to the noisy 1.4 GHz data on 3C 351, we chose archivaldata for this source as well. For 3C 208 at 5 GHz, sincethere were issues with amplitude calibration using 3C 48,the source flux was rescaled using the aips task RESCALE and the core flux density from Yuan & Wang (2012).The data in the SDMset format were pre-calibrated us-ing the casa calibration pipeline . The final imaging andself-calibration were done in the Astronomical Image Pro-cessing System ( aips ), by iteratively running the tasks IMAGR and
CALIB . The 1.4 − COMB after first creating L and C-band imageswith identical circular beams (at the poorer L-band resolu-tion), which were then positionally aligned using the
AIPS task
OGEOM . We used 3 σ r.m.s. noise of the respective L andC-band images as the clipping level input to COMB . Final fluxdensity and spectral index values were estimated using the
AIPS verbs
TVWIN and
IMSTAT , as well as the Gaussian-fittingtask
JMFIT . All extents were measured in
AIPS using
IMDIST .The basic properties and derived parameters are listed inTables 1 through 2.
In order to put the results from the 11 quasars in perspec-tive, we have analysed their results along with the sampleof 13 FR II narrow-line radio galaxies that were studied inKharb et al. (2008); the radio galaxy study had been carriedout at similar frequencies and resolutions as the 11 quasarspresented in this paper. Some properties of the radio galaxieshave been re-estimated (like R c , 1.4 GHz flux density fromNVSS) to compare them with the current quasar sample,and are tabulated in Table 6. We note that the combinedquasar and radio galaxy sample studied here is statisticallysmall and eclectic. Some statistical tests, therefore, sufferfrom small number statistics. The spectral index is estimated from the spectral index im-ages. The spectral index of the core is obtained by placing abox around the core using
TVWIN and estimating the meanusing
IMSTAT . The hotspot spectral index is measured at theimage position corresponding to the peak intensity in 1.4 and5 GHz image, where the peak intensity is determined using
TVMAXFIT .The arm-length ratio is defined as the ratio of the longercore-hotspot distance to the shorter core-hotspot distance.The distance is measured from the peak flux position at thecore to the peak flux position at the hotspot. The peak fluxdensity was estimated using the procedure
TVMAXFIT . The One of the observations (for 3C432) had technical issues andwas therefore re-observed. However, due to confusion in the se-quencing of observations, we missed obtaining data for 3C336.This was realised late and could not be rectified Project ID: AB0454; C-band B-array configuration Project ID: AD0429; L-band A-array configuration casa release 4.5.3 with pipeline release 4.6.0MNRAS000
TVMAXFIT . The One of the observations (for 3C432) had technical issues andwas therefore re-observed. However, due to confusion in the se-quencing of observations, we missed obtaining data for 3C336.This was realised late and could not be rectified Project ID: AB0454; C-band B-array configuration Project ID: AD0429; L-band A-array configuration casa release 4.5.3 with pipeline release 4.6.0MNRAS000 , 1–15 (2018) adio-loud AGN properties SLFIT to a one-dimensional slice across the lobe. Theslice is chosen by-eye to be perpendicular to and at halfthe distance between the core and hotspot. To account forthe finite resolution of the observing beam, we subtractthe beam in quadrature from the measured Gaussian fit: Θ decon = Θ G − Θ b where Θ G is the measured FWHM fromthe Gaussian fit, and Θ b is the average FWHM of the ob-serving beam ( (cid:112) ( FW HM major axis ∗ FW HM minor axis ) ), and Θ decon is the FWHM of the Gaussian fit deconvolved withthe beam. The lobe width is then taken as 2/ √ of the de-convolved Gaussian fit (Leahy et al. 1989). Correction forthe radio galaxies is negligible since its lobe widths are largecompared to the beam size (Daly et al. 2010).The largest linear size (LLS) is defined as the distancebetween two hotspots (definition consistent with Kharbet al. (2008)). The distance is estimated by first obtainingthe position of the hotspot peak intensity with TVMAXFIT and then measuring the distance between the hotspot peakintensity using
IMDIST . The 5 GHz maps were used in theestimates.The misalignment angle is the difference between theposition angles of the core-hotspot segments on either sideof the core. When there is more than one hotspot on oneside of the core, the other side is used to define the core-hotspot line segment and misalignment angles to each of thetwo hotspots on the opposite side are obtained. Figure A16in the appendix identifies different components that wereestimated using AIPS task
TVDIST . Uncertainties in the flux measurements were estimated bycombining all errors in quadrature. The errors consideredare the calibration errors, rms, and random errors ( σ = (cid:113) σ cal + σ rms + σ random ). We assumed that the calibrationerrors are 10 per cent of the measured flux densities. RMSis the error reported in the tasks ( JMFIT , IMSTAT ) used forflux measurement. Random error of 5 per cent accounts forthe different values we get when taking repeated measure-ments; an average of five measurements were obtained fora few sources and the per cent standard deviation from theaverage values was noted. This gives the uncertainties of theorder of few mJy beam − .The error in the spectral index is estimated by addingin quadrature the noise map produced in the COMB task anda 5 per cent random error for α HS , 15 per cent random errorfor the α core , and a 10 per cent random error for the α lobe .The uncertainty on the measurement of the lobe widthis obtained by adding in quadrature the 5 per cent randomerror on the measured width and the error in the Gaus- sian fit to the slice. The uncertainty of the deconvolvedlobe width is obtained using the expression δ Θ decon = ( Θ decon / Θ G ) δ Θ G + ( Θ decon / Θ b ) δ Θ b where δ Θ G is the un-certainty of the lobe width and δ Θ b is the uncertainty ofthe observing beam. Lobes whose widths are similar to thebeam have larger uncertainty.The uncertainties on angular size, arm-length ratios areestimated by repeated measurements (five times) on fewsources and taking the standard deviation of these measure-ments. The ratios are calculated using the usual error prop-agation. These uncertainties are between 5 −
20 per cent.The LLS is estimated from peak hotspot-to-hotspot dis-tance which need not be the true LLS and true LLS is usu-ally larger than the hotspot-hotspot distance. To quote theuncertainty in the LLS due to this, we estimate an averageof several lobe edge-to-edge distance measurement obtainedusing
TVDIST and the deviation of this estimate with thehotspot-hotspot distance is taken as the uncertainty. Theuncertainty of the misalignment angle is estimated based onthe uncertainties of the hotspot and core positions.
MNRAS , 1–15 (2018)
Vaddi et al. T a b l e . B a s i c p r o p e r t i e s o f t h e r a d i o - l o ud q u a s a r s a m p l e N a m e R A D E C z l og M B H S . t o t a l S t o t a l S t o t a l LL S l og R c A r m - l e n g t h M i s a li g n m e n t Q j e t J J M (cid:12) ( J y )( J y )( J y )( k p c ) R a t i o A n g l e ( d e g )( e r g s − ) ( )( )( )( )( )( )( )( )( )( )( )( )( ) C : : . + : : . . . ± . . ± . . ± . . - . ± . . . , . . ± .
33 3 C : : . + : : . . . ± . . ± . . ± . . - . ± . . . . ± .
05 3 C : : . + : : . . . ± . . ± . . ± . . - . ± . . . . ± .
04 3 C : : . + : : . . . ± . . ± . . ± . . - . ± . . . , . . ± .
24 3 C : : . + : : . . . ± . . ± . . ± . . - . ± . . , . . ± .
30 3 C : : . + : : . . . ± . . ± . . ± . . - . ± . . . ± .
23 3 C . : : . + : : . . . ± . . ± . . ± . . † - . ± . . . . ± .
05 3 C : : . + : : . . . ± . . ± . . ± . . - . ± . . . . ± .
12 3 C : : . + : : . . . ± . . ± . . ± . . - . ± . . . . ± .
21 3 C : : . + : : . . . ± . . ± . . ± . . - . ± . . . , . ± .
06 3 C : : . + : : . . . ± . . ± . . ± . . - . ± . . . . ± . C o l. ( - ) g i v e t h e s o u r ce n a m e a ndp o s i t i o n . C o l ( ) - R e d s h i f t f r o m N E D . C o l. ( ) - L oga r i t h m o f b l a c k h o l e m a ss f r o m M c L u r ee t a l. ( ) . C o l ( - ) - T o t a l flu x d e n s i t y a t . G H z , G H z , a nd M H z r e s p ec t i v e l y i n J y B e a m − . C o l ( ) - L a r g e s t li n e a r s i ze w h i c h i s t h e d i s t a n ce b e t w ee n t h e t w o h o t s p o t s , m e a s u r e du s i n g IMDIST o n G H z i m ag e s . C o l ( ) - L oga r i t h m o f t h e r a d i o c o r e p r o m i n e n ce . C o l ( ) - A r m - l e n g t h r a t i o w h i c h i s t h e d i s t a n ce b e t w ee n t h ec o r e t o t h e h o t s p o t , m e a s u r e du s i n g TVDIST o n G H z i m ag e s . C o l ( ) - M i s a li g n m e n t a n g l e i nd e g r ee . W h e r e m u l t i p l e h o t s p o t s a r e p r e s e n t , a n g l e t o e a c h o f t h e h o t s p o t s i s m e n t i o n e d s e p a r a t e db y a c o mm a . A n a n g l e o f ze r o i nd i c a t e s t h a tt h e h o t s p o t s a nd c o r e a llli e o n a s i n g l e li n e . G H z i m ag e s a r e u s e d t og e tt h e a n g l e s . T h e un ce r t a i n t y o n t h e m i s a li g n m e n t a n g l e s i s a b o u t a t e n t h o f a d e g r ee . C o l ( ) - J e t k i n e t i c p o w e r . † S i n ce t h i ss o u r ce h a s o n l y o n e h o t s p o t , t h e LL S f o r t h i ss o u r ce i s m e a s u r e du s i n g TVDIST a nd c h oo s i n g t h ee ndp o i n t s b y e y e . MNRAS000
06 3 C : : . + : : . . . ± . . ± . . ± . . - . ± . . . . ± . C o l. ( - ) g i v e t h e s o u r ce n a m e a ndp o s i t i o n . C o l ( ) - R e d s h i f t f r o m N E D . C o l. ( ) - L oga r i t h m o f b l a c k h o l e m a ss f r o m M c L u r ee t a l. ( ) . C o l ( - ) - T o t a l flu x d e n s i t y a t . G H z , G H z , a nd M H z r e s p ec t i v e l y i n J y B e a m − . C o l ( ) - L a r g e s t li n e a r s i ze w h i c h i s t h e d i s t a n ce b e t w ee n t h e t w o h o t s p o t s , m e a s u r e du s i n g IMDIST o n G H z i m ag e s . C o l ( ) - L oga r i t h m o f t h e r a d i o c o r e p r o m i n e n ce . C o l ( ) - A r m - l e n g t h r a t i o w h i c h i s t h e d i s t a n ce b e t w ee n t h ec o r e t o t h e h o t s p o t , m e a s u r e du s i n g TVDIST o n G H z i m ag e s . C o l ( ) - M i s a li g n m e n t a n g l e i nd e g r ee . W h e r e m u l t i p l e h o t s p o t s a r e p r e s e n t , a n g l e t o e a c h o f t h e h o t s p o t s i s m e n t i o n e d s e p a r a t e db y a c o mm a . A n a n g l e o f ze r o i nd i c a t e s t h a tt h e h o t s p o t s a nd c o r e a llli e o n a s i n g l e li n e . G H z i m ag e s a r e u s e d t og e tt h e a n g l e s . T h e un ce r t a i n t y o n t h e m i s a li g n m e n t a n g l e s i s a b o u t a t e n t h o f a d e g r ee . C o l ( ) - J e t k i n e t i c p o w e r . † S i n ce t h i ss o u r ce h a s o n l y o n e h o t s p o t , t h e LL S f o r t h i ss o u r ce i s m e a s u r e du s i n g TVDIST a nd c h oo s i n g t h ee ndp o i n t s b y e y e . MNRAS000 , 1–15 (2018) adio-loud AGN properties Table 2.
Properties of hotspotsName Side S peak α . Θ (mJy beam − ) (arcsec)(1) (2) (3) (4) (5)3C14 N1 81.90 -0.98 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± TVMAXFIT . The errors are of the orderof a few per cent ( < IMVAL on the spectral images. Col(5): Dis-tance from core to the hotspot in arcsec obtained using
TVDIST on 5 GHz intensity images. The uncertaintiesare (cid:28)
In our quasar sample, ∼
70 per cent of sources show one-sided radio jets, while the remaining 30 per cent sources donot clearly show a jet. In all but one source (3C 249.1 whichshows a hybrid FR I/FR II morphology) two-sided hotspotslocated at the end of the radio lobe (i.e. FR II type mor-phology) are seen. For two sources (3C 109, 3C 336), jet-likefeatures are seen in higher resolution images in the litera-ture (Giovannini et al. 1987). In the radio galaxy sample ofKharb et al. (2008), 30 per cent of the sources show one-sided jet-like features (see Table 6 of (Kharb et al. 2008)).The 5 GHz images of the quasars show the presence of mul-tiple hotspots in some sources. The southern lobe of 3C 263and northern lobe of 3C 351 have off-axis extended emissioni.e., the emission is extended on one side of the jet axis. Seethe Appendix for the contour images of the quasars at 1.4GHz and 5 GHz.We studied various properties of the cores, lobes, andhotspots of quasars and radio galaxies. We have quantified
Table 3.
Properties of the coreName S . tot al S tot al α . (mJy) (mJy)(1) (2) (3) (4)3C14 24.3 ± ± − ± ± ± ± ± ± − ± ± ± − ± ± ± − ± ± ± − ± ± ± ± ± ± ± ± ± − ± ± ± − ± ± ± − ± TVWIN + IMSTAT on thespectral index image. the observed trends with appropriate statistical tests. Thesignificance of the relationship between properties is assessedusing the non-parametric Spearman and Kendall rank tests.Both tests produced nearly identical results and so we havequoted here the results from the Spearman rank test. Thep-value is a measure of the significance of the null hypothe-sis that two variables are uncorrelated. To determine if twodistributions are the same, we have used the two sampleKolmogorov − Smirnov (KS) test. The null hypothesis to betested in the KS test assumes that both the samples aredrawn from the same population. A p-value of 0.05 and loweris considered to be significant to reject the null hypothesis. − p gives the per cent confidence level at which the nullhypothesis is rejected. Arm-length ratio is an asymmetry parameter and is definedas the ratio of the longer to the shorter arm-length, wherearm-length is the core-to-hotspot distance. For quasars thatshow two hotspots on one side, we measured the arm-lengthratio using both the hotspots. Figure 1 shows the arm-lengthratio with respect to the redshift for the quasars and FR II radio galaxies. The arm-length ratio distribution for quasarsshows that there are both symmetric and asymmetric struc-tures. A larger fraction of the sources in the radio galaxysample are symmetric sources than in the quasar sample.Combining both the samples, we find that ∼ per centof the sources have arm-length ratios lower than the com-bined average arm-length ratios (=1.49) suggesting symmet-ric structures. The KS test indicates that there is no substan-tial difference in arm-length ratios between the two types ofAGNs and they may be drawn from the same population( p = . ). The arm-length ratios appear to be uncorrelatedwith redshift ( p = . ) (see also Kharb et al. 2008) suggest-ing that symmetric and asymmetric structures prevail at allredshifts.The (brighter) jet side is observed in seven out of 10quasars (3C 249.1 is excluded because of its hybrid struc-ture). The remaining three sources have ambiguity in the MNRAS , 1–15 (2018)
Vaddi et al.
Table 4.
Properties of the radio lobesName α . Θ width B min P min Axial RatioN,S N,S µ G 10 − dyne cm − N,S(1) (2) (3) (4) (5) (6)3C14 -1.39 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ‡ -1.42 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ‡ -0.75 ± ± ± ± ± ± ± ± ‡ ..., -1.03 ± ± ± ± ‡ -1.06 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ‡ The estimates are for the eastern and western lobe respectively.
Table 5.
Quasar properties - Jet sidednessName Jet Side Bright HS Ratio of HS ShorterSide peak flux Side(1) (2) (3) (4) (5)3C14 S S 1.36 N3C47 S S 7.91 N3C109 S † S 6.61 S3C204 W W 1.34 W3C205 S S 2.02 N3C208 E(?) E 6.62 W3C249.1 - - - N3C263 E E 17.70 E3C336 S † N 1.68 S3C351 N(?) N 98.03 N3C432 S(?) N 1.19 N † Not observed in the current data, but observed in other high-resolution VLA archival images (Giovannini et al. 1987). jet feature (see Table 5). Of these seven jetted quasars, fourquasars have their bright hotspot side on the same side asthe shorter lobe. If we also include the “tentative” brighterjet sides from our images, 50 per cent (i.e. five out of tenquasars) have their brighter hotspot side on the same side asthe shorter lobe. Simple relativistic beaming theories, how-ever expect the longer arm to be the brighter arm (Ryle &Longair 1967). Our results suggest that the brighter hotspotside is equally likely to be either on the shorter arm or on thelonger arm . This asymmetry in brightness and morphologymay be a result of asymmetric environments, rather thanorientation-dependent Doppler effects. The environmentaleffects that constrained the jet from expanding might alsomake the hotspot appear brighter (Saikia et al. 1995; Gopal-Krishna & Wiita 2004).
Figure 1.
Arm-length ratio ( Q ) which is the ratio of longer tothe shorter core-hotspot distance vs redshift. Quasars from oursample are indicated by stars and FR II radio galaxies by di-amond symbols. The radio galaxy sample is from Kharb et al.(2008). Q for sources with multiple hotspots is also plotted. Thehistogram of the Q for quasar and radio galaxies is also shown tothe right of the y-axis. KS test indicates no substantial differencein arm-length ratio between quasars and radio galaxies. The correlation of spectral index with redshift or radio lu-minosity is well established (Tielens et al. 1979; Blumen-thal & Miley 1979; Wellman et al. 1997; Dennett-Thorpeet al. 1999; Ishwara-Chandra & Saikia 2000; Kharb et al.2008). The tendency for the integrated spectral index tosteepen with redshift is conventionally attributed to a “k-correction”. At higher redshift, the spectrum gets shifted tolower frequencies bringing the steep part of the spectrumcloser to the observed frequency (Bolton 1966; Blumenthal
MNRAS000
MNRAS000 , 1–15 (2018) adio-loud AGN properties Table 6.
Newly estimated properties of the Kharb et al. (2008) radio galaxy sample.Name z Arm-length log R c LLS S . tot al S tot al P min (N,S) Q jet Ratio (kpc) Jy Jy 10 − dyne cm − erg s − (1) (2) (3) (4) (5) (6) (7) (8) (9)3C6.1 0.804 1.15 -2.90 241 3.8 17.86 15.70, 7.51 11.483C13 1.351 1.36 -4.19 276 1.92 17.11 ... 28.463C34 0.69 1.14 -3.47 346 1.78 16.03 7.46, 2.81 6.743C41 0.794 1.11 -3.40 204 3.51 12.16 15.10, 9.27 8.213C44 0.66 1.63 -3.94 502 1.37 10.98 4.24, 5.27 4.823C54 0.8274 1.07 -3.96 458 2.03 11.48 3.87, 3.16 8.213C114 0.815 1.09 -2.53 451 1.01 11.56 5.36, 2.22 6.353C142.1 0.4061 1.87 -3.57 299 3.10 28.48 4.88, 1.42 3.003C169.1 0.633 1.44 -3.40 367 1.297 8.00 2.18, 1.21 3.123C172 0.5191 1.13 -4.18 676 2.84 18.85 1.45, 0.65 4.543C441 0.707 2.45 -4.32 299 2.53 † † Taken from Condon et al. (1998)Col.(1)- Name of the source. Col(2)- Redshift from NED. Col(3)- Arm-length ratio. Col(4)- K-corrected radiocore prominence parameter S GHz ( core )/ S . GHz ( lobe ) . The 1.4 GHz flux density is taken from White &Becker (1992) and the 5 GHz flux density is estimated from 8 GHz assuming α = . . Col.(5)- Projectedlargest linear separation. Col.(6)- Total flux density of the source at 1.4 GHz taken from White & Becker(1992) in units of (Jy). Col(7)- 150 MHz flux density from TGSS-GMRT in units of (Jy). Col(8)- Minimuminternal pressure in the lobe obtained using Equation 3. Col(9)- Jet kinetic power estimated as described inSection 4.1. Figure 2.
Spectral index of the hotspot between 1.4 and 5 GHz inthe observed frame vs. redshift. Multiple hotspots are also plotted. & Miley 1979; Laing & Peacock 1980; Gopal-Krishna 1988).This interpretation for the z − α is based on the observationthat the SEDs of radio galaxies steepen at high frequencies.However, the k-correction mechanism has been disputed ow-ing to the remarkable linearity and the lack of evidence fora spectral curvature in wide band radio spectrum of highredshift radio galaxies (Chambers et al. 1990; Klamer et al.2006, 2007). Gopal-Krishna et al. (2012) however argue thatsince the steepness of the spectral index anti-correlates withthe spectral curvature (Mangalam & Gopal-Krishna 1995),the spectral steepening becomes negligible for sources with steep spectra ( α GHz ≤ − . , also reflected in Klamer et al.(2006)). k-correction being insufficient to explain the ob-served z − α relation, alternative effects have been proposed.A density-dependent effect in which the jets are workingagainst a denser environment at higher redshift can resultin greater synchrotron losses at higher frequencies (Athreya& Kapahi 1998; Klamer et al. 2006, 2007; Ker et al. 2012).It has also been suggested that for high- z sources, therecan be an enhanced spectral aging due to increased inverse-Compton losses of the relativistic electrons against the cos-mic microwave background (CMB), since the CMB energydensity increases as ( + z ) (Gopal-Krishna et al. 1989; Kro-lik & Chen 1991; Morabito & Harwood 2018). High powersources have higher magnetic fields in the hotspots thanin the less powerful sources. This can result in higher syn-chrotron losses, thus steepening the spectra (Meisenheimeret al. 1989; Gopal-Krishna & Wiita 1990; Blundell et al.1999). Yet another possibility is that the z − α is an indirectmanifestation of an intrinsic correlation between the lumi-nosity and spectral index which when coupled to Malmquistbias translates to the z − α correlation (Laing & Peacock1980; Chambers et al. 1990; Blundell et al. 1999). This, how-ever, seems to be not the case in our sample as we discussahead.Unlike previous studies where integrated spectral in-dex was used, we examine the hotspot spectral index. Sincehotspots are located far away from the galaxy, their studycan provide physical insight into the environments of theradio source, and thereby test the effect of ambient den-sity on source properties. Figure 2 shows the variation ofspectral index of the hotspot with redshift in quasars andradio galaxies. We see a spectral steepening in the quasarand radio galaxies with increasing redshift consistent withearlier findings. The Spearman rank test indicates a statis-tically significant correlation with the correlation coefficient MNRAS , 1–15 (2018)
Vaddi et al.
Figure 3.
Total 1.4 GHz radio power versus . − GHz hotspotspectral indices. The dashed line is the best-fit line with a slopeof − . . of -0.42 at a significance level greater than 99.99 per cent. Astronger dependence of spectral index on the luminosity isalso observed in our sample and is shown in Figure 3. Highradio power sources have steeper spectral indices and fol-low the relation log P . GHz ∝ α . HS at a significance level of99.99 per cent. However, this P − α correlation turns out tobe not significant when the partial correlation coefficient( ρ = . , p − v alue = . ) was measured considering the effectof redshift. The observed P − α correlation is likely due tothe strong correlations between z − α (correlation coefficient= 0.7) and z − P (correlation coefficient = 0.5) at > ± α HS =0.87 ± α HS distri-bution of quasars and radio galaxies are the same ( p − v alue = . ). In ∼
80 per cent of the quasars, the hotspot with theflatter spectral index is also the one with higher peak powerat 1.4 GHz; this fraction is ∼ per cent in the case ofthe radio galaxies. In 70 per cent of the quasars (i.e., 7 outof 10 quasars excluding the hybrid quasar 3C349.1) whereone-sided jets are observed, the jet-sided lobe has a signif-icantly brighter hotspot than the non-jetted side in 6 outthese 7 quasars. On the other hand, in the radio galaxies,only ∼ per cent of them show one-sided jet-like featuresand again the jet side points to the brightest hotspot. Theseobservations (i.e. a similar distribution of hotspot spectralindices, jet-side pointing to the brightest hotspot in quasarsand radio galaxies) imply that the brighter hotspot is likely,not due to Doppler boosting. The hotspots of quasars andradio galaxies considered here, do not have a bulk motionthat is relativistic. The hotspot power is between 10-20 per The ppcor package in R programming language has been usedin partial correlation statistics.
Figure 4.
Logarithm of the radio core prominence (ratio of coreflux to the lobe flux density) R c vs. redshift for quasars and radiogalaxies. Quasars have larger R c than radio galaxies consistentwith the orientation-based unification scheme. cent of the total power at 1.4 GHz. The hotspot power corre-lates and increases with the total power in quasars and radiogalaxies. The statistical significance of the α − z relation forthe hotspot ( p − v alue = E − ) is stronger than the α − z relation for core spectral index ( p − v alue = . ). The radio core prominence R c , is the ratio of the (beamed)radio core flux density to the (unbeamed) lobe flux density(Orr & Browne 1982; Zirbel & Baum 1995). It is a statisticalindicator of beaming and thereby orientation (Padovani &Urry 1992; Kharb & Shastri 2004); larger R c values corre-spond to smaller viewing angles. The k-corrected radio coreprominence is calculated using R c = S core s total − s core ( + z ) α core − α ext (1)where S core is the core flux density at 5 GHz and S total isthe total radio emission at 1.4 GHz. Figure 4 shows log R c with respect to redshift for quasars and radio galaxies. Forthe radio galaxies, the 5 GHz core flux density is estimatedfrom the 8 GHz measurements assuming a spectral indexof − . (average core spectral index in Kharb et al. (2008)sample), and the total flux density is taken at 1.4 GHz. It canbe seen that quasars have larger R c values when comparedto the radio galaxies. This is consistent with the orientation-based unification of quasars with radio galaxies; quasars areoriented at relatively smaller angles compared to the radiogalaxies.The median log R c for the quasars and radio galaxies is − . and − . , respectively, consistent with the RL unifi-cation scheme. For quasars, − . < lo g R c < − . , and for theradio galaxies, − . < lo g R c < − . . Kharb & Shastri (2004)(see their Figure 3) have estimated log R c values for a large MNRAS000
Logarithm of the radio core prominence (ratio of coreflux to the lobe flux density) R c vs. redshift for quasars and radiogalaxies. Quasars have larger R c than radio galaxies consistentwith the orientation-based unification scheme. cent of the total power at 1.4 GHz. The hotspot power corre-lates and increases with the total power in quasars and radiogalaxies. The statistical significance of the α − z relation forthe hotspot ( p − v alue = E − ) is stronger than the α − z relation for core spectral index ( p − v alue = . ). The radio core prominence R c , is the ratio of the (beamed)radio core flux density to the (unbeamed) lobe flux density(Orr & Browne 1982; Zirbel & Baum 1995). It is a statisticalindicator of beaming and thereby orientation (Padovani &Urry 1992; Kharb & Shastri 2004); larger R c values corre-spond to smaller viewing angles. The k-corrected radio coreprominence is calculated using R c = S core s total − s core ( + z ) α core − α ext (1)where S core is the core flux density at 5 GHz and S total isthe total radio emission at 1.4 GHz. Figure 4 shows log R c with respect to redshift for quasars and radio galaxies. Forthe radio galaxies, the 5 GHz core flux density is estimatedfrom the 8 GHz measurements assuming a spectral indexof − . (average core spectral index in Kharb et al. (2008)sample), and the total flux density is taken at 1.4 GHz. It canbe seen that quasars have larger R c values when comparedto the radio galaxies. This is consistent with the orientation-based unification of quasars with radio galaxies; quasars areoriented at relatively smaller angles compared to the radiogalaxies.The median log R c for the quasars and radio galaxies is − . and − . , respectively, consistent with the RL unifi-cation scheme. For quasars, − . < lo g R c < − . , and for theradio galaxies, − . < lo g R c < − . . Kharb & Shastri (2004)(see their Figure 3) have estimated log R c values for a large MNRAS000 , 1–15 (2018) adio-loud AGN properties Figure 5.
Arm-length ratio vs. logarithm of radio core promi-nence R c . Arm-length ratio from multiple hotspots is plotted.The KS test indicates a weak correlation suggesting that arm-length ratio is not strongly affected by projection. number of AGN ranging from the plane-of-sky narrow-lineradio galaxies to nearly pole-on blazars. Using the relationsbetween R c , jet speed and orientation angles presented inAppendix A of Kharb & Shastri (2004), a minimum log R c ( R minc ) value of − . (see Kharb & Shastri 2004), bulk jetLorentz factor of 10 (Kapahi & Saikia 1982), we deduce thatthe radio galaxies considered here lie at orientation anglesbetween 34 ± ◦ and 74 ± ◦ , while the quasars lie at orien-tation angles between 19 ± ◦ and 38 ± ◦ . 85% of the radiogalaxies have orientation angles greater than 45 ◦ . Barthel(1989) suggested that the average θ = ◦ for quasars, and θ = ◦ for radio galaxies. These are, of course, crude rep-resentative estimates because R c is a statistical indicator oforientation and cannot be used to derive orientation anglesfor individual sources. It is important to note that whilequasars are at relatively small angles compared to the radiogalaxies, as indicated by their relative R c values, the over-lapping spread in R c values indicates that the quasars arenot oriented at small enough angles ( (cid:46) ◦ ) to qualify asblazars.Figure 5 shows log R c plotted against the arm-lengthratio, Q . We find a weak anti-correlation in the combinedsample with a correlation coefficient of − . at a signifi-cance of 95 per cent ( p − v alue = . ). This marginal rela-tion, which also has a large scatter, might suggest that thearm-length ratio is not strongly affected by projection butis an intrinsic asymmetry. We suggest that this weak rela-tion is apparent and may be a result of small sample size,and R c and arm-length ratio may not be correlated. This issupported by the orientation independent statistics for thearm-length ratio in this sample (see Section 3.1). The jet orientation angle with respect to our line of sightinfluences the observed size of a radio source such that,
LLS proj = LLS true × sin θ . Sources with jets oriented closeto the line of sight will have smaller apparent sizes thansources oriented close to the plane of the sky for sourceswith similar intrinsic sizes. In the present work, the angu-lar sizes of the radio galaxies appear to be larger than thequasars. The average projected LLS of radio galaxies is 1.6times larger than those of quasars. Following the Unificationscheme, if we assume that quasars and radio galaxies arethe same objects and have similar distributions in true lin-ear sizes, then LLS proj RG / LLS proj
RLQ = sin θ RG / sin θ RLQ .Using an average θ RLQ = ◦ and θ RG = ◦ for quasarsand radio galaxies, respectively, as indicated by their R c values, gives sin θ RG / sin θ RLQ ∼ . , fully consistent with LLS proj RG / LLS proj
RLQ = . ± . .The LLS ratio can be used to estimate the angle divid-ing quasars and radio galaxies ( θ ) using the foreshorteningcalculations given by: LLS proj RG / LLS proj
RLQ = sin ( cos − ( . cos θ )) sin ( cos − ( . ( + cos θ ))) (2)For an LLS ratio of 1.6, this gives θ = ◦ . These are inagreement with the torus opening angle estimated from theprojected linear sizes of the radio sources. The average pro-jected LLS of quasars is 240 ±
28 kpc, while the radio galaxieshave an average projected LLS of 390 ± kpc. Using Eq (7)of Arshakian (2005), the torus opening angle for the quasarsin our sample is 57 ◦ ± ◦ . This yields an average orientationangle of 74 ± ◦ for radio galaxies and 39 ± ◦ for quasars. En-couragingly, this matches with the upper end of the averageorientation angles estimated using R c .Wan & Daly (1998) and Barthel (1989) reported LLS proj RG / LLS proj
RLQ ratios of 1.25 and 1.8 respectivelyfor 3C sources. Recently, using galaxies from the Bootes sur-vey observed using LOFAR, Morabito et al. (2017) found theratio of the average projected linear size of radio galaxies tothe quasars to be . ± . , and . ± . after correctingfor redshift evolution. The LLS estimates in this study areconsistent with the LLS estimates in the literature, and min-imal difference in the LLS ratio is likely due to the way thesamples are selected. The selection criteria in our study (andWan & Daly (1998)) favours quasars that have well-resolvedextended lobes which in turn correspond to quasars at rela-tively larger viewing angles to the line of sight.Overall, our results are consistent with the orientation-based unification scheme for quasars and radio galaxies.However, the range of orientation angles might not belarge enough to show clear correlations with respect toorientation-indicators like R c or misalignment angles. Wefind the inverse dependence of the angular size with the red-shift (widely known as the D-z relation) consistent with pre-vious studies (Miley 1968; Kapahi 1985; Blundell et al. 1999;Ker et al. 2012; Morabito et al. 2017).The dependence of the . − GHz spectral index of thehotspots on the projected core-hotspot distance is shown inFigure 6. We have plotted the spectral index of the hotspotsof both the lobes for each source. We find a correlation be-tween the spectral index and the projected core-hotspot dis-tance of the quasars at a 96 per cent significance level. This
MNRAS , 1–15 (2018) Vaddi et al.
Figure 6. . − GHz hotspot observed spectral index versusprojected core-hotspot distance. The α H S for multiple hotspotsin each lobe is plotted. apparent correlation, however, disappears once a partial cor-relation test is performed between α HS and the core-hotspotdistance neglecting the effects of redshift. This is indicativeof the evolution of spectral index and size with redshift. The axial ratios (lobe length-to-width ratio) for quasars arelower and statistically different from the radio galaxies inour study consistent with results obtained by Leahy et al.(1989). The intensity maps show more asymmetric and dis-torted lobes (such as extended diffuse emission to one sideof the jet) in quasars (e.g. 3C 14, 3C 204, 3C 249.1, 3C 263,3C 351) than in radio galaxies. The KS test suggests thatquasars and radio galaxies have different distributions at 96per cent significance ( p − v alue = . ). The median axialratios of quasars and radio galaxies are 3.0 and 4.2 respec-tively. The lobe widths of quasars are on an average only 1.3times fatter (i.e. larger lobe width) than the radio galaxiesand their median lobe widths are similar. Thus, lobe widthsof quasars studied here are rather insensitive to projectioneffects, consistent with Wan & Daly (1998) who showed thatprojection has little effect on lobe width except at very smallangles or for source widths measured close to the hotspot.This indicates that the ratio of lobe length to the widthi.e. axial ratio is likely governed by the projection of thelength. The average core-hotspot size ratio for similar lobewidths is 1.45. This likely suggests that the different axialratios can be accounted for by projection effects alone. Leahyet al. (1989) however find that the differences in axial ratioscannot be explained by projection effects alone. Larger lobestructure distortions seen in quasars (also seen in our quasarsample) can partly explain the low axial ratios. Such lobedistortions can, for example, arise due to variations in theoutflow angles, as discussed in Section 3.6. Any evolutionary Figure 7.
Axial ratio vs lobe spectral index shows significantmarginal anti-correlation ( ρ =-0.3). relationship between quasars and radio galaxies leading tosmaller linear sizes in quasars compared to radio galaxies,however, cannot be ruled out (e.g. Miley 1971; Wardle &Miley 1974).The axial ratios show a marginal correlation with thespectral index of the lobes (correlation coefficient = 0.3, p − v alue = . ) (see Figure 7) and an anti-correlationwith the lobe flux density ( ρ =-0.4, p − v alue = . ), both ata greater significance level i.e. fatter lobes are brighter andhave steeper spectra. This is believed to be due to confine-ment of the lobes which enhances the radio emission throughsynchrotron losses while reducing the adiabatic expansionlosses (e.g., Roland et al. 1985; Barthel & Arnaud 1996).We further compare the axial ratios against the structureasymmetry using the arm-length ratio in Figure 8. Individ-ually, the quasars and radio galaxies do not show any cor-relation, but the combined sample shows an anti-correlationat a significance level of 96 per cent ( p − v alue = . ). Thisanti-correlation implies that sources that are narrower i.e.with larger axial ratios are also more symmetric in terms oftheir lobe sizes (i.e., have arm-length ratios closer to unity).The shorter or fatter sources are the more asymmetric. Suchan anti-correlation was also observed in the combined radiogalaxy sample in Kharb et al. (2008), who found that theshorter lobe was fatter and had a steeper spectral index. Misalignment angle is the complement of the angle betweenthe lines joining the core to the two hotspots. This is anasymmetry parameter and is sensitive to the orientation ofthe source to the line of sight. An intrinsic jet axis mis-alignment can appear enhanced for small angles to the lineof sight, due to projection effects (Reynolds 1980; Con-way & Murphy 1993). However, the misalignment angle isalso found to be sensitive to environmental effects (Kharbet al. 2008). The misalignment angles for quasars are typi-
MNRAS000
MNRAS000 , 1–15 (2018) adio-loud AGN properties Figure 8.
Plot of the axial ratio vs arm-length ratio for thequasar and radio galaxy sample. cally greater than ◦ and the average misalignment angle forquasars is about 1.8 times greater than radio galaxies (SeeTable 1), however, we cannot reject the hypothesis thatboth the samples have similar distribution at 28 per cent( p − v alue = . ). Although differences in misalignment an-gles can become amplified due to a geometrical projectioneffect, the difference seen in our sample is marginal becauseamplification is likely to be significant only in sources thatare oriented close to the line of sight (Moore et al. 1981;Saikia et al. 1995).Figure 9 shows the misalignment angle versus the ra-dio core prominence, log R c . Statistical tests show no cor-relation for the quasars and radio galaxies taken together( p − v alue = . ) or separately for quasars ( p − v alue = . ).Kharb et al. (2008) had found a weak positive correla-tion for a large sample of radio galaxies. The radio coreprominence − misalignment angle correlation has also beenobserved in quasars by Kapahi & Saikia (1982); Hough& Readhead (1989). This implies that either R c or themisalignment angles or both are not uniquely orientation-dependent. Environmental factors may be instrumental inthis lack of a correlation. R c could be influenced by envi-ronmental asymmetries on parsec-scales, while misalignmentangles could be influenced by environmental asymmetries onparsec as well as kpc-scales (Kharb et al. 2010).Figure 10 shows the misalignment angles against axialratios (longer and narrower sources have larger axial ratios).Quasars show smaller axial ratios and larger misalignmentangles. This is consistent with the idea that environmentplays an important role in tracing the jet and lobe structure.When the jet and the counter-jet plough into a medium withdifferent densities, it will cause the jet to bend leading togreater misalignment angles (McCarthy et al. 1991). It isalso possible that outflow angle is changing with time, as Figure 9.
Misalignment angle vs logarithm of the radio coreprominence. Misalignment angle obtained for multiple hotspotsis also plotted.
Figure 10.
Misalignment angle vs axial ratio for the quasar andradio galaxy sample. in the “dentist drill” model (Scheuer 1982) − the jet axisrandom walks sending out the jet in different directions. The internal pressure of the jet when compared with the ex-ternal gas pressure of the ambient medium can be used tounderstand whether the ambient medium is sufficient to con-
MNRAS , 1–15 (2018) Vaddi et al. fine the outer parts of the radio structure. The non-thermalinternal pressure in the radio jets contributed by the rela-tivistic particles and magnetic field can be crudely estimatedfrom the minimum energy arguments for synchrotron radi-ation. It assumes equipartition of energy density in the par-ticles and in the magnetic field (e.g., Burbidge 1956; Miley1980). This internal pressure will be a lower limit and isgiven by (following O’Dea & Owen (1987)) P min = B min π + E min φ V d y ne cm − (3)where, B min = [ π ( + k ) C L rad ( V φ ) − ] / µ G (4)is the magnetic field at minimum pressure condition, and E min = (cid:20) V φ π (cid:21) / [ L rad ( + k ) C ] / er g (5)is the energy of the particles at minimum pressure. k isthe ratio of proton to electron energy and is assumed to be1, C is a constant from synchrotron theory that dependson the spectral index, and the upper and lower cut-off fre-quencies, L rad is the radio luminosity within the upper andlower cut-off frequencies. We use C = 2.8 × by assuminga spectral index of − . , 10 MHz and 100 GHz as the lowerand upper cut-off frequencies, respectively. φ is the volumefilling factor and is assumed to be equal to 1. To estimate thevolume V (in cm − ), we make an assumption that the radiolobe is cylindrical with half the lobe width as the radius andcore-to-hotspot distance as the height of the cylinder. Theradio luminosity L rad is calculated using (O’Dea & Owen1987) L rad = . × D M pc S ν − α ( + z ) −( + α ) ×( ν ( + α ) u − ν ( + α ) l )( + α ) − er g s − (6)The average P min for quasars is . × − dyne cm − .We follow the same procedure and estimate minimum pres-sure for the radio galaxies studied here. The average P min = . × dynes cm − for radio galaxies. This is roughlya factor of 4 lower than the P min in quasars. Given thata greater fraction of radio galaxies are below redshift of 1,the lower P min of radio galaxies may likely be a result ofthe increasing P min with redshift as seen in Figure 11 anddescribed below.Figure 11 shows the minimum lobe pressure as a func-tion of redshift. The lobe pressure correlates strongly withredshift with a correlation coefficient of 0.83 obtained at asignificance greater than 99.99 per cent. The slope of the lin-ear fit is 1.32. The plot suggests that high redshift sourceshave lobes with higher minimum non-thermal internal pres-sure (see also O’Dea et al. 2009). This could be an out-come of the increasing radio luminosity and decreasing lobewidths observed in this sample with redshift. This also re-sults in increasing B min with redshift. Wan & Daly (1996)suggest that inner regions on radio lobes (close to the core)are in pressure equilibrium with their surroundings and theambient gas densities are found to have generally no de-pendence on redshift (O’Dea et al. 2009). This implies thatthe actual source magnetic field strengths are lower thanthe minimum energy magnetic field strengths. Alternatively, Figure 11.
Minimum pressure in the radio lobes vs redshift forquasars and radio galaxies.
Table 7.
Spearman correlation statistics for different propertiesof the combined quasars and radio galaxy sample.Property 1 Property 2 Correlation Probabilitycoefficient p-value z Arm-length Ratio 0.2 0.4 z α H S -0.4 0.003 α H S P . GHz -0.5 0.02 R c Arm-length Ratio -0.4 0.05LLS α H S -0.3 0.2Axial Ratio α H S R c Misalignment Angle 0.03 0.8 high redshift sources are likely interacting with the ambientmedium and have not yet reached pressure equilibrium withtheir surrounding. High redshift sources have denser ambi-ent gas than at low redshift. This requires high pressures inthe radio lobes to maintain pressure equilibrium with theambient dense gas.
TGSS is a radio survey carried out using the GMRT at150 MHz and covers a declination range of − ◦ to + ◦ .It has the highest resolution of (cid:48)(cid:48) in the low frequencyregime. The TGSS Alternative Data Release (ADR1, In-tema et al. 2017) re-processed the TGSS images using arobust automated pipeline SPAM (Intema 2014) which em-ploys techniques such as direction dependent calibration ofionospheric phase errors and image-based flagging to dealwith the widespread RFI at the low frequency. We use theTGSS ADR1 images to study the morphology of this quasarand radio galaxy sample at low frequencies and derive to-tal energy transported by the jets during the lifetime of theAGN. We find that the classical FR II type morphology MNRAS000
TGSS is a radio survey carried out using the GMRT at150 MHz and covers a declination range of − ◦ to + ◦ .It has the highest resolution of (cid:48)(cid:48) in the low frequencyregime. The TGSS Alternative Data Release (ADR1, In-tema et al. 2017) re-processed the TGSS images using arobust automated pipeline SPAM (Intema 2014) which em-ploys techniques such as direction dependent calibration ofionospheric phase errors and image-based flagging to dealwith the widespread RFI at the low frequency. We use theTGSS ADR1 images to study the morphology of this quasarand radio galaxy sample at low frequencies and derive to-tal energy transported by the jets during the lifetime of theAGN. We find that the classical FR II type morphology MNRAS000 , 1–15 (2018) adio-loud AGN properties seen in the 1.4 and 5 GHz images is no longer the same at150 MHz. Several sources (see Figure A15) show signaturesof wings and extended emission in directions perpendicularand at an oblique orientation to the primary lobes observedat GHz frequencies, reminiscent of X-shaped galaxies (Leahy& Williams 1984; Leahy & Parma 1992). Here we simply re-fer to all such structures as “distorted”. We note that thesewings persist in higher resolution GMRT images at 610 MHz(Vaddi et al. 2018, in preparation), attesting to the reality ofthese features. These distorted radio morphologies may indi-cate jet axis re-alignment and/or multiple activity episodesin these AGNs. The synchrotron emitting plasma in the lobes can be used asan indicator of the amount of energy supplied by the jets tothe lobes (Godfrey & Shabala 2016). The time-averaged to-tal bulk kinetic jet power, defined as the total energy trans-ported by the jets over the lifetime of the source, is given by(Equation 12 of Willott et al. (1999)) Q jet ≈ × [( + z ) −( + α ) π D L S ] / er g s − (7)where S is the total flux density at 150 MHz inW m − Hz − , α is the spectral index estimated using theflux density at 150 MHz and 1.4 GHz, and D L is the lumi-nosity distance. Equation 7 assumes that synchrotron radi-ation losses are negligible. The average quasar jet power is6.4 ± × erg s − . The KS test shows that the time-averaged jet kinetic power for quasars and radio galaxiesmay have similar distribution ( p − v alue = . ). The jetpower ranges from ( − ) × erg s − , with 3C 470 ra-dio galaxy having the highest jet kinetic power (refer Table1, and Table 6). The jet power and redshift are correlatedwhich is expected from a flux limited 3C sample. For thequasar sample, we notice that jet power is related to themass of the supermassive black hole such that higher jetpowers are associated with more massive black holes. How-ever, a partial correlation test between jet power and theblack hole mass while ignoring the effect of redshift doesnot show any correlation ( ρ =-0.3, p − v alue =0.5) betweenjet power and black hole mass suggesting that the observedtrend is likely a manifestation of Malmquist bias. The Kharb et al. (2008) radio galaxies have radio power > WHz − at 178 MHz. Their angular sizes are > > × h − W Hz − sr − which for our chosen H gives 5 × WHz − sr − ; the sources have redshift between0.3 and 1.78. The black hole masses for the quasars were ob-tained from McLure et al. (2006), who studied the FWHM ofthe broad-line emission and typically found v > − .Based on these selection criteria, we can say the followingabout its effects on our results. The selected radio galaxiesare NLRG that are mostly in the plane of the sky and lack the broad-lines for BH mass estimates through virial meth-ods. This BH mass selection criteria for quasars, therefore,does not change the results. Since the quasars in our sampleare selected such that spectral ageing can be performed, itautomatically selects quasars that have larger lobe extentswhich in turn selects sources that are intrinsically larger orare at larger angles to the line of sight. This gives the quasarsin our sample a larger average LLS compared to previousstudies. This, however, does not change the general conclu-sion that quasars have statistically smaller projected sizesthan the radio galaxies, in line with unification. We have presented the results from our 1.4 and 5 GHz studywith the VLA of 11 FR II radio-loud quasars. The . − GHz spectral index images reveal spectral steepening alongthe lobes, moving away from the hotspots. These maps andresults will be used in the spectral ageing analysis, the resultsof which will be presented in Paper II. We compare thesequasars with FR II radio galaxies to study the radio-loudunification scheme. We consider 13 narrow-line FR II radiogalaxy sample that span similar redshift, luminosity, andhave matched frequency/resolution data. We see that radio-loud unification largely holds but environmental factors alsoplay an important role. We have shown that:(i) The radio core prominence ( R c ) parameter, which isa statistical indicator of beaming and thereby orientation,suggests that the radio galaxies considered in our study lie atorientation angles between ◦ and ◦ , while the quasars lieat orientation angles between ◦ and ◦ , broadly consistentwith the radio-loud unification scheme.(ii) About 90 per cent of the sources have brighterhotspots on the jetted side and the brighter hotspot isequally likely to be on the shorter or on the longer arm indi-cating that beaming effects may not be the primary contrib-utor to the observed structural and brightness asymmetry.(iii) The hotspot spectral index ( α HS ) distribution is sim-ilar for quasars and radio galaxies. The α HS is stronglyanti-correlated with redshift and it is argued to be dueto higher synchrotron losses at higher redshifts due todensity-dependent effects while luminosity-dependent orsize-dependent effects are not seen.(iv) The average projected LLS of radio galaxies is1.6 ± R c . This is consistent withthe orientation based unification scheme and LLS is likelyprobing orientation.(v) The median axial ratios of quasars are roughly a factorof 1.4 lower than the radio galaxies. This may be explainedby projection in length since the differences in lobe widthsbetween quasars and radio galaxies are minimal. However,larger and significant lobe distortions seen in quasars com-pared to radio galaxies support the importance of intrin-sic/environmental differences. It is possible that the direc-tion of the quasar jets in our sample is changing with time,as in the “dentist drill” model, while radio galaxies have sta-ble long-term collimated jets. Alternatively, evolutionaryrelationship between quasars and radio galaxies, however,cannot be ruled out. MNRAS , 1–15 (2018) Vaddi et al. (vi) The misalignment angles for quasars are roughly 1.8times greater than radio galaxies likely indicating an orienta-tion effect. However, the radio core prominence which is alsoa statistical indicator of orientation does not show a corre-lation with the misalignment angle. Environmental factorsmay be instrumental in this lack of correlation. R c couldbe influenced by small-scale environment asymmetries whilethe misalignment angle could be influenced by both smallscale and large scale environment asymmetries.(vii) The minimum lobe pressure is strongly correlatedwith redshift. High redshift sources have lobes with higherminimum internal pressure to maintain pressure equilibriumwith the denser ambient gas.(viii) Low frequency emission at 150 MHz with theGMRT reveal the lobes to move away from the classicalFR II morphologies; many sources reveal extended, wingedmorphologies that could suggest changes in jet orientationand/or restarted AGN activity in these quasars.(ix) The time-averaged jet power estimated using the150 MHz data is similar for quasars and radio galaxies.In summary, several of the observed properties of quasarsare consistent with the orientation-based radio-loud unifi-cation scheme. However, differences and relations betweenorientation indicators such as axial ratio, misalignment an-gle, and larger lobe distortions in quasars can be explainedby intrinsic/environmental asymmetries. ACKNOWLEDGEMENTS
We thank the referee for constructive and insightful sug-gestions that have significantly improved the quality of themanuscript. The National Radio Astronomy Observatory isa facility of the National Science Foundation operated un-der cooperative agreement by Associated Universities, Inc.The work of Stefi Baum and Chris O’Dea was supported byNSERC (Natural Sciences and Engineering Research Coun-cil of Canada). The work of Ruth Daly and Trent Bar-busca was supported by Penn State University. This researchhas made use of the NASA/IPAC Extragalactic Database(NED), which is operated by the Jet Propulsion Laboratory,California Institute of Technology, under contract with theNational Aeronautics and Space Administration. We thankthe staff of the GMRT who have made these observationspossible. GMRT is run by the National Centre for RadioAstrophysics of the Tata Institute of Fundamental Research.
REFERENCES
Antonucci R. R. J., 1984, ApJ, 278, 499Antonucci R., 1993, ARA&A, 31, 473Antonucci R., Barvainis R., 1990, ApJ, 363, L17Antonucci R. R. J., Miller J. S., 1985, ApJ, 297, 621Antonucci R. R. J., Ulvestad J. S., 1985, ApJ, 294, 158Arshakian T. G., 2005, A&A, 436, 817Athreya R. M., Kapahi V. K., 1998, Journal of Astrophysics andAstronomy, 19, 63Barthel P. D., 1989, ApJ, 336, 606Barthel P. D., Arnaud K. A., 1996, MNRAS, 283, L45Blumenthal G., Miley G., 1979, A&A, 80, 13Blundell K. M., Rawlings S., Willott C. J., 1999, AJ, 117, 677Bolton J. G., 1966, Nature, 211, 917Bridle A. H., Perley R. A., 1984, ARA&A, 22, 319Browne I. W. A., Clark R. R., Moore P. K., Muxlow T. W. B.,Wilkinson P. N., Cohen M. H., Porcas R. W., 1982, Nature,299, 788Burbidge G. R., 1956, ApJ, 124, 416Chambers K. C., Miley G. K., van Breugel W. J. M., 1990, ApJ,363, 21Cohen M. H., et al., 1977, Nature, 268, 405Cohen M. H., Ogle P. M., Tran H. D., Goodrich R. W., MillerJ. S., 1999, AJ, 118, 1963Condon J. J., Cotton W. D., Greisen E. W., Yin Q. F., PerleyR. A., Taylor G. B., Broderick J. J., 1998, AJ, 115, 1693Conway J. E., Murphy D. W., 1993, ApJ, 411, 89Daly R. A., 2011, MNRAS, 414, 1253Daly R. A., Kharb P., O’Dea C. P., Baum S. A., Mory M. P.,McKane J., Altenderfer C., Beury M., 2010, ApJS, 187, 1Dennett-Thorpe J., Bridle A. H., Laing R. A., Scheuer P. A. G.,1999, MNRAS, 304, 271Fanaroff B. L., Riley J. M., 1974, MNRAS, 167, 31PGiovannini G., Feretti L., Gregorini L., 1987, A&AS, 69, 171Godfrey L. E. H., Shabala S. S., 2016, MNRAS, 456, 1172Gopal-Krishna 1988, A&A, 192, 37Gopal-Krishna Wiita P. J., 1990, A&A, 236, 305Gopal-Krishna Wiita P. J., 2004, ArXiv Astrophysics e-prints,Gopal-Krishna Wiita P. J., Saripalli L., 1989, MNRAS, 239, 173Gopal-Krishna Mhaskey M., Mangalam A., 2012, ApJ, 744, 31Hough D. H., Readhead A. C. S., 1989, AJ, 98, 1208Intema H. T., 2014, SPAM: Source Peeling and Atmospheric Mod-eling, Astrophysics Source Code Library (ascl:1408.006)Intema H. T., Jagannathan P., Mooley K. P., Frail D. A., 2017,A&A, 598, A78Ishwara-Chandra C. H., Saikia D. J., 2000, MNRAS, 317, 658Jackson N., Tadhunter C., Sparks W. B., 1998, MNRAS, 301, 131Kapahi V. K., 1985, MNRAS, 214, 19PKapahi V. K., Saikia D. J., 1982, Journal of Astrophysics andAstronomy, 3, 465Kellermann K. I., Sramek R., Schmidt M., Shaffer D. B., GreenR., 1989, AJ, 98, 1195Kellermann K. I., et al., 2007, Ap&SS, 311, 231Ker L. M., Best P. N., Rigby E. E., R¨ottgering H. J. A., GendreM. A., 2012, MNRAS, 420, 2644Kharb P., Shastri P., 2004, A&A, 425, 825Kharb P., O’Dea C. P., Baum S. A., Daly R. A., Mory M. P.,Donahue M., Guerra E. J., 2008, ApJS, 174, 74Kharb P., Lister M. L., Cooper N. J., 2010, ApJ, 710, 764Klamer I. J., Ekers R. D., Bryant J. J., Hunstead R. W., SadlerE. M., De Breuck C., 2006, MNRAS, 371, 852Klamer I. J., Ekers R. D., Hunstead R. W., 2007, in Afonso J.,Ferguson H. C., Mobasher B., Norris R., eds, AstronomicalSociety of the Pacific Conference Series Vol. 380, Deepest As-tronomical Surveys. p. 213Krolik J. H., Chen W., 1991, AJ, 102, 1659Laing R. A., Peacock J. A., 1980, MNRAS, 190, 903MNRAS000
Antonucci R. R. J., 1984, ApJ, 278, 499Antonucci R., 1993, ARA&A, 31, 473Antonucci R., Barvainis R., 1990, ApJ, 363, L17Antonucci R. R. J., Miller J. S., 1985, ApJ, 297, 621Antonucci R. R. J., Ulvestad J. S., 1985, ApJ, 294, 158Arshakian T. G., 2005, A&A, 436, 817Athreya R. M., Kapahi V. K., 1998, Journal of Astrophysics andAstronomy, 19, 63Barthel P. D., 1989, ApJ, 336, 606Barthel P. D., Arnaud K. A., 1996, MNRAS, 283, L45Blumenthal G., Miley G., 1979, A&A, 80, 13Blundell K. M., Rawlings S., Willott C. J., 1999, AJ, 117, 677Bolton J. G., 1966, Nature, 211, 917Bridle A. H., Perley R. A., 1984, ARA&A, 22, 319Browne I. W. A., Clark R. R., Moore P. K., Muxlow T. W. B.,Wilkinson P. N., Cohen M. H., Porcas R. W., 1982, Nature,299, 788Burbidge G. R., 1956, ApJ, 124, 416Chambers K. C., Miley G. K., van Breugel W. J. M., 1990, ApJ,363, 21Cohen M. H., et al., 1977, Nature, 268, 405Cohen M. H., Ogle P. M., Tran H. D., Goodrich R. W., MillerJ. S., 1999, AJ, 118, 1963Condon J. J., Cotton W. D., Greisen E. W., Yin Q. F., PerleyR. A., Taylor G. B., Broderick J. J., 1998, AJ, 115, 1693Conway J. E., Murphy D. W., 1993, ApJ, 411, 89Daly R. A., 2011, MNRAS, 414, 1253Daly R. A., Kharb P., O’Dea C. P., Baum S. A., Mory M. P.,McKane J., Altenderfer C., Beury M., 2010, ApJS, 187, 1Dennett-Thorpe J., Bridle A. H., Laing R. A., Scheuer P. A. G.,1999, MNRAS, 304, 271Fanaroff B. L., Riley J. M., 1974, MNRAS, 167, 31PGiovannini G., Feretti L., Gregorini L., 1987, A&AS, 69, 171Godfrey L. E. H., Shabala S. S., 2016, MNRAS, 456, 1172Gopal-Krishna 1988, A&A, 192, 37Gopal-Krishna Wiita P. J., 1990, A&A, 236, 305Gopal-Krishna Wiita P. J., 2004, ArXiv Astrophysics e-prints,Gopal-Krishna Wiita P. J., Saripalli L., 1989, MNRAS, 239, 173Gopal-Krishna Mhaskey M., Mangalam A., 2012, ApJ, 744, 31Hough D. H., Readhead A. C. S., 1989, AJ, 98, 1208Intema H. T., 2014, SPAM: Source Peeling and Atmospheric Mod-eling, Astrophysics Source Code Library (ascl:1408.006)Intema H. T., Jagannathan P., Mooley K. P., Frail D. A., 2017,A&A, 598, A78Ishwara-Chandra C. H., Saikia D. J., 2000, MNRAS, 317, 658Jackson N., Tadhunter C., Sparks W. B., 1998, MNRAS, 301, 131Kapahi V. K., 1985, MNRAS, 214, 19PKapahi V. K., Saikia D. J., 1982, Journal of Astrophysics andAstronomy, 3, 465Kellermann K. I., Sramek R., Schmidt M., Shaffer D. B., GreenR., 1989, AJ, 98, 1195Kellermann K. I., et al., 2007, Ap&SS, 311, 231Ker L. M., Best P. N., Rigby E. E., R¨ottgering H. J. A., GendreM. A., 2012, MNRAS, 420, 2644Kharb P., Shastri P., 2004, A&A, 425, 825Kharb P., O’Dea C. P., Baum S. A., Daly R. A., Mory M. P.,Donahue M., Guerra E. J., 2008, ApJS, 174, 74Kharb P., Lister M. L., Cooper N. J., 2010, ApJ, 710, 764Klamer I. J., Ekers R. D., Bryant J. J., Hunstead R. W., SadlerE. M., De Breuck C., 2006, MNRAS, 371, 852Klamer I. J., Ekers R. D., Hunstead R. W., 2007, in Afonso J.,Ferguson H. C., Mobasher B., Norris R., eds, AstronomicalSociety of the Pacific Conference Series Vol. 380, Deepest As-tronomical Surveys. p. 213Krolik J. H., Chen W., 1991, AJ, 102, 1659Laing R. A., Peacock J. A., 1980, MNRAS, 190, 903MNRAS000 , 1–15 (2018) adio-loud AGN properties Laing R. A., Riley J. M., Longair M. S., 1983, MNRAS, 204, 151Leahy J. P., Parma P., 1992, in Roland J., Sol H., Pelletier G.,eds, Extragalactic Radio Sources. From Beams to Jets. pp307–308Leahy J. P., Williams A. G., 1984, MNRAS, 210, 929Leahy J. P., Muxlow T. W. B., Stephens P. W., 1989, MNRAS,239, 401Mangalam A. V., Gopal-Krishna 1995, MNRAS, 275, 976McCarthy P. J., van Breugel W., Kapahi V. K., 1991, ApJ, 371,478McLure R. J., Jarvis M. J., Targett T. A., Dunlop J. S., BestP. N., 2006, MNRAS, 368, 1395Meisenheimer K., Roser H.-J., Hiltner P. R., Yates M. G., LongairM. S., Chini R., Perley R. A., 1989, A&A, 219, 63Miley G. K., 1968, Nature, 218, 933Miley G. K., 1971, MNRAS, 152, 477Miley G., 1980, ARA&A, 18, 165Miller P., Rawlings S., Saunders R., 1993, MNRAS, 263, 425Moore P. K., Browne I. W. A., Daintree E. J., Noble R. G., WalshD., 1981, MNRAS, 197, 325Morabito L. K., Harwood J. J., 2018, MNRAS,Morabito L. K., et al., 2017, MNRAS, 469, 1883Mullin L. M., Riley J. M., Hardcastle M. J., 2008, MNRAS, 390,595Netzer H., 2015, ARA&A, 53, 365O’Dea C. P., Owen F. N., 1987, ApJ, 316, 95O’Dea C. P., Daly R. A., Kharb P., Freeman K. A., Baum S. A.,2009, A&A, 494, 471Ogle P. M., Cohen M. H., Miller J. S., Tran H. D., FosburyR. A. E., Goodrich R. W., 1997, ApJ, 482, L37Orr M. J. L., Browne I. W. A., 1982, MNRAS, 200, 1067Padovani P., 2017, Nature Astronomy, 1, 0194Padovani P., Urry C. M., 1992, ApJ, 387, 449Pogge R. W., 1988, ApJ, 328, 519Reynolds J. E., 1980, Proceedings of the Astronomical Society ofAustralia, 4, 74Roland J., Hanisch R. J., Veron P., Fomalont E., 1985, A&A, 148,323Ryle Sir M., Longair M. S., 1967, MNRAS, 136, 123Saikia D. J., Jeyakumar S., Wiita P. J., Sanghera H. S., SpencerR. E., 1995, MNRAS, 276, 1215Scheuer P. A. G., 1982, in Heeschen D. S., Wade C. M., eds, IAUSymposium Vol. 97, Extragalactic Radio Sources. pp 163–165Smith E. P., Heckman T. M., 1990, ApJ, 348, 38Strittmatter P. A., Hill P., Pauliny-Toth I. I. K., Steppe H., WitzelA., 1980, A&A, 88, L12Tadhunter C., 2008, New Astron. Rev., 52, 227Tadhunter C., 2016, A&ARv, 24, 10Tielens A. G. G. M., Miley G. K., Willis A. G., 1979, A&AS, 35,153Tran H. D., Cohen M. H., Goodrich R. W., 1995, AJ, 110, 2597Urry C. M., Padovani P., 1995, PASP, 107, 803Wan L., Daly R. A., 1996, ApJ, 467, 145Wan L., Daly R. A., 1998, ApJ, 499, 614Wardle J. F. C., Miley G. K., 1974, A&A, 30, 305Wellman G. F., Daly R. A., Wan L., 1997, ApJ, 480, 96White R. L., Becker R. H., 1992, ApJS, 79, 331Willott C. J., Rawlings S., Blundell K. M., Lacy M., 1999, MN-RAS, 309, 1017Wilson A. S., Braatz J. A., Heckman T. M., Krolik J. H., MileyG. K., 1993, ApJ, 419, L61Young S., Hough J. H., Efstathiou A., Wills B. J., Axon D. J.,Bailey J. A., Ward M. J., 1996, MNRAS, 279, L72Yuan Z., Wang J., 2012, ApJ, 744, 84Zirbel E. L., Baum S. A., 1995, ApJ, 448, 521
APPENDIX A: SPECTRAL INDEX ANDCONTINUUM IMAGES OF THE QUASARS
This paper has been typeset from a TEX/L A TEX file prepared bythe author.MNRAS , 1–15 (2018) Vaddi et al. (a) 3C 14 (b) 3C 47(c) 3C 109 (d) 3C 205
Figure A1.
Spectral index maps of the quasars made from the 1.4 GHz and 5 GHz VLA images. The contours trace the 1.4 GHz totalintensity. The color bar indicates the variation in the spectral index. The beam is convolved with the largest FWHM of the 1.4 and 5GHz total intensity images. MNRAS000
Spectral index maps of the quasars made from the 1.4 GHz and 5 GHz VLA images. The contours trace the 1.4 GHz totalintensity. The color bar indicates the variation in the spectral index. The beam is convolved with the largest FWHM of the 1.4 and 5GHz total intensity images. MNRAS000 , 1–15 (2018) adio-loud AGN properties (a) 3C 336 (b) 3C 351(c) 3C 204 (d) 3C 208 Figure A2.
Spectral index maps of the quasars made from the 1.4 GHz and 5 GHz VLA images. The contours trace the 1.4 GHz totalintensity. The color bar indicates the variation in the spectral index. The beam is convolved with the largest FWHM of the 1.4 and 5GHz total intensity images.MNRAS , 1–15 (2018) Vaddi et al. (a) 3C 249.1 (b) 3C 263(c) 3C 432
Figure A3.
Spectral index maps of the quasars made from the 1.4 GHz and 5 GHz VLA images. The contours trace the 1.4 GHz totalintensity. The color bar indicates the variation in the spectral index. The beam is convolved with the largest FWHM of the 1.4 and 5GHz total intensity images. MNRAS000
Spectral index maps of the quasars made from the 1.4 GHz and 5 GHz VLA images. The contours trace the 1.4 GHz totalintensity. The color bar indicates the variation in the spectral index. The beam is convolved with the largest FWHM of the 1.4 and 5GHz total intensity images. MNRAS000 , 1–15 (2018) adio-loud AGN properties (a) 3C 14 :- Total intensity contour map at 1.4 GHz.The peak surface brightness is 0.275 Jy beam − . Thecontours are such that the levels increase in steps of2 (-0.02 (dashed), 0.02,...,90)% of the peak brightness.The CLEAN beam FWHM is 1.50 × − . Thecontours are such that the levels increase in steps of2 (-0.01 (dashed), 0.01,...,90)% of the peak brightness.The CLEAN beam FWHM is 1.81 × Figure A4.
Total intensity maps at 1.4 GHz and 5 GHz.(a) 3C 47 :- Total intensity contour map at 1.4 GHz.The peak surface brightness is 0.271 Jy beam − . Thecontours are such that the levels increase in steps of2 (-0.02 (dashed), 0.02,...,90)% of the peak brightness.The CLEAN beam FWHM is 1.50 × − . Thecontours are such that the levels increase in steps of2 (-0.01 (dashed), 0.01,...,90)% of the peak brightness.The CLEAN beam FWHM is 1.50 × Figure A5.
Total intensity maps at 1.4 GHz and 5 GHz.MNRAS , 1–15 (2018) Vaddi et al. (a) 3C 109 :- Total intensity contour map at 1.4 GHz.The peak surface brightness is 0.313 Jy beam − . Thecontours are such that the levels increase in steps of 2(-0.175 (dashed), 0.175,...,90)% of the peak brightness.The CLEAN beam FWHM is 1.60 × − . Thecontours are such that the levels increase in steps of2 (-0.01 (dashed), 0.01,...,90)% of the peak brightness.The CLEAN beam FWHM is 1.50 × Figure A6.
Total intensity maps at 1.4 GHz and 5 GHz.(a) 3C 204 :- Total intensity contour map at 1.4 GHz.The peak surface brightness is 0.201 Jy beam − . Thecontours are such that the levels increase in steps of2 (-0.01 (dashed), 0.01,...,90)% of the peak brightness.The CLEAN beam FWHM is 1.99 × − . Thecontours are such that the levels increase in steps of2 (-0.02 (dashed), 0.02,...,90)% of the peak brightness.The CLEAN beam FWHM is 1.61 × Figure A7.
Total intensity maps at 1.4 GHz and 5 GHz. MNRAS000
Total intensity maps at 1.4 GHz and 5 GHz. MNRAS000 , 1–15 (2018) adio-loud AGN properties (a) 3C 205 :- Total intensity contour map at 1.4 GHz.The peak surface brightness is 0.702 Jy beam − . Thecontours are such that the levels increase in steps of2 (-0.01 (dashed), 0.01,...,90)% of the peak brightness.The CLEAN beam FWHM is 2.13 × − . Thecontours are such that the levels increase in steps of2 (-0.01 (dashed), 0.01,...,90)% of the peak brightness.The CLEAN beam FWHM is 1.87 × Figure A8.
Total intensity maps at 1.4 GHz and 5 GHz.(a) 3C 208 :- Total intensity contour map at 1.4 GHz.The peak surface brightness is 1.35 Jy beam − . Thecontours are such that the levels increase in steps of2 (-0.01 (dashed), 0.01,...,90)% of the peak brightness.The CLEAN beam FWHM is 1.46 × − . Thecontours are such that the levels increase in steps of2 (-0.01 (dashed), 0.01,...,90)% of the peak brightness.The CLEAN beam FWHM is 1.87 × Figure A9.
Total intensity maps at 1.4 GHz and 5 GHz.MNRAS , 1–15 (2018) Vaddi et al. (a) 3C 249.1 :- Total intensity contour map at 1.4 GHz.The peak surface brightness is 0.329 Jy beam − . Thecontours are such that the levels increase in steps of2 (-0.02 (dashed), 0.02,...,90)% of the peak brightness.The CLEAN beam FWHM is 1.80 × − . Thecontours are such that the levels increase in steps of2 (-0.01 (dashed), 0.01,...,90)% of the peak brightness.The CLEAN beam FWHM is 1.79 × Figure A10.
Total intensity maps at 1.4 GHz and 5 GHz.(a) 3C 263 :- Total intensity contour map at 1.4 GHz.The peak surface brightness is 1.50 Jy beam − . Thecontours are such that the levels increase in steps of2 (-0.01 (dashed), 0.01,...,90)% of the peak brightness.The CLEAN beam FWHM is 2.30 × − . Thecontours are such that the levels increase in steps of 2(-0.005 (dashed), 0.005,...,90)% of the peak brightness.The CLEAN beam FWHM is 1.83 × Figure A11.
Total intensity maps at 1.4 GHz and 5 GHz. MNRAS000
Total intensity maps at 1.4 GHz and 5 GHz. MNRAS000 , 1–15 (2018) adio-loud AGN properties (a) 3C 336 :- Total intensity contour map at 1.4 GHz.The peak surface brightness is 0.624 Jy beam − . Thecontours are such that the levels increase in steps of2 (-0.02 (dashed), 0.02,...,90)% of the peak brightness.The CLEAN beam FWHM is 1.90 × − . Thecontours are such that the levels increase in steps of2 (-0.01 (dashed), 0.01,...,90)% of the peak brightness.The CLEAN beam FWHM is 1.32 × Figure A12.
Total intensity maps at 1.4 GHz and 5 GHz.(a) 3C 351 :- Total intensity contour map at 1.4 GHz.The peak surface brightness is 0.911 Jy beam − . Thecontours are such that the levels increase in steps of 2(-0.005 (dashed), 0.005,...,90)% of the peak brightness.The CLEAN beam FWHM is 1.84 × − . Thecontours are such that the levels increase in steps of2 (-0.01 (dashed), 0.01,...,90)% of the peak brightness.The CLEAN beam FWHM is 1.40 × Figure A13.
Total intensity maps at 1.4 GHz and 5 GHz.MNRAS , 1–15 (2018) Vaddi et al. (a) 3C 432 :- Total intensity contour map at 1.4 GHz.The peak surface brightness is 0.746 Jy beam − . Thecontours are such that the levels increase in steps of 2(-0.005 (dashed), 0.005,...,90)% of the peak brightness.The CLEAN beam FWHM is 2.50 × − . Thecontours are such that the levels increase in steps of2 (-0.01 (dashed), 0.01,...,90)% of the peak brightness.The CLEAN beam FWHM is 1.44 × Figure A14.
Total intensity maps at 1.4 GHz and 5 GHz. MNRAS000
Total intensity maps at 1.4 GHz and 5 GHz. MNRAS000 , 1–15 (2018) adio-loud AGN properties (a) 3C204 - TGSS contour map. The lowest TGSS con-tour is at 0.08 Jy/Beam and the contours increase insteps of 2. The peak intensity is 7.5 Jy/Beam and therms is 5.2 mJy/Beam. (b) 3C208 - TGSS contour map. The lowest TGSS con-tour is at 0.08 Jy/Beam and the contours increase insteps of 2. The peak intensity is 20 Jy/Beam and anrms of 9 mJy/Beam. TGSS data reveal extended struc-ture that is perpendicular to the VLA 1.4 GHz jet-lobeaxis(c) 3C249.1 - TGSS contour map. The lowest TGSScontour is at 0.08 Jy/Beam and the contours increasein steps of 2. The peak intensity is 11.5 Jy/Beam and anrms of 5 mJy/Beam. This source also shows extendedradio emissionat 150 MHz along the axis perpendicularto that of the 1.4 GHz emission. (d) 3C263 - TGSS contour map. The lowest TGSS con-tour is at 0.04 Jy/Beam and the contours increase insteps of 2. The peak intensity is 14 Jy/Beam and anrms of 3.1 mJy/Beam. Figure A15.
Total intensity maps at 150 MHz reveal diffuse extended emission much beyond the emission at 1.4 GHz.MNRAS , 1–15 (2018) Vaddi et al.
Figure A16.
An example figure with labels to different components that are used to estimate different physical parameters discussedin Section 2.3 . MNRAS000