The Event Horizon Telescope Image of the Quasar NRAO 530

NRAO 530 (1730−130, J1733−1304) is a flat-spectrum radio quasar (FSRQ) that belongs to the class of bright γ-ray blazars and shows significant variability across the entire electromagnetic spectrum. The source was monitored by the University of Michigan Radio Observatory at 4.8, 8.4, and 14.5 GHz for several decades ¹⁵¹ until 2012. The quasar underwent a dramatic radio outburst in 1997 (Aller et al. 2009), during which its flux density at 14.5 GHz exceeded 10 Jy, while the average value is ∼2 Jy. Since 2002, NRAO 530 has been monitored by the Submillimeter Array (SMA; Maunakea, Hawaii) at 1.3 mm and 870 μm. ¹⁵² The data show high amplitude variability at 1.3 mm from 1–4 Jy, with the brightest outburst in the end of 2010 contemporaneous with prominent γ-ray activity (Williamson et al. 2014). Ramakrishnan et al. (2015) found a statistically significant correlation between the radio and γ-ray light curves of NRAO 530, based on measurements at 37 GHz by the Metsähovi Radio Observatory (Aalto University, Finland) and γ-ray fluxes measured with the Large Area Telescope (LAT) on board the Fermi Space Gamma-Ray Telescope. The blazar is included in a sample of active galactic nuclei (AGNs) observed with the POLAMI program (Agudo et al. 2014), which provides both the flux density and polarization measurements at 3 mm and 1.3 mm started in 2009. According to the data during the perion 2007–2021, the degree of linear polarization (LP) at 1.3 mm changes from 1% to 15%, with circular polarization detections at a level of 1%–2% (private communication).

The quasar is known as a violently variable object at optical wavelengths. According to the SMARTS optical/IR monitoring program of blazars (Bonning et al. 2012), NRAO 530 has a fractional variability amplitude of ∼68% and ∼35% at optical and IR wavelengths, respectively, with an average magnitude of ∼14 at the K band (λ_eff ∼ 2.2 μm) and ∼17.5 in the R band (λ_eff ∼ 658 nm). Foschini et al. (2006) reported a short hard X-ray flare detected serendipitously by the IBIS/ISGRI detector on board INTEGRAL on 2004 February 17, although the timescale of the flare, <1 hr, questions the association with the FSRQ. However, a moderate increase of the LP at 2 cm contemporaneous with the flare was observed as well. The recent 10 yr Fermi LAT catalog, 4FGL (Abdollahi et al. 2020), lists the average γ-ray flux of the source at 0.1–300 GeV as (1.8 ± 1.2) × 10⁻⁷ phot cm⁻² s⁻¹. During this period NRAO 530 underwent the highest amplitude γ-ray outburst in 2010 October–November, when the γ-ray flux rose to 10⁻⁶ phot cm⁻² s⁻¹ (Williamson et al. 2014). This γ-ray activity coincided with a significant brightening at optical wavelengths and an increase of the optical linear degree of polarization up to 15%. ¹⁵³

NRAO 530 possesses a highly relativistic jet. The source is intensively monitored with the Very Long Baseline Array (VLBA) at 15 GHz within the MOJAVE program (e.g., Lister et al. 2016) and at 43 GHz within the VLBA-BU-BLAZAR program (Jorstad et al. 2017; Weaver et al. 2022). VLBA images show a radio jet dominated by the core located at the southern end of the jet. At 15 GHz, the jet extends up to 10 mas to the north, with a weak feature observed ∼25 mas from the core. Based on 11 moving features, Lister et al. (2019) reported a median apparent speed of 12.3 ± 0.6 c, with a maximum apparent speed of 27.3 ± 1.0 c. At 43 GHz, the jet contains a stationary feature located ∼0.3 mas from the core and curves to the northwest ∼0.8 mas from the core. Eight moving knots detected at 43 GHz over 2007–2018 exhibit a wide range of apparent speeds, from 2.6 ± 0.5 c to 37.2 ± 0.2 c (Weaver et al.2022), with three knots revealing a strong acceleration after passing the stationary feature, while knot B3 ejected in the beginning of 2010 demonstrates both accelerations from 4.1 c to 7.7 c and to 33.7 c and then a deceleration to 8.5 c. Using properties of the light curves of moving knots and their apparent speeds, Jorstad et al. (2017) estimated the average Lorentz and Doppler factors of the jet to be Γ ∼ 8 and δ ∼ 9, respectively, with a jet viewing angle of ∼3°. These values of the Lorentz and Doppler factors are close to the minimum values obtained by Liodakis et al. (2018) for the jet of NRAO 530. These authors estimated the variability of the Doppler factor by analyzing the 15 GHz light curve provided by the Owens Valley 40 m Radio Telescope after taking into consideration brightness temperature arguments and the apparent speeds of knots at 15 GHz; this resulted in ${{\rm{\Gamma }}}_{\max }\sim 40$ and ${\delta }_{\max }\sim 21.$

On kiloparsec scales, the quasar shows a weak two-sided jet elongated in the east–west direction over 25, with a very diffuse eastern part and a bright, compact knot on the western side, located about 1'' from the core (Kharb et al. 2010). The kiloparsec-scale jet is almost perpendicular to the parsec-scale jet and has a complex radio morphology: the eastern part is similar to that expected for Fanaroff–Riley I (FRI) type radio galaxies, while the western part can be classified as FRII type.

NRAO 530 has a redshift of z = 0.902 (Junkkarinen 1984), for which 100 μas corresponds to a linear distance of 0.803 pc (Planck Collaboration et al. 2016, H₀ = 67.7 km s⁻¹ Mpc⁻¹, Ω_m = 0.307, and Ω_Λ = 0.693). The source contains a supermassive black hole, the mass of which is currently uncertain, with estimates ranging from 3 × 10⁸ M_☉ (Keck 2019) to 2 × 10⁹ M_☉ (Liang & Liu 2003).

Due to its brightness, compactness, and close location on the sky to the Galactic Center, NRAO 530 is frequently used as a calibrator in very long baseline interferometry (VLBI) observations of the radio source Sagittarius A^* (Sgr A* ) located in the center of the Milky Way (e.g., Lu et al. 2011). In this paper we report on the VLBI observations of NRAO 530 in 2017 April, when the source was employed as a calibrator during the Event Horizon Telescope (EHT) observation of Sgr A* at 1.3 mm (EHTC et al. 2022a). During the EHT observation, NRAO 530 was in a moderate activity state at γ-ray energies with the flux ∼1 × 10⁻⁷ phot cm⁻² s⁻¹ as well as at optical (R-band magnitude ∼17.8) ¹⁵⁴ and radio wavelengths (the flux density at 37 GHz ∼3.7 Jy). ¹⁵⁵ According to the VLBA monitorings at 43 GHz (Weaver et al. 2022) and 15 GHz (Lister et al. 2019), no superluminal components were ejected in 2017. The EHT observation of NRAO 530 provides unique insight into the subparsec scale structure of the quasar with microarcsecond resolution, which are important for understanding the blazar physics in general and properties of the NRAO 530 jet in particular. The outline of the paper is as follows: in Section 2 we describe the EHT observations of NRAO 530, their reduction and general data set properties, in Sections 3 and 4 we present results of total intensity and LP imaging, respectively, in Section 5 we discuss our results, and in Section 6 we summarize our findings.

The EHT observed NRAO 530 on three consecutive nights on April 5–7 during the 2017 campaign (MJD 57848–57850), as a calibrator of Sgr A^* (EHTC et al. 2022a, 2022b). The observations were performed with the full EHT 2017 array of eight telescopes located at six geographical sites: the Atacama Large Millimeter/submillimeter Array (ALMA) ¹⁵⁶ and the Atacama Pathfinder Experiment (APEX) telescope in Chile; the Large Millimeter Telescope Alfonso Serrano (LMT) in Mexico; the IRAM 30 m telescope (PV, joined only on April 7) in Spain; the Submillimeter Telescope (SMT) in Arizona; the James Clerk Maxwell Telescope (JCMT) and SMA in Hawaii; and the South Pole Telescope (SPT) in Antarctica. Two 2 GHz wide frequency bands, centered at 227.1 GHz (LO), and 229.1 GHz (H i), were recorded. The observations were carried out using dual feeds, right-hand and left-hand circularly polarized (RCP ad LCP, respectively), for all stations other than ALMA and JCMT. ALMA recorded dual LP, which was subsequently converted at the correlation stage to a circular basis by PolConvert (Martí-Vidal et al. 2016; Goddi et al. 2019). The JCMT only observed the RCP component, which we utilized to approximate total intensity (this approximation is correct as long as the fractional circular polarization of the source can be neglected), and we typically omit JCMT baselines for the polarimetric imaging. The EHT array setup is detailed in EHTC et al. (2019a, 2019b).

Recorded signals were correlated at the MIT Haystack Observatory and the Max-Planck-Institut für Radioastronomie, Bonn. Subsequent data reduction procedures are described in EHTC et al. (2019c), Blackburn et al. (2019), and Janssen et al. (2019). There were minor updates to the calibration with respect to the EHT results published earlier (EHTC et al. 2019a; Kim et al. 2020), particularly regarding the telescope sensitivity estimates and complex polarimetric gains calibration. These updates are identical to those described in the EHT Sgr A* publications (EHTC et al. 2022a, 2022b). Flux density on the short intra-site baselines (ALMA-APEX and SMA-JCMT) was gain-calibrated to the simultaneous ALMA-only flux density of 1.6 Jy, reported by Goddi et al. (2021), providing an additional constraint on the amplitude gains for the stations with a colocated partner (network calibration; Blackburn et al. 2019). The polarimetric leakage calibration follows procedures outlined in EHTC et al. (2021), where D-terms of stations with an intra-site VLBI baseline (ALMA, APEX, SMA, and JCMT) were calculated through a multisource fitting procedure (Martí-Vidal et al. 2021), and several estimates for D-terms of remaining stations were reported. Here we employ the fiducial D-terms given by Issaoun et al. (2022) based on the analysis presented in EHTC et al. (2021). Some imaging algorithms that we use ignore the leakage calibration and estimate D-terms independently, providing an additional consistency check of the calibration procedures and their impact on the resulting images (see Section 3).

The (u, v)-coverage of the EHT observations of NRAO 530 is given in Figure 1. We show the coverage on each individual day, as well as aggregated over the entire observing campaign. The longest projected baseline reaches 8.66 Gλ for SPT-PV, which determines the instrumental angular resolution of 24 μas, measured as the minimum fringe spacing. Inspection of the data revealed no indication of the source structure evolution during the observations spanning only about 52 hr on 2017 April 5−7, with the total on-source integration time of 76 minutes (see Figures 2–3). For that reason, the entire data set was used simultaneously for static imaging and model fitting.

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The NRAO 530 data set self-consistency has been rigorously verified as a validation of the EHT Sgr A* results, presented in EHTC et al. (2022b). The signal-to-noise ratio (S/N) of individual NRAO 530 detections (band-averaged in frequency, scan-averaged in time) typically exceeds 100 on baselines to ALMA, and is about an order of magnitude lower on remaining baselines; see the top panel of Figure 2. We estimated an additional 2% of complex visibility uncertainties due to variety of nonclosing effects (e.g., residual polarimetric leakage, frequency, and time smearing); see EHTC et al. (2019c,2022b). Inspecting the calibrated visibility amplitudes in the middle panel of Figure 2, we notice that the shortest inter-site baseline (LMT-SMT) provides a lower limit on the compact emission flux density corresponding to about 0.6 Jy, which implies the presence of up to 1 Jy of milliarcsecond-scale flux that is resolved out by the EHT but measured on the intra-site VLBI baselines and by the ALMA-only observations. Additionally, in the bottom panel of Figure 2 we present the measured visibility amplitudes as a function of the baseline length, projected at the jet PA (see Section 3). We notice that the amplitudes on baselines shorter than about 2.5 Gλ can be approximated by a Gaussian of FWHM 55 μas and 0.6 Jy amplitude, roughly informing us about the size and the total flux density of the observed compact feature. Measurements on baselines longer than 2.5 Gλ indicate a deviation from the Gaussian model, in agreement with the resolved source consisting of more than one component. Both EHT Sgr A* calibrators, NRAO 530 and J1924-2914 (Issaoun et al.2022) are fairly compact and bright sources at 230 GHz. During the week-long EHT observing run, their source structure and flux density did not show significant variations (see Figure 3). These properties allowed us to use both sources for quantifying the effects of the residual errors in the characterization of the antenna gains. Mitigating such effects was crucial for discerning between the rapid intrinsic variability of Sgr A* and the apparent flux density variations produced by imperfect calibration (EHTC et al. 2022c, 2022d; Wielgus et al.2022). Details about the procedures and methods used to obtain the antenna gains from the two calibrators and the subsequent transfer to the Sgr A* data sets are reported in Section 3.2 in EHTC et al. (2022b).

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For the imaging of NRAO 530, we have utilized data calibrated by the EHT-HOPS pipeline (Blackburn et al. 2019), combined over all days (2017 April 5–7) and both frequency bands (227.1 and 229.1 GHz). Nonzero closure phases shown in Figure 3 reveal resolved and nontrivial structure of the source. We used three analysis methods across five different implementations. These are: imaging via inverse modeling (the traditional CLEAN method), forward regularized maximum likelihood (RML) modeling (eht-imaging and SMILI algorithms), which are described in detail in EHTC et al. (2019d), as well as two posterior exploration methods based on a Markov Chain Monte Carlo (MCMC) scheme—a D-term Modeling Code (DMC; Pesce 2021) and a dedicated Bayesian image reconstruction framework Themis (Broderick et al. 2020a). For each of the methods, the freedom to decide resolution, field of view, or amount of data coherent averaging was left to the expert imaging subteams, who made such decisions based on the specific properties of each algorithm. As an example, MCMC methods tend to average data more to reduce the computational complexity of the likelihood evaluation. Below we give a short description of each method, with some details on the particular application to the NRAO 530 imaging.

3.1.  DIFMAP

3.2.  eht-imaging

3.3.  SMILI

3.4.  DMC

3.5.  Themis

3.6. Fiducial Images of NRAO 530

Figure 4 presents final (fiducial) total intensity images obtained by the methods discussed in Sections 3.2–3.5. For the presentation purposes, all images are shown using the same field of view of 128 × 128 μas, more narrow than the field of view typically used for the imaging. The contribution of emission outside of this region is relatively small and consistent with the image noise level, which, in the case of the EHT, is rather high and dominated by the systematic uncertainties related to sparse (u, v)-coverage. There is a high degree of agreement between the different imaging methods in terms of the source structure and the relative brightness of individual features. All images show the jet to be elongated from the southeast to the northwest over ∼60 μas. A visual inspection suggests that the jet consists of two dominant features (see Figure 5), with the brightest one, C0, located at the southeast end of the jet and itself exhibiting substructures C0a and C0b. However, there is also a difference between the models—DIFMAP and DMC images contain a very low-brightness extended feature that turns to the northeast, almost by ∼90° to the main direction of the jet, the eht-imaging image shows a hint of such a feature, while the SMILI and Themis methods do not reveal its presence. This discrepancy is likely related to the latter two algorithms promoting sparsity and compactness in the reconstructed images. We have designated this ambiguous feature as C2 in Figure 5, marked by a dashed line ellipse. Although the knot appears in the fiducial method-averaged image if we increase the dynamic range, its brightness peak is on the level of the image noise. In addition, DIFMAP, eht-imaging, and SMILI images correspond to very similar peak brightness values, but the Themis and DMC images indicate somewhat brighter and weaker brightness peaks, respectively.

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Figure 3 shows consistency between closure phase measurements on selected triangles and corresponding predictions of the fitted models. Table 1 provides goodness of fit normalized χ² statistics computed for the models shown in Figure 4 for self-calibrated visibility amplitudes (${\chi }_{\mathrm{AMP}}^{2}$), closure phases (${\chi }_{\mathrm{CP}}^{2}$), and log closure amplitudes (${\chi }_{\mathrm{logCA}}^{2}$). The estimated total flux density of the compact source model is also provided. This quantity is particularly challenging to constrain with the EHT array, which in 2017 lacked baselines between 5 Mλ and 500 Mλ projected length, sensitive to milliarcsecond-scale emission. Based on the presented statistics, there is no strong preference for any particular model. Therefore, we have combined the final images constructed by the different methods, shown in Figure 4, to produce a fiducial method-averaged total intensity image of NRAO 530, presented in Figure 5. The averaging procedure involves aligning images to maximize their cross-correlation, re-gridding them to a common resolution and field of view, and taking a standard arithmetic mean on a pixel-by-pixel basis. It follows the practice of EHTC et al. (2019d, 2022c), Kim et al. (2020), and Issaoun et al. (2022), and it is designed to make the resulting image-domain morphology more robust against method-specific systematics.

Table 1. Characterization of the Fits to NRAO 530 Data

Quantity	DIFMAP	eht-imaging	SMILI	DMC	Themis
${\chi }_{\mathrm{AMP}}^{2}$	0.974	0.638	0.858	0.517	1.211
${\chi }_{\mathrm{CP}}^{2}$	3.812	0.899	2.346	0.977	4.210
${\chi }_{\ \mathrm{logCA}}^{2}$	1.513	1.137	2.727	0.943	2.844
Flux (Jy)	1.030	0.958	0.899	1.585	1.120

Note. For the χ² calculation, the original thermal error budget was inflated by a nonclosing systematic error amounting to 2% of the complex visibilities.

Download table as:  ASCIITypeset image

3.7. Total Intensity Component Modeling

We have performed LP imaging of NRAO 530 using four out of five algorithms described in Section 3: DIFMAP (CLEAN), eht-imaging, DMC, and Themis. SMILI implementation does not currently have the capability to reconstruct polarimetric images. Given the large and poorly constrained systematic uncertainties in the polarimetric calibration of the EHT data at times when ALMA is not participating in observations, only 2017 April 6 and 7 data were used, except for DMC, which allows for a very flexible calibration of complex polarimetric gains simultaneous with the image reconstruction (Pesce 2021), and hence could reliably fit the entire data set. Similarly, except for DMC and Themis, we disregarded JCMT single polarization data for the LP analysis.

4.1. Consistency Test for D-terms

For all methods other than Themis, we either fixed all polarimetric leakage D-terms following the multisource fitting procedures described in EHTC et al. (2021) and Issaoun et al. (2022), or we only solved for the SPT D-terms. The latter resulted in the SPT D-terms being no larger than 1%, with uncertainties comparable to or larger than the values themselves, which justifies setting SPT D-terms to zero. In the case of Themis, we performed full fitting of D-term coefficients simultaneously with the image reconstruction as an additional consistency test. In Figure 6 we provide a comparison between the assumed and estimated D-terms. The results show a generally high level of consistency, particularly when considering the difficulty of quantifying the systematics present in a fit to a single sparsely sampled data set. JCMT D-terms are constrained very poorly as a consequence of the station operating with a single polarization receiver in 2017. There is a good correlation between the assumed and calculated D-terms, corresponding to a Pearson coefficient of 0.86 for the vector of 64 D-terms (ignoring the uncertainties) and median residual of 2% on a single complex D-term. Altogether, the consistency test results support fixing D-terms for the model fitting and data analysis.

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4.2. Linear Polarization Images

Figure 7 presents LP images of NRAO 530 in 2017 April, obtained using a selection of methods. The absolute magnitude of LP ($\sqrt{{{ \mathcal Q }}^{2}+{{ \mathcal U }}^{2}}$ for LP Stokes image-domain components ${ \mathcal Q }$ and ${ \mathcal U }$) is indicated with white contours and the length of the electric vector position angle (EVPA) ticks, while the EVPA tick colors represent fractional LP magnitude. The Faraday rotation toward NRAO 530 is entirely negligible at 230 GHz (Goddi et al. 2021); hence, observed EVPA can be directly interpreted as an intrinsic source property. The grayscale background image and black contours correspond to the total intensity. Similarly as in Section 3.6, in Figure 8 we also present the method-averaged image, where the background image and cyan contours represent the LP magnitude, while the white contours describe the total intensity image map. Similarly as in the case of the total intensity images, we adopted images shown in Figures 7–8 to a common field of view of 128 μas. The contribution of the emission outside of this region is consistent with the image noise.

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Figure 8 serves the purpose of identifying the total intensity and LP components. As for the total intensity images, the LP images show similar structure across all algorithms, although there are some differences in the polarization properties of the jet features. The most consistent among the methods is the polarized feature P0 near the core, which has a slight shift to the northeast with respect to the total intensity peak C0 in the DIFMAP, eht-imaging, and DMC images. In the DMC image, the polarized feature in the southeast part of the C1 knot is most prominent, compared to images produced by the other methods. The DIFMAP and DMC images show a polarized feature, P2, associated with the diffuse northeastern total intensity component C2, which has the highest degree of polarization. A hint of this feature can be seen in the eht-imaging image, while the Themis image possesses a prominent polarized feature P1 that is rather a combination of polarization that can be associated with knot C1 and the northeastern component. Discrepancies seen in the Themis image may be related to the spline raster resolution limitations imposed by the MCMC algorithm. We expect that the method-averaged LP image, shown in Figure 8, should reveal the most reliable image-domain polarization features.

4.3. Linear Polarization Component Modeling

4.4. Constraints on Circular Polarization

The high-resolution total and polarized intensity images of the quasar NRAO 530 at 230 GHz obtained during the EHT campaign in 2017 April indicate a jet direction of ∼−28° on μas scales. This jet direction is different from those seen at 86 GHz (PA ∼ 10°; Issaoun et al. 2019) and 43 GHz (PA ∼ −3°; Weaver et al. 2022), implying a wiggling of the jet with the distance from the core. In fact, the PA of component C2/P2 (see Table 3) agrees with that of the jet direction at 86 GHz within the uncertainties, which can be a sign of jet curvature already at this location. If that is indeed the case, the EVPA of the P2 component could be more aligned with the local direction of the bending jet, which supports the origin of the emission in the shear. The curvature could be caused by an imbalance between the pressure inside and outside the jet resulting in the development of instabilities in the flow, or it could be connected with jet precession. An analysis of the jet structure from subparsec to kiloparsec scales using multiwavelength VLBI images of NRAO 530 contemporaneous with the 230 GHz EHT image will be presented in Lisakov et al. (2023, in preparation).

The brightest feature in the source, C0, which we associate with the VLBI core at 1.3 mm, is complex and consists of two components, C0a and C0b. Complex structure in the core was also revealed in the EHT images of the quasar 3C 279 (Kim et al. 2020). Variability of such core structures can be expected on short timescales with quasiperiodic oscillations produced by kink instabilities in the case of a magnetically dominated jet (Dong et al. 2020). The intrinsic brightness temperatures of both C0 components are lower than the equipartition brightness temperature (see Table 2), even if the Doppler factor of the jet on microarcsecond scales is lower than that found on milliarcsecond scales. A possible explanation is that the energy density of the jet on microarcsecond scales is dominated by the magnetic field.

Homan et al. (2006) analyzed the brightness temperatures of VLBI cores at 15 GHz of a large sample of AGNs. They found that, in a quiescent state, cores have a narrow range of intrinsic brightness temperatures close to the value expected for equipartition, while during outbursts the characteristic intrinsic brightness temperature significantly exceeds T_eq. The authors have estimated that in active states, the energy in radiating particles exceeds the energy in the magnetic field by a factor of ∼10⁵. This implies that somewhere between the 230 and 15 GHz cores, there is a region where a magnetically dominated jet changes its state and reaches equipartition conditions. Moreover, Jorstad et al. (2007) suggested, based on quasi-simultaneous multiepoch observations at 43 GHz (VLBA), 86 GHz (BIMA ¹⁵⁷ ), and 230/345 GHz (JCMT), that this region is located between the 86 and 43 GHz VLBI cores, while Lee (2013) found that 86 GHz VLBI cores in a sample of compact radio sources observed with Global Millimeter VLBI Array have a characteristic intrinsic brightness temperature lower than the equipartition temperature. The observed low brightness temperature of C0 somewhat supports this scenario. However, analysis of the simultaneous VLBI observations at 230 and 86 GHz is needed to draw firm conclusions.

The polarized features in the jet are shifted with respect to the total intensity components with which they are associated. The shifts are almost transverse to the inner jet direction—the jet direction is from southeast to northwest, while the shifts are to the east. This feature appears robust in the image reconstructions. One viable interpretation of this phenomenon involves a difference in ordering of the magnetic field transverse to the jet, with the magnetic field on the eastern part of the jet being more ordered than that of the western part. This can be understood if the magnetic field in the jet consists of two components—ordered and turbulent, with the turbulent component being more pronounced on the western side of the jet, while a helical magnetic field can represent the ordered component. Indeed, a helical structure of the magnetic field is supported by the EVPA behavior in different polarized components. The EVPA undergoes significant changes along ∼60 μas of the jet length: it goes from being oblique in the core to aligning with the jet direction in P1 ∼30 μas to perpendicular to the inner jet direction in P2 ∼55 μas from the core. In addition, a higher polarization at the western side can be attributed to velocity shear between the jet boundary and the ambient medium being stronger on the western side, which is consistent with the explanation of the nature of component P2/C2. However, the EVPA behavior is more aligned with a helical magnetic field, although we do not have a sufficient resolution to resolve the EVPA structure across the jet. Recent work by Pushkarev et al. (2022) analyzed the polarization properties in a large sample of AGNs observed with the VLBA at 15 GHz within the MOJAVE survey, which presented transverse fractional polarization slices beyond the core region for 307 sources. The authors have found that the majority of the sources (including NRAO 530) show a clear increase in fractional polarization toward the jet edges, with an asymmetric profile. They suggest that the observed patterns of polarization can be explained by helical magnetic fields with different pitch angles and different geometric and kinematic parameters of the jet, which is surrounded by a sheath. It therefore appears that a helical magnetic field structure persists in the quasar jet from subparsec to hundred-parsec scales.

In addition to EHT data, we have obtained optical polarization observations of NRAO 530 several days before and after the EHT campaign. Figure 9 shows polarization measurements in the optical R band, at 221 GHz for the arcsecond resolution of ALMA (Goddi et al. 2021), and those of the EHT resolved components at 230 GHz. The optical observations were carried out with the 1.8 m Perkins telescope (Flagstaff, AZ) using the PRISM camera. The description of data reduction can be found in Jorstad et al. (2010). When comparing to the optical and ALMA values, the best agreement across wavelengths is found with the P2 component, indicating dominance of the magnetic field direction perpendicular to the microarcsecond scale jet direction in the source. This suggests that the magnetic field direction in the optical emission region is similar to that of P2, which has the highest degree of polarization in the jet.

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Using the 2017 EHT observations, we have obtained the first total and polarized intensity images of the quasar NRAO 530 at 230 GHz, with an unprecedented angular resolution of ∼20 μas. We have not detected statistically significant variability of the source structure on a timescale of 3 days, and hence we have combined the data over epochs and frequencies to generate final images. We have used a variety of methods to construct the total and linearly polarized intensity images and averaged the final reconstructions produced by different methods to obtain the fiducial total and polarized intensity maps.

On microarcsecond scales, the quasar has a bright core of ∼300 mJy, with a jet extending up to 60 μas along a PA ∼ −28°. The core has substructure consisting of two features, C0a and C0b. We associate the 1.3 mm VLBI core with the brightest knot, C0a, while C0b, located at a PA ∼ +155° and ∼3 times weaker than C0a can be a more opaque region of the core. The core is polarized, with a degree of polarization of ∼5%−8%. The extended jet includes two features, C1/P1 and C2/P2, with mutually orthogonal directions of polarization, parallel and perpendicular to the jet, respectively, suggestive of a helical magnetic field structure. The centers of the polarized knots P0 and P1 are shifted slightly transverse to the jet with respect to the centers of the corresponding total intensity knots C0 and C1. This can be caused either by a more ordered magnetic field on the eastern side of the jet, connected with a helical structure of the magnetic field, or a stronger interaction between the jet and the external medium on this side.

Feature C2 has the highest degree of polarization in the jet, ∼20%. However, polarized knot P2, which occupies only a fraction of the total area of C2, has a peak polarization of ∼60%. The latter implies a nearly uniform magnetic field in the P2 region. In fact, the optical polarization measurement nearest in time to the EHT campaign has an EVPA similar to that in P2. NRAO 530 is a bright γ-ray source, and the origin of high-energy emission in blazars continues to be highly debated. We can speculate that the P2 region, with a very uniform magnetic field, might be a location where nonthermal optical and high-energy emission originates. Future multiepoch and multiwavelength observations of the source simultaneous with EHT campaigns can provide a significant advance toward understanding the location of the high-energy dissipation zone in blazar jets.

The Event Horizon Telescope Collaboration thanks the following organizations and programs: the Academia Sinica; the Academy of Finland (projects 274477, 284495, 312496, 315721); the Agencia Nacional de Investigación y Desarrollo (ANID), Chile via NCN19_058 (TITANs) and Fondecyt 1221421, the Alexander von Humboldt Stiftung; an Alfred P. Sloan Research Fellowship; Allegro, the European ALMA Regional Centre node in the Netherlands, the NL astronomy research network NOVA and the astronomy institutes of the University of Amsterdam, Leiden University and Radboud University; the ALMA North America Development Fund; the black hole Initiative, which is funded by grants from the John Templeton Foundation and the Gordon and Betty Moore Foundation (although the opinions expressed in this work are those of the author(s) and do not necessarily reflect the views of these Foundations); Chandra DD7-18089X and TM6-17006X; the China Scholarship Council; China Postdoctoral Science Foundation fellowship (2020M671266); Consejo Nacional de Ciencia y Tecnología (CONACYT, Mexico, projects U0004-246083, U0004-259839, F0003-272050, M0037-279006, F0003-281692, 104497, 275201, 263356); the Consejería de Economía, Conocimiento, Empresas y Universidad of the Junta de Andalucía (grant P18-FR-1769), the Consejo Superior de Investigaciones Científicas (grant 2019AEP112); the Delaney Family via the Delaney Family John A. Wheeler Chair at Perimeter Institute; Dirección General de Asuntos del Personal Académico-Universidad Nacional Autónoma de México (DGAPA-UNAM, projects IN112417 and IN112820); the Dutch Organization for Scientific Research (NWO) VICI award (grant 639.043.513) and grant OCENW.KLEIN.113; the Dutch National Supercomputers, Cartesius and Snellius (NWO Grant 2021.013); the EACOA Fellowship awarded by the East Asia Core Observatories Association, which consists of the Academia Sinica Institute of Astronomy and Astrophysics, the National Astronomical Observatory of Japan, Center for Astronomical Mega-Science, Chinese Academy of Sciences, and the Korea Astronomy and Space Science Institute; the European Research Council (ERC) Synergy Grant "BlackHoleCam: Imaging the Event Horizon of Black Holes" (grant 610058); the European Union Horizon 2020 research and innovation program under grant agreements RadioNet (No. 730562) and M2FINDERS (No. 101018682); the Horizon ERC Grants 2021 program under grant agreement No. 101040021; the Generalitat Valenciana postdoctoral grant APOSTD/2018/177 and GenT Program (project CIDEGENT/2018/021); MICINN Research Project PID2019-108995GB-C22; the European Research Council for advanced grant "JETSET: Launching, propagation and emission of relativistic jets from binary mergers and across mass scales" (grant No. 884631); the Institute for Advanced Study; the Istituto Nazionale di Fisica Nucleare (INFN) sezione di Napoli, iniziative specifiche TEONGRAV; the International Max Planck Research School for Astronomy and Astrophysics at the Universities of Bonn and Cologne; DFG research grant "Jet physics on horizon scales and beyond" (grant No. FR 4069/2-1); Joint Columbia/Flatiron Postdoctoral Fellowship, research at the Flatiron Institute is supported by the Simons Foundation; the Japan Ministry of Education, Culture, Sports, Science and Technology (MEXT; grant JPMXP1020200109); the Japanese Government (Monbukagakusho: MEXT) Scholarship; the Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for JSPS Research Fellowship (JP17J08829); the Joint Institute for Computational Fundamental Science, Japan; the Key Research Program of Frontier Sciences, Chinese Academy of Sciences (CAS, grants QYZDJ-SSW-SLH057, QYZDJSSW-SYS008, ZDBS-LY-SLH011); the Leverhulme Trust Early Career Research Fellowship; the Max-Planck-Gesellschaft (MPG); the Max Planck Partner Group of the MPG and the CAS; the MEXT/JSPS KAKENHI (grants 18KK0090, JP21H01137, JP18H03721, JP18K13594, 18K03709, JP19K14761, 18H01245, 25120007); the Malaysian Fundamental Research Grant Scheme (FRGS) FRGS/1/2019/STG02/UM/02/6; the MIT International Science and Technology Initiatives (MISTI) Funds; the Ministry of Science and Technology (MOST) of Taiwan (103-2119-M-001-010-MY2, 105-2112-M-001-025-MY3, 105-2119-M-001-042, 106-2112-M-001-011, 106-2119-M-001-013, 106-2119-M-001-027, 106-2923-M-001-005, 107-2119-M-001-017, 107-2119-M-001-020, 107-2119-M-001-041, 107-2119-M-110-005, 107-2923-M-001-009, 108-2112-M-001-048, 108-2112-M-001-051, 108-2923-M-001-002, 109-2112-M-001-025, 109-2124-M-001-005, 109-2923-M-001-001, 110-2112-M-003-007-MY2, 110-2112-M-001-033, 110-2124-M-001-007, and 110-2923-M-001-001); the Ministry of Education (MoE) of Taiwan Yushan Young Scholar Program; the Physics Division, National Center for Theoretical Sciences of Taiwan; the National Aeronautics and Space Administration (NASA, Fermi Guest Investigator grants 80NSSC20K1567 and 80NSSC22K1571, NASA Astrophysics Theory Program grant 80NSSC20K0527, NASA NuSTAR award 80NSSC20K0645); NASA Hubble Fellowship grants HST-HF2-51431.001-A, HST-HF2-51482.001-A awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., for NASA, under contract NAS5-26555; the National Institute of Natural Sciences (NINS) of Japan; the National Key Research and Development Program of China (grant 2016YFA0400704, 2017YFA0402703, 2016YFA0400702); the National Science Foundation (NSF, grants AST-0096454, AST-0352953, AST-0521233, AST-0705062, AST-0905844, AST-0922984, AST-1126433, AST-1140030, DGE-1144085, AST-1207704, AST-1207730, AST-1207752, MRI-1228509, OPP-1248097, AST-1310896, AST-1440254, AST-1555365, AST-1614868, AST-1615796, AST-1715061, AST-1716327, AST-1716536, OISE-1743747, AST-1816420, AST-1935980, AST-2034306); NSF Astronomy and Astrophysics Postdoctoral Fellowship (AST-1903847); the Natural Science Foundation of China (grants 11650110427, 10625314, 11721303, 11725312, 11873028, 11933007, 11991052, 11991053, 12192220, 12192223); the Natural Sciences and Engineering Research Council of Canada (NSERC, including a Discovery grant and the NSERC Alexander Graham Bell Canada Graduate Scholarships-Doctoral Program); the National Youth Thousand Talents Program of China; the National Research Foundation of Korea (the Global PhD Fellowship grant: grants NRF-2015H1A2A1033752, the Korea Research Fellowship Program: NRF-2015H1D3A1066561, Brain Pool Program: 2019H1D3A1A01102564, Basic Research Support grant 2019R1F1A1059721, 2021R1A6A3A01086420, 2022R1C1C1005255); Netherlands Research School for Astronomy (NOVA) Virtual Institute of Accretion (VIA) postdoctoral fellowships; Onsala Space Observatory (OSO) national infrastructure, for the provisioning of its facilities/observational support (OSO receives funding through the Swedish Research Council under grant 2017-00648); the Perimeter Institute for Theoretical Physics (research at Perimeter Institute is supported by the Government of Canada through the Department of Innovation, Science and Economic Development and by the Province of Ontario through the Ministry of Research, Innovation and Science); the Princeton Gravity Initiative; the Spanish Ministerio de Ciencia e Innovación (grants PGC2018-098915-B-C21, AYA2016-80889-P, PID2019-108995GB-C21, PID2020-117404GB-C21); the University of Pretoria for financial aid in the provision of the new Cluster Server nodes and SuperMicro (USA) for a SEEDING grant approved toward these nodes in 2020; the Shanghai Pilot Program for Basic Research, Chinese Academy of Science, Shanghai Branch (JCYJ-SHFY-2021-013); the State Agency for Research of the Spanish MCIU through the "Center of Excellence Severo Ochoa" award for the Instituto de Astrofísica de Andalucía (SEV-2017-0709); the Spinoza Prize SPI 78-409; the South African Research Chairs Initiative, through the South African Radio Astronomy Observatory (SARAO, grant ID 77948), which is a facility of the National Research Foundation (NRF), an agency of the Department of Science and Innovation (DSI) of South Africa; the Toray Science Foundation; the Swedish Research Council (VR); the US Department of Energy (USDOE) through the Los Alamos National Laboratory (operated by Triad National Security, LLC, for the National Nuclear Security Administration of the USDOE (Contract 89233218CNA000001); and the YCAA Prize Postdoctoral Fellowship.

We thank the staff at the participating observatories, correlation centers, and institutions for their enthusiastic support. This paper makes use of the following ALMA data: ADS/JAO.ALMA#2016.1.01154.V. ALMA is a partnership of the European Southern Observatory (ESO; Europe, representing its member states), NSF, and National Institutes of Natural Sciences of Japan, together with National Research Council (Canada), Ministry of Science and Technology (MOST; Taiwan), Academia Sinica Institute of Astronomy and Astrophysics (ASIAA; Taiwan), and Korea Astronomy and Space Science Institute (KASI; Republic of Korea), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, Associated Universities, Inc. (AUI)/NRAO, and the National Astronomical Observatory of Japan (NAOJ). The NRAO is a facility of the NSF operated under cooperative agreement by AUI. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under contract No. DE-AC05-00OR22725. We also thank the Center for Computational Astrophysics, National Astronomical Observatory of Japan. The computing cluster of Shanghai VLBI correlator supported by the Special Fund for Astronomy from the Ministry of Finance in China is acknowledged. This work was supported by FAPESP (Fundacao de Amparo a Pesquisa do Estado de Sao Paulo) under grant 2021/01183-8.

APEX is a collaboration between the Max-Planck-Institut für Radioastronomie (Germany), ESO, and the Onsala Space Observatory (Sweden). The SMA is a joint project between the SAO and ASIAA and is funded by the Smithsonian Institution and the Academia Sinica. The JCMT is operated by the East Asian Observatory on behalf of the NAOJ, ASIAA, and KASI, as well as the Ministry of Finance of China, Chinese Academy of Sciences, and the National Key Research and Development Program (No. 2017YFA0402700) of China and Natural Science Foundation of China grant 11873028. Additional funding support for the JCMT is provided by the Science and Technologies Facility Council (UK) and participating universities in the UK and Canada. The LMT is a project operated by the Instituto Nacional de Astrófisica, Óptica, y Electrónica (Mexico) and the University of Massachusetts at Amherst (USA). The IRAM 30 m telescope on Pico Veleta, Spain is operated by IRAM and supported by CNRS (Centre National de la Recherche Scientifique, France), MPG (Max-Planck-Gesellschaft, Germany) and IGN (Instituto Geográfico Nacional, Spain). The SMT is operated by the Arizona Radio Observatory, a part of the Steward Observatory of the University of Arizona, with financial support of operations from the State of Arizona and financial support for instrumentation development from the NSF. Support for SPT participation in the EHT is provided by the National Science Foundation through award OPP-1852617 to the University of Chicago. Partial support is also provided by the Kavli Institute of Cosmological Physics at the University of Chicago. The SPT hydrogen maser was provided on loan from the GLT, courtesy of ASIAA.

This work used the Extreme Science and Engineering Discovery Environment (XSEDE), supported by NSF grant ACI-1548562, and CyVerse, supported by NSF grants DBI-0735191, DBI-1265383, and DBI-1743442. XSEDE Stampede2 resource at TACC was allocated through TG-AST170024 and TG-AST080026N. XSEDE JetStream resource at PTI and TACC was allocated through AST170028. This research is part of the Frontera computing project at the Texas Advanced Computing Center through the Frontera Large-Scale Community Partnerships allocation AST20023. Frontera is made possible by National Science Foundation award OAC-1818253. This research was carried out using resources provided by the Open Science Grid, which is supported by the National Science Foundation and the U.S. Department of Energy Office of Science. Additional work used ABACUS2.0, which is part of the eScience center at Southern Denmark University. Simulations were also performed on the SuperMUC cluster at the LRZ in Garching, on the LOEWE cluster in CSC in Frankfurt, on the HazelHen cluster at the HLRS in Stuttgart, and on the Pi2.0 and Siyuan Mark-I at Shanghai Jiao Tong University. The computer resources of the Finnish IT Center for Science (CSC) and the Finnish Computing Competence Infrastructure (FCCI) project are acknowledged. This research was enabled in part by support provided by Compute Ontario (http://computeontario.ca), Calcul Quebec (http://www.calculquebec.ca), and Compute Canada (http://www.computecanada.ca).

The EHTC has received generous donations of FPGA chips from Xilinx Inc., under the Xilinx University Program. The EHTC has benefited from technology shared under open-source license by the Collaboration for Astronomy Signal Processing and Electronics Research (CASPER). The EHT project is grateful to T4Science and Microsemi for their assistance with Hydrogen Masers. This research has made use of NASA's Astrophysics Data System. We gratefully acknowledge the support provided by the extended staff of the ALMA, both from the inception of the ALMA Phasing Project through the observational campaigns of 2017 and 2018. We would like to thank A. Deller and W. Brisken for EHT-specific support with the use of DiFX. We thank Martin Shepherd for the addition of extra features in the Difmap software that were used for the CLEAN imaging results presented in this paper. We acknowledge the significance and cultural reverance that Maunakea, where the SMA and JCMT EHT stations are located, has always held within the indigenous Hawaiian people.

Software: DIFMAP (Shepherd 1997), Matplotlib (Hunter 2007), DiFX (Deller et al. 2011), NumPy (van der Walt et al. 2011), eht-imaging (Chael et al. 2016), PolConvert (Martí-Vidal et al. 2016), SMILI (Akiyama et al. 2017), EHT-HOPS (Blackburn et al. 2019), Themis (Broderick et al. 2020a), DMC (Pesce 2021).