Free Access
Issue
A&A
Volume 614, June 2018
Article Number A121
Number of page(s) 13
Section Extragalactic astronomy
DOI https://doi.org/10.1051/0004-6361/201832694
Published online 03 July 2018

© ESO 2018

1. Introduction

The study of high-redshift active galactic nuclei (AGN) represents one of the frontiers of modern astrophysics. In the past decades, more than 200 quasars (QSOs) with spectroscopic redshift z > 5.5 were discovered by wide-area optical and near-IR (NIR) surveys (Fan et al. 2006; Willott et al. 2010; Venemans et al. 2013; Bañados et al. 2016, 2018; Matsuoka et al. 2016; Reed et al. 2017; Tang et al. 2017; Yang et al. 2017).

Multi-wavelength observations showed that these QSOs are evolved systems with large black hole masses (108 – 1010M; Mortlock et al. 2011; Wu et al. 2015), and large amount of gas and dust, and intense star formation in their host galaxies (Mgas ~ 109−10M, Mdust ~ 108−9M, SFR up to 1000 M yr−1; for example, Calura et al. 2014; Venemans et al. 2016, 2017; Gallerani et al. 2017b; Decarli et al. 2018). Optical and NIR observations showed that the broad-band spectral energy distributions (SEDs) and the rest-frame NIR/optical/UV spectra of QSOs have not significantly evolved over cosmic time (e.g., Mortlock et al. 2011; Barnett et al. 2015). Only 29 of these high-z QSOs have been studied through their X-ray emission (e.g., Brandt et al. 2002; Farrah et al. 2004; Vignali et al. 2005; Shemmer et al. 2006; Moretti et al. 2014; Page et al. 2014; Ai et al. 2016; Gallerani et al. 2017a). In particular, our group performed a systematic analysis of X-ray archival data of all the 29 QSOs at z > 5.5 observed so far with Chandra, XMM-Newton and Swift-XRT, concluding that the X-ray spectral properties of high-redshift QSOs do not differ significantly from those of AGN at lower redshift (Nanni et al. 2017).

How the 108−10 M black holes (BHs) powering z ~ 6 QSOs could form and grow within 1 Gyr (the age of the Universe at z ≈ 6) is still a challenge for theory. Different scenarios have been proposed to explain the formation of the BH seeds that eventually became SMBHs by z ~ 6. The two most promising ones involve either the remnants of PopIII stars (100 M; e.g., Madau & Rees 2001), or more massive (104−6M) BHs formed from the direct collapse of primordial gas clouds (e.g., Volonteri et al. 2008; Agarwal et al. 2014; see also Valiante et al. 2016 for a model of seed formation at different mass scales). In the case of low-to-intermediate mass (M ≤ 104) seeds, super-Eddington accretion is required to form the black-hole masses observed at z > 6 (e.g., Madau et al. 2014; Volonteri et al. 2016; Pezzulli et al. 2017).

There is general agreement that early massive BHs form in overdense environments, that may extend up to 10 physical Mpc (pMpc), and host large gas quantities (Overzier et al. 2009; Dubois et al. 2013; Costa et al. 2014; Barai et al. 2018). According to simulations, the fields around high-redshift QSOs are expected to show galaxy overdensities, which probably represent the progenitors of the most massive clusters in the local Universe (Springel et al. 2005). In the past decade, large efforts have been made to find overdense regions in fields as large as 2 × 2 pMpc aroundz ~ 6QSOs(e.g.,Stiavelli et al.2005;Husband et al.2013; Bañados et al. 2013; Simpson et al. 2014; Mazzucchelli et al. 2017), but the results were inconclusive. Some of them ascribed the lack of detection of overdensities at very high-z to the strong ionizing radiation from the QSO that may prevent star formation in its vicinity. The presence of strong gas jets and/or radiation feedback extending up to few hundreds of kpc at z = 6 is, in fact, predicted in modern simulations of BHs formation (Costa et al. 2014; Barai et al. 2018).

The QSO SDSS J1030 + 0525 at z = 6.31 (Fan et al. 2001) was one of the first z ~ 6 QSOs discovered by the Sloan Digital Sky Survey (SDSS), and its field is part of the Multiwavelength Chile-Yale survey (MUSYC). It has also been covered by HST/WFC3. Near-IR spectroscopy showed that it is powered by a BH with mass of 1.4 × 109M (Kurk et al. 2007; De Rosa et al. 2011). It was not detected in the submillimeter (Priddey et al. 2003) and radio bands (Petric et al. 2003), but it was detected in the X-rays by Chandra (one 8-ks snapshot in 2002; Brandt et al. 2002) and by XMM-Newton (one 105 ks observation in 2003; Farrah et al. 2004). In concordance with literature results on other z ~ 6 QSOs, the rest-frame optical continuum shape and luminosity of this QSO are consistent with those of lower redshift AGN (Fan et al. 2001). The X-ray spectrum is instead possibly steeper than standard QSOs spectra (Γ ~ 2.1 – 2.4; Farrah et al. 2004 and Nanni et al. 2017). Deep and wide imaging observations of a 8 × 8 pMpc2 region around SDSS J1030 + 0524 also showed that this field features the best evidence to date of an overdense region around a z ~ 6 QSO (Morselli et al. 2014; Balmaverde et al. 2017). In the last few years, our group has obtained data in the optical and X-ray bands to further investigate and confirm the presence of the putative overdensity, and to obtain one of the highest quality spectrum ever achieved in X-ray for a QSO at z ~ 6. In particular, we report here the results from our ~ 500 ks Chandra ACIS-I observation of SDSS J1030 + 0524 that represents the deepest X-ray look at a z > 6 QSO to date.

The paper is organized as follows. In Sect. 2 we describe the X-ray Chandra data, and the data reduction procedure. In Sect. 3 we report the data analysis and spectral fitting, the X-ray variability, and the study of the diffuse emission around the QSO. In Sect. 4 we discuss the physical conditions that can be responsible for the X-ray observed features, provide the multiband SED of the QSO, and discuss the possible origins of the diffuse emission. In Sect. 5 we give a summary of our results. Throughout this paper we assume H 0 = 70 km s−1 Mpc−1, Ω = 0.7, and ΩM = 0.3 (Bennett et al. 2013), and errors are reported at 68% confidence level if not specified otherwise. Upper limits are reported at the 3σ confidence level.

2. Chandra observations

The source SDSS J1030 + 0524 was observed by Chandra with ten different pointings between January and May 2017 for a total exposure of 479 ks. Observations were taken using the Advanced CCD Imaging Spectrometer (ACIS) instrument and the target was positioned on the ACIS-I3 chip, at roll-angle ~ 64° for the first five observations, and at roll-angle ~ 259°, for the others. The ten observations (hereafter ObsIDs) cover a total area of roughly 335 arcmin2 in size and the exposure times of the individual observations range from 26.7 to 126.4 ks. A summary of the observational parameters is provided in Table 1. The data were reprocessed using the Chandra software CIAO v. 4.8 using the vfaint mode for the event telemetry format. Data analysis was carried out using only the events with ASCA grades 0, 2, 3, 4, and 6. We then produced X-ray images in the soft (0.5–2 keV), hard (2–7 keV) and full (0.5–7 keV) bands for each ObsID.

Table 1.

Data information on J1030 + 0524.

After this basic reduction, we corrected the astrometry (applying shift and rotation corrections) of the individual ObsIDs using as reference catalog the WIRCAM catalog comprising 14777 J-band selected sources down to J AB  = 24.5 (Balmaverde et al. 2017). First we created exposure maps and psf maps for all ObsIDs using the CIAO tools fluximage and mkpsfmap, respectively. The exposure and psf maps were computed in the full band at the 90% of the encircled energy fraction (EEF) and at an energy of 1.4 keV. Then, we ran the Chandra source detection task wavdetect on the 0.5–7 keV images to detect sources to be matched with the J-band detected objects. We set the detection threshold to 10−6 and wavelet scales up to 8 pixels in order to get only the brightest sources with a well defined X-ray centroid and we also provided the exposure and psf maps. For the match we considered only the X-ray sources with a positional error1 below ~ 0.4″, in order to avoid sources with too uncertain centroid position. We used the CIAO tool wcs_match and wcs_update to match the sources and correct the astrometry, and create new aspect solution files. We considered a matching radius of 2″ and we applied both translation and rotation corrections. The new aspect solutions were then applied to the event files and the detection algorithm was run again (using the same wavdetect parameters and criteria adopted previously). The applied astrometric correction reduces the mean angular distance between the X-ray sources and their J-band counterparts from θ = 0.253″ to θ = 0.064″. Finally, we stacked the corrected event files using the reproject_obs task and derived a new image of the field. In Fig. 1 we display the final Chandra full band image around the QSO position.

thumbnail Fig. 1.

Full-band (0.5–7 keV) Chandra ACIS-I image of SDSS J1030 + 0524. The red circle represents our extraction region (2″ radius). The grid separation is 5″ and the cutout spans 20″ × 20″ on the sky. Units on the colorbar are counts per pixel.

3. Results

3.1. Timing analysis

The long total exposure taken on a time span of five months enabled us to study the possible presence of flux and spectral variability. We extracted the number of counts in each ObsID from circular regions centered at the optical position of the QSO. We used a radius of 2″, corresponding to 95% of the encircled energy fraction (EEF) at 1.5 keV, for the source extraction, and a nearby region (free of serendipitously detected sources), with a 100 times larger area, for the background extraction. In the final four columns of Table 1 we report the full (0.5–7 keV), soft (0.5–2 keV), hard (2–7 keV) band net counts, extracted in each single observation, and the hardness ratios (HRs), computed as where H and S are the net counts in the hard and soft bands, respectively.

We first determined whether the QSO varied during the Chandra observations by applying a χ 2 test to its entire light curve in the full band. This is computed as(1)

where fi and σi are the full band count rates and its error in the ith observation, is the average count rate of the source and N is the number of the X-ray observations. The null hypothesis is that the count rate in each epoch is consistent with the mean count rate of the entire light curve, within the errors. We show the distribution of the count rates in the three bands (full, soft, and hard) vs time, starting from the first observation, in Fig. 2, where the red lines represent the mean weighted value of the rates. We computed the probability by which the null hypothesis can be rejected (p), and obtained p ~ 0.47 (0.44, and 0.40) for the full (soft, and hard) band, respectively. We then conclude that there is no evidence of count rate variability among our Chandra observations.

thumbnail Fig. 2.

Count rate of SDSS J1030+0524 in the three X-ray bands (full in the top, soft in the middle, and hard in the bottom panel) extracted from the ten Chandra observations vs. the days since the first observation. Errors are reported at the 1σ level. The red solid lines represent the weighted mean.

The HR distribution vs the observation time is reported in Fig. 3. Despite some fluctuations, also the HRs of the different observations show no significant variability (p = 0.53).

thumbnail Fig. 3.

Hardness ratio of SDSS J1030 + 0524 in the ten Chandra observations vs. the days since the first observation. Errors are reported at the 1 σ level. The red solid line represents the weighted mean.

3.2. Spectral analysis

The lack of significant flux and hardness ratio variability allowed us to combine the ten spectra together (each extracted from the corresponding event file), and obtain a final spectrum with 125 net counts in the full band. The spectral channels were binned to ensure a minimum of one count for each bin, and the best-fit model was decided using the Cash statistics (Cash 1979). First, we modeled the spectrum with a simple power-law, using XSPEC v. 12.9 (Arnaud 1996), with a Galactic absorption component fixed to 2.6 × 1020 cm−2 (the value along the line of sight toward the QSO, Kalberla et al. 2005). We found that the best-fit photon index is (C-stat = 88.3 for 93 d.o.f.), and the flux in the 0.5–2 keV band is . The value of the photon index is consistent with the mean photon indices obtained by jointly fitting spectra of unobscured QSOs at the same and at lower redshifts (Γ ~ 1.6–2.0 for 1 ≤ z ≤ 7; e.g., Vignali et al. 2005; Shemmer et al. 2006; Just et al. 2007; Nanni et al. 2017; Vito et al. 2018), but it is flatter than the XMM-Newton value found for the same QSO by Farrah et al. (2004; ) and by Nanni et al. (2017; ; although they fit a power-law plus intrinsic absorption model). Also our measured soft flux is 3.6 times lower than the one derived by Farrah et al. (2004) (f0.5–2 = 6.3 × 10−15 erg s−1 cm−2), and this difference is significant at the 4σ level (see Sect. 3.3 for a detailed study of the long-term variability). The spectrum and its best-fit model and residuals are shown in Fig. 4.

thumbnail Fig. 4.

X-ray spectrum of SDSS J1030 + 0524 fitted with a power-law model (). In the bottom panel we report the residuals [(data–model)/error]. For display purposes we adopted a minimum binning of ten counts per bin.

We performed other fits by adding spectral components to the power-law plus Galactic absorption model. First, we added an intrinsic absorption component. Because of the very high red-shift of the QSO, this fit is sensitive only to very high values of obscuration (N H  ≥ 1023−24 cm−2). We found that the column density is poorly constrained and consistent with no absorption (), as it may be expected for a luminous Type 1 QSO such as J1030 + 0524. Then, we fit the same model with the photon index fixed to Γ = 2.39 (best-fit value found in the XMM-Newton data for this QSO by Nanni et al. 2017) and

we found . To search for the presence and significance of a narrow emission iron line, we also added to the power-law model (with photon index fixed to Γ = 1.8) a Gaussian line, with rest-frame energy of 6.4 keV and width of 10 eV (both fixed in the fit). We obtained a fit with similar quality (C-stat/d.o.f. = 88.4/93) to that of the single power-law model and we derived an upper limit for the rest-frame iron line equivalent width of EW ≤ 460 eV. We also checked the presence of iron lines at rest-frame energy of 6.7 and 6.9 keV (as expected from highly ionized iron, FeXXV and FeXXVI), obtaining a rest-frame equivalent width of EW ≤ 420 eV, in both cases. Considering that we are sampling rest frame energies in the range 3.5–50 keV, where a hardening of AGN spectra is often observed because of the so called “Compton-reflection hump”2, we checked the possible contribution to the spectrum of a reflected component (pexrav model) finding that the photon index is poorly constrained (). The possible presence of a FeXXVI emission line at 6.7 keV in the XMM-Newton spectrum, with significance at the 2.5 σ level, suggested us to fit the Chandra spectrum with a power-law model plus a reflection ionized component (reflionx). However, we obtained an ionization parameter that is poorly constrained and so is the normalization of the reflection component.

Finally, we noted the possible presence of a dip in the Chandra spectrum at ~ 2.4 keV observed-frame (~ 17.5 keV restframe; see red spectrum in Fig. 5). Previous studies of X-ray AGN spectra revealed the presence of blue-shifted Fe K-shell absorption lines, at rest-frame energies > 7 keV, possibly related to ultra-fast outflows (UFOs) of gas ejected from the QSOs with velocities ≥ 104 km s−1 (e.g., Tombesi et al. 2013, 2015). We then checked the presence of absorption features producing this dip, fitting the spectrum with a power-law model (with Γ free to vary) plus an absorption line component. We generated 104 fake spectra (using the same response files and statistics of our original spectrum), and fit them with the power-law plus absorption line model; the adopted procedure is fully described in Lanzuisi et al. (2013b) and Tombesi et al. (2013). Comparing the C-stat distribution of the 104 fake spectra with the one obtained for the original one, we found that the absorption feature is not significant ( < 2 σ level). We also noted the presence of a dip in the 5–10 keV rest-frame energy range (Fig. 5), followed by a rise at lower energies, that could be related to the absorption by warm absorbers; however, this rise at low energies contains only 1–2 counts per bin. We tried to fit this low-energy dip with a warm absorber (warmabs 3 ) plus a power-law model, fixing the photon index to the Chandra (Γ = 1.81) and XMM-Newton (Γ = 2.39) best-fit values. In both cases we found best-fit values of column density (N H  ~ 6 × 1023 cm−2) and ionization parameter (log(xi) ~ 2) that point back to a cold absorber scenario (the one we tested with the power-law plus absorption model). Furthermore, these values are not well constrained due to the limited counting statistics. Finally, we also tried to fit the rise at low energies with a power-law plus a partial covering absorption model (zxipc f), fixing again the photon index to the Chandra (Γ = 1.81) and XMM-Newton (Γ = 2.39) best-fit values. Also in that case, the result points back to a cold absorber scenario (Nh ~ 7 × 1023 cm−2, log(xi) ≤ 2, and covering factor of f ~ 0.9) with a similar statistical quality of the fit. In Table 2 we summarize the results of our spectral analysis.

thumbnail Fig. 5.

Comparison between the 2003 XMM-Newton (in blue) and 2017 Chandra (in red) spectrum of SDSS J1030 + 0524. Both spectra have been corrected for the effective response. The XMM-Newton spectrum is the average of the three EPIC cameras (with S/N weighting). The left panel shows the observed-frame spectra, while the right panel shows the rest-frame one.

Table 2.

Best-fit results of the Chandra data.

3.3. Comparison with previous analysis

J1030 + 0524 has been observed in the X-rays twice in the past: by Chandra in 2002 and by XMM-Newton in 2003. As reported in Sect. 3.2, our derived soft band Chandra flux is ~ 3.6 times lower than that observed by XMM-Newton.

We derived the observed-frame full band (0.5–7 keV) fluxes for the 2002, 2003, and 2017 observations to build the longterm X-ray light curve (see Fig. 6). From the fit we performed on the 2017 Chandra observation, using a simple power-law model (first row in Table 2), we found .

thumbnail Fig. 6.

Long-term X-ray light curve of SDSS J1030 + 0524 in the 0.5 7 keV band. Errors are reported at the 1σ level.

For the Chandra snapshot we extracted the number of counts from a circular region with 2″ radius, centered on the source position, and the background counts from a nearby circular region with ten times larger area. The source is detected with ~6 net counts in the full band. Assuming a power-law with Γ = 1.8, we derived a full band flux of , which is 1.4 times higher than the value found for the 2017 observation but consistent with it within the uncertainties.

For the XMM-Newton observation, we extracted the three spectra (pn, MOS1, MOS2) from circular regions centered at the optical position of the QSOs with radius of 15″, corresponding to 65% of EEF at 1.5 keV, to avoid contamination from nearby luminous sources, while the background was extracted from a nearby region with radius of 30″. We used a grouping of one count for each bin for all spectra of the three cameras, and fit the three EPIC spectra (pn, MOS1, and MOS2) with a simple power-law model with photon index free to vary. We obtained a best-fit value , that is consistent with the one found by Farrah et al. (2004) but is inconsistent at the 2.4σ level with the value reported in the first column of Table 2 (Γ = 1.81 ± 0.18), and a flux , that is 2.5 times higher than the full flux derived from the longest Chandra observation (); the difference is significant at the 4.9σ level. In Fig. 6 we show the 0.5–7 keV light curve of the QSO with the fluxes obtained from the three epochs, while in Fig. 5 we show the observed-frame (left) and rest-frame (right) spectra of our Chandra (in red) and XMM-Newton (in blue) analyses. We determined whether the QSO could be considered variable by applying the X test to its entire light curve in the full band on year timescale, considering the first Chandra (2002) and the XMM-Newton (2003) observations and our longer Chandra observation (2017). We found from our X test that the QSO has varied (p ~ 0.99) with aX value of 8.51 (d.o.f. = 2).

Previous works (see Appendix B of Lanzuisi et al. 2013a) showed that the XMM-Newton source spectra tend to be fitted with softer power-laws (up to 20% difference in photon index) than those observed by Chandra. This difference may be now possibly exacerbated by the rapid degradation of the Chandra ACIS-I effective area, which, for instance, has decreased by 18% at 1.5 keV and by 38% at 1 keV between the observations described in Lanzuisi et al. (2013a) and ours. In order to verify whether the flux and slope variations are due to the AGN variability and not to any instrumental effect related to the different responses of Chandra and XMM-Newton, we performed additional checks on the XMM-Newton and Chandra data-sets. First, we changed the QSO spectral extraction parameters (e.g., size and position of both source and background extraction regions), and the QSO light curve filtering (e.g., cutting the XMM-Newton background fluctuations adopting a different thresholds during the data reprocessing) to verify the fit stability, and found that the new best-fit parameters were fully consistent with the XMM-Newton values reported above. Secondly, we selected five QSOs detected by both Chandra and XMM-Newton (with similar counting statistics of our QSO and observed in the central region of the data-sets), and extracted their spectra with the same extraction parameters that we adopted for J1030 + 0524. We found that the XMM-Newton spectra are neither systematically steeper nor brighter than the corresponding Chandra spectra, at least in the photon counting statistics regime considered here. Furthermore, our normalized difference in photon index ((ΓXMM-NewtonChandra) – 1 = 0.31 ± 0.04) is three times higher than (~ 4σ off) the mean value found for X-ray sources detected with similar statistic ((ΓXMM-NewtonChandra) – 1 = 0.1 in Lanzuisi et al. 2013a). We conclude that the XMM-Newton results are stable and that the observed spectral variability in SDSS J1030 + 0524 is real.

3.4. Diffuse emission southward the QSO

By visual inspection of the 2017 Chandra observation, we noted an excess of photons extending up to 25″ southward of the QSO. This excess becomes more evident by smoothing the image with the task csmooth, using a minimal (maximal) signal-to-noise ratio (S/N) of 2 (50), as shown in Fig. 7 (left). This diffuse emission lies in a region in which our observations are very sensitive, as shown in Fig. 7 (central). We performed photometry on the un-smoothed image, extracting the diffuse counts and spectrum from a region with an area of 460 arcsec2, shown in Fig. 7 (green polygon in the central panel), and the background counts from nearby circular regions (free of X-ray point like sources) with a total area ~ 3 times larger. We found that the diffuse emission is highly significant, with 90 net counts, corresponding to a S/N = 5.9, and a hardness ratio . A hint of this diffuse emission is also visible in the XMM-Newton observation (right panel of Fig. 7), at the same sky coordinates, although its significance is less clear as it is difficult to disentangle the diffuse emission from the emission of the nearby QSO, due to the limited XMM-Newton angular resolution. Visual inspection of Fig. 7 (left) suggests that the diffuse emission may be structured into a few blobs. However, we do not detect any point-like X-ray source running wavdetect with a detection threshold relaxed to 10−5.

thumbnail Fig. 7.

Left panel: Chandra 0.5–2 keV image (2′ × 2′), smoothed with csmooth (see text) and centered at the QSO position. North is up and east is to the left. Green circles mark point-like sources X-ray detected in the soft band. Units on the colorbar are counts per pixel. Central panel: 2′ × 2′ image of the exposure map computed at 1.4 keV and centered at the QSO position. The green 460 arcsec2 region is the one used to extract the net counts of the diffuse emission southward on the QSO in the un-smoothed image, and it lays in the most sensitive peak of the exposure map. The four green dashed circles are the regions used to extract the background. Units on the colorbar are cm2 s per pixel. Right panel: XMM-Newton-pn image in the 0.5–2 keV band (2′ × 2′), centered at the QSO position. Units on the colorbar are counts per pixel.

We fit the diffuse spectrum with a power-law model (including Galactic absorption) and derived a soft band flux of . Considering the soft band flux limit for point like sources of our Chandra observation (f 0.5–2 keV ~ 10−16 erg s−1 cm−2), at least 10 unresolved X-ray sources would be required to reproduce the observed diffuse X-ray flux.

We searched in radio and optical bands for sources detected within the region of diffuse X-ray emission. In the radio observation at 1.4 GHz taken by the Very Large Array (down to a 3σ limit of 60 μJy/beam; Petric et al. 2003), we found a radio lobe of a FRII galaxy (RA = 10:30:25.19, Dec =+5:24:28.50; Petric et al. 2003) inside our region (see Fig. 8, top). We also considered an archival Hubble Space Telescope WFC3 observation in the F160W filter down to mag 27 AB (bottom panel of Fig. 8) and an archival 6.3 h MUSE observation, both centered on the QSO. The sources for which we were able to analyze MUSE spectra are marked in the image with circles of different colors (see bottom panel of Fig. 8): none of them show any sign of AGN activity in their optical spectra.

thumbnail Fig. 8.

Top panel: smoothed Chandra 0.5 – 7 keV image of the 1.5 × 0.8 arcmin2 field around SDSS J1030 + 0524 and the nearby FRII radio galaxy. Radio contours at 1.4 GHz are shown in white. Contour levels are a geometric progression in the square root of two starting at 60μJy. The green polygon marks the region of diffuse X-ray emission southward of the QSO. Units on the colorbar are counts per pixel. Bottom panel: 0.7′ × 0.7′ HST image in the H-band. Circles are centered at the position of MUSE detected sources. The white circle marks SDSS J1030 + 0524. Cyan circles mark sources for which a redshift was measured, yellow circles are stars, green circles mark sources for which no redshift was measured. Magenta contours mark the emission of radio sources at 1.4 GHz. The radio lobe of a FRII radio galaxy (Petric et al. 2003) falls within the region of X-ray diffuse emission (green polygon). In both panels, another radio source is visible at the edge of the diffuse X-ray emission (6″ south-west the QSO). In all images north is up and east is to the left.

4. Discussion

4.1. Variability amplitude

To compare the variability seen in SDSS J1030 + 0524 with that typically seen in AGN, we computed its normalized excess variance, as defined by Nandra et al. (1997) and Turner et al. (1999), and compared it with what is measured in the samples of Shemmer et al. (2017) and Paolillo et al. (2017). Paolillo et al. (2017) measured the variability amplitude (), in the restframe 2–8 keV band, primarily for minimizing the effects of variable obscuration, of X-ray-selected AGN in the 7 Ms exposure of the Chandra Deep Field-South (CDF-S) survey (Luo et al. 2017). This sample includes variable and nonvariable radio-quiet AGN. Shemmer et al. (2017) studied a luminous sample of four radio-quiet quasars (RQQs) at 4.10 ≤ z ≤ 4.35, monitored by Chandra at different epochs. Both Shemmer et al. (2017) and Paolillo et al. (2017) found that the X-ray variability anticorrelates with intrinsic AGN X-ray luminosity. This effect has been also observed in samples of nearby AGN and has been interpreted as the consequence of a larger BH mass in more luminous objects, which would also increase the size of the last stable orbit of the accretion disk and thus influence the overall variability produced in its innermost parts (Papadakis 2004).

We derived the rest-frame 2–8 keV (observed-frame 0.3-1 keV) band fluxes of the three observations and computed the corresponding X-ray variance and error. In Table 3 we summarize the observed-frame full band and the rest-frame 2-8 keV band fluxes, and luminosity obtained from our best-fit models and analysis described in Sect. 3.3.

Table 3.

Best-fit fluxes.

Considering the rest-frame 2–8 keV band fluxes reported in Table 3, we obtained an X-ray variance . The weighted mean luminosity of the three X-ray observations is . This value of is nominally 8 times higher than the average value found for QSOs of similar luminosities by Paolillo et al. (2017) and Shemmer et al. (2017). However, because of the limited monitoring of the X-ray light curve, the formal errors on the excess variance are much smaller than the true uncertainties, which should be assessed with dedicated simulations (see Paolillo et al. 2017). Therefore, we are not able to determine whether the observed variability is still consistent with what is typically observed in luminous QSOs. For instance, in 2003 XMM-Newton may have caught the QSO in a burst period produced by an enhanced accretion episode. Further X-ray observations are needed to determine what is the typical flux state of SDSS J1030 + 0524, increasing the X-ray monitoring of the QSO and adding more data points to the light curve shown in Fig. 6.

4.2. Spectral variability

The results reported in Sect. 3.3 highlighted a significant spectral variation between the XMM-Newton and the Chandra observations. We investigated the possibility that the high X-ray flux level and the steep QSO spectrum measured by XMM-Newton are contaminated by the diffuse X-ray emission seen southward of the QSO in the Chandra image, which is partly included in the 15″-radius extraction region used for the analysis of the XMM-Newton spectrum of the QSO. To this goal, we first considered the portion of diffuse emission falling within r < 15″ from the QSO, extracted its spectrum, and fit it with a power-law model. This spectrum contains about 1/3 of the total photons and flux of the diffuse emission reported in Sect. 3.4. Then, we fit the XMM-Newton spectrum of the QSO with a double power-law model, where the best fit slope and normalization of one of the two power law components were fixed to the best fit values measured for the diffuse emission within r < 15″ from the QSO. As a result of this test, we found that the diffuse component contributes less than 10% to the QSO flux measured by XMM-Newton, and also has negligible impact on its spectral slope. The contamination by the diffuse emission is therefore not able to explain the observed X-ray spectral variability.

Spectral changes are often detected in a sizable fraction of high-z AGN samples (e.g., Paolillo et al. 2002), and in about 50% of the cases such changes correlate with flux variations. In our case, the origin of the flattening of the X-ray spectral slope is unclear due to the relatively poor counting statistics that affects all the X-ray observations. This spectral variability could be related to two possible scenarios. The first one considers a change in the spectral slope related to the variation of the accretion rate with time, that makes the spectrum of the QSO steeper when the accretion rate is higher Sobolewska & Papadakis (2009). To test this scenario, we computed the X-ray Eddington ratio λX,E = L2−10 keV/LE, as defined in Sobolewska & Papadakis (2009), where LE = 1.3 × 1038MBH/M erg s−1 is the Eddington luminosity, and MBH = 1.4 × 109M in SDSS J1020 + 0524 (Kurk et al. 2007; De Rosa et al. 2011). We computed λX,E for the 2017 Chandra () and the 2003 XMM-Newton () observations and found that they are in general agreement with the Γ−λXE relation found by Sobolewska & Papadakis (2009). The second scenario considers the “flattening” effect caused by an occultation event, for instance produced by gas clouds in the broad line region or in the clumpy torus, as sometimes observed in local AGN (Risaliti et al. 2007). As shown in Sect. 3.2, an intervening gas cloud with NH ~ 5.3 x 1023 cm−2 is needed to reproduce the observed “flattening” of a power-law with Γ ~ 2.4, that is the photon index found in the XMM-Newton observation. Removing the absorption term from the power-law plus absorption model (the third one reported in Table 2), we derived a full band flux of , that is 1.7 times lower than the one found with XMM-Newton (with a 3σ significance). We conclude that the spectral and flux variabilities are not related to a simple absorption event, but that this must be coupled with an intrinsic decrease of the source power.

4.3. The multi-wavelength SED

In Fig. 9 we provide the multi-wavelength SED of SDSS J1030 + 0524, which is one of the z ~ 6 QSO best studied in different bands. The XMM-Newton and Chandra values are from this paper; LBT values from Morselli et al. (2014; r, i, z bands); Spitzer and Herschel fluxes from Leipski et al. (2014) (IRAC and MIPS for Spitzer; PACS and SPIRE for Herschel); Scuba values from Priddey et al. (2008; at 1250, 850, and 450 jum); CFHT values from Balmaverde et al. (2017; Y, J bands); H-band and K-band values are from the MUSYC survey (Gawiser et al. 2006); VLA value from Petric et al. (2003; at 1.4 GHz); ALMA flux from Decarli et al. (2018; at 252 GHz). The QSO SED is consistent with the combined SED of lower redshift QSOs, of Richards et al. (2006; green curve in Fig. 9), showing that SDSS J1030 + 0524 has the typical optical properties of lower redshift luminous AGN.

thumbnail Fig. 9.

Multi-wavelength SED of SDSS J1030 + 0524. References to the points are labeled. The green curve is the combined SED for luminous lower redshift QSOs taken from Richards et al. (2006). The drop on the SED of SDSS J1030 + 0524 at λ < 1 μm is produced by the Lyman alpha forest.

Decarli et al. (2018) studied the FIR properties of luminous high-z AGN, based on a sample of 27 QSOs at z ≥ 5.9 observed with ALMA. SDSS J1030 + 0524 is one of the few objects in the sample that is not detected in the [CII] (158 um), and is only marginally detected in the continuum, suggesting a star formation rate (SFR) < 100 M0 yr−1, whereas the average SFR in the sample is a few hundreds M0 yr−1. This may suggest that SDSS J1030 + 0524 is in a more evolved state than the other luminous QSOs at that redshift, that is, it may be in a stage where the star formation in its host is being quenched by its feedback (Hopkins et al. 2008; Lapi et al. 2014).

We use the full-band Chandra flux and the 1450 Å magnitude of the QSO (m 1450Å = 19.7; Bañados et al. 2018) to compute the optical-X-ray power-law slope, defined as(2)

where f2500Å and f2500Å are frame 2 keV and 2500 Å, respectively. The flux density at 2500 Å was derived from the 1450 Å magnitude, assuming a UV-optical power-law slope of 0.5. We found , that is consistent with the mean value, , found for sources at the same redshift (5.9 ≤ z ≤ 6.5: Nanni et al. 2017). The errors on aoX were computed following the numerical method described in SECT. 1.7.3 of Lyons (1991), taking into account the uncertainties in the X-ray counts and an uncertainty of 10% in the 2500 Å flux corresponding to a mean z-magnitude error of 0.1. Previous works have shown that there is a significant correlation between αox and the monochromatic L2500 Å(αox decreases as L2500 Å increases; Steffen et al. 2006; Lusso & Risaliti 2017; Nanni et al. 2017), whereas the apparent dependence of α ox on redshift can be explained by a selection bias (Zamorani et al. 1981; Vignali et al. 2003; Steffen et al. 2006; Shemmer et al. 2006; Just et al. 2007; Lusso et al. 2010; but see also Kelly et al. 2007) The derived value is not consistent with the one found by Nan17 for XMM-Newton data (), that is one of the flattest found among all z ~ 6QSOs. Considering also the already discussed evidence that the XMM-Newton photon index is steeper than the mean population of QSOs at z ~ 6, we conclude that the properties derived from XMM-Newton data do not probably represent the typical status for SDSS J1030 + 0524 (that is probably more similar to that found with Chandra), strengthening the idea that the higher flux measured by XMM-Newton is related to an episodic burst occurred during that observation.

We also checked for the presence of long-term optical variability by comparing the J-band magnitude taken from MUSYC in 2003 (Quad07) with the one taken by WIRCAM in 2015 (Bal17). We used stars in both images to calibrate for the differences in aperture correction and in the filter response, finding a r.m.s. in the distribution of magnitude differences of Δmag ~ 0.04. From 2003 to 2015 the QSO decreased its luminosity by Δ mag ~ 0.1. Therefore, the variation is significant only at 2σ, and if it is on the order of 10% or less as suggested by our measurements, it would have negligible impact on the reported αox values.

Multi-wavelength SED of SDSS J1030 + 0524. References to the points are labeled. The green curve is the combined SED for luminous lower redshift QSOs taken from Richards et al. (2006). The drop on the SED of SDSS J1030 + 0524 at λ < 1 μm is produced by the Lyman alpha forest.

4.4 Origin of the diffuse emission

The origin of the diffuse X-ray emission seen southward of the QSO is far from being clear. We discuss below some possible interpretations.

4.4.1 Unresolved sources or foreground group or cluster

In the 460 arcsec2 region where we find significant excess of X-ray emission (see Fig. 7, left) we do not detect any X-ray pointlike source down to f0.5–2 keV = 10−16 erg s−1 cm−2 and we do not find any sign of AGN activity in any of the MUSE spectra of the optical sources we were able to extract in the same region. An implausibly high surface density of undetected point-like X-ray sources (100 times larger than that expected by the logN-logS relation at f0.5–2 keV = 10−16 erg s−1 cm−2; Luo et al. 2017) would be required to reproduce entirely the observed flux.

The number density of optical sources detected within the extended X-ray emission is similar to that in nearby regions, and neither the angular distribution of galaxies within the region, nor the redshifts measured with MUSE (see Fig. 8, bottom) suggest the presence of a foreground group or cluster. Therefore, we can exclude the presence of a foreground virialized group or cluster as responsible for the diffuse X-ray emission.

4.4.2 Emission from a foreground radio galaxy

A radio-galaxy with FRII morphology was found ~ 40″ southwest of the QSO in a relatively deep VLA observation at 1.4 GHz with ~ 1.5″ esolution (Petric et al. 2003). We reanalyzed the archival VLA data, and derived the radio contours shown in Fig. 8 4. The nucleus of the FRII coincides with a Chandra source detected only above 2 keV (with ~30 net counts), suggesting an extremely obscured nucleus (see Fig. 8, top). The Eastern lobe of the FRII has a total radio flux of 1.7 mJy and falls within the region of diffuse emission, while the Western one is much brighter, with a total radio flux of 24 mJy. No X-ray emission is associated with the Western lobe. A radio jet is also seen running from the radio core to the Eastern lobe. Because of the beamed nature of the jet synchrotron emission, the Eastern lobe is then supposedly the closest to the observer. A detailed analysis of the radio source is beyond the scope of this paper. Here we discuss its basic properties and the likelihood that the diffuse X-ray emission seen southward of SDSS J1030 + 0524 can be associated to it.

The emission at 1.4 GHz in the Eastern lobe is not as extended as the diffuse X-ray emission. The interferometric radio observations were, however, conducted with the “A” configuration at the VLA, which may have filtered out diffuse radio emission on scales of tens of arcsec. As a matter of fact, low surface brightness, low significance radio emission in coincidence with the diffuse X-ray structure and even beyond it may be present survey5. Among the possible processes responsible for X-ray emission in radio lobes, we first investigated synchrotron models by extrapolating to the X-rays the flux densities measured at 1.4 GHz and 150 MHz and using the spectral index ± measured between the two radio bands (fvvα). Because of the widely different angular resolution between TGSS and VLA data (25″ vs. 3.5″ FW H M), we also performed tests using data from the NVSS survey at 1.4 GHz ( ~ 45″ resolution). Unfortunately, the low S/N of the TGSS data, coupled to the complex structure of the source (the Western lobe heavily contaminates the Eastern emission in NVSS data), prevents us from obtaing a robust estimate of the radio spectral index of the Eastern lobe: we measured values in the range α ~ 0.7–0.9, depending on the adopted extraction regions. When extrapolating the radio fluxes over more than 8 dex in frequency, this uncertainty in a produces a wide range of predicted X-ray emission, that can be as high as what we measured with Chandra. Current data are therefore not sufficient to rule out this possibility, but we note, however, that it would be odd to see X-ray synchrotron emission in the Eastern but not in the Western lobe, that is 5–6 times brighter in the radio bands.

Besides synchrotron emission, there are two other possible scenarios to produce X-ray photons within a radio lobe. The first one involves Inverse Compton scattering between the relativistic electrons in the lobe and photons coming from either the cosmic microwave background (IC-CMB; e.g., Erlund et al. 2006), the synchrotron photons in the radio lobe itself (Synchro-Self Compton, SSC), or even the photons emitted from the nucleus of the FRII (Brunetti et al. 1997). The second scenario considers thermal emission produced by diffuse gas shock-heated by the jet (Carilli et al. 2002; Overzier et al. 2005). Unfortunately, we do not have enough photon statistics to distinguish between thermal and non-thermal emission based on the current low S/N X-ray data.

In the IC scenario, the observed hardness ratio of the diffuse X-ray emission would correspond to a power-law with photon index of Γ = 1.6 ± 0.4, which is consistent with X-ray emission from radio lobes ascribed to IC processes (e.g., Smail et al. 2012). However, all the diffuse X-ray emission is associated to the fainter, Eastern radio lobe, whereas, if it was produced by SSC or IC, X-ray emission in the Western lobe, which is > 6 times more powerful in the radio band, would be expected as well. In the case of anisotropic scattering of photons from the FRII nucleus, backward scattering would be favored (Brunetti et al. 1997) and one would then expect the farthest (Western) lobe to be the brightest, which is not the case. Ascribing the observed diffuse X-ray emission to processes associated with the FRII radio lobe is not therefore entirely convincing.

As an alternative, the diffuse X-ray emission may be associated with shocked gas, as is sometimes seen in distant (z ~ 2) radio galaxies that are embedded in gas-rich large scale structures (Overzier et al. 2005). As there is no spectroscopic redshift for the host of the FRII, we first derived a K-band magnitude from the public data of the MUSYC survey (Gawiser et al. 2006), and then estimated a redshift of z ≈ 1–2, based on the K-band magnitude vs redshift relation observed for radio-galaxies (e.g., Willott et al. 2003). Assuming that the X-ray emission is thermal, and that both the FRII radio galaxy and the diffuse gas are embedded in a large scale structure at z = 1.7 (there is a indeed weak evidence for a spike at z = 1.69 in the red-shift distribution of MUSE sources, see the also bottom panel of Fig. 8), we derived a temperature of T ≥ 4 keV for the X-ray emitting gas (using the apec model within XSPEC with 0.3 × solar metal abundances). Some further diffuse X-ray emission can be recognized in Fig. 8 (top) in correspondence of the FRII radio jet and north-west of the FRII core. This emission is at very low S/N, but, if real, it may suggest the presence of other diffuse hot gas in a putative large scale structure. At this redshift, the extension of the diffuse X-ray emission southward of SDSSJ1030 + 0524 would correspond to 240 physical kpc, and its luminosity to L2–10 keV = 3 × 1043 erg s−1.

Assuming that the point-like X-ray emission observed in the core of the FRII traces the accretion luminosity, we can estimate the power carried out by the jet toward the lobe by considering that this is generally equal or larger than the accretion luminosity (Ghisellini et al. 2014). At z = 1.7, using a photon index of Γ = 1.8, the hard X-ray source at the FRII nucleus would be a Compton-thick AGN (N H  ≈ 1.5 × 1024 cm−2) with rest-frame deabsorbed 2–10 keV luminosity of Adopting a bolometric correction of 30, as appropriate for these X-ray luminosities (e.g., Lusso et al. 2011), we estimate a total accretion luminosity, and hence a total jet power of Pjet > 3 × 1045 erg s−1. From the fit to the diffuse X-ray emission we derived a gas density of n ~ 4 x 10−3 cm−3, and hence a total thermal energy of E th  ~ nVkT ~ 5 × 1060 erg, assuming a spherical volume of radius 120 kpc. To deposit such amount of energy in the gas, the jet would have had to be active at that power for at least 100 Myr (even assuming that the 100% of the jet power is transferred to the gas), which is larger than the typical lifetime of FRII jets (~ 1.5 × 107 yr; Bird et al. 2008). Because of the many uncertainties and assumptions, the above computation must be taken with caution. However, it shows that even thermal emission from gas shock-heated by the FRII jet is not a secure interpretation.

4.4.3. QSO feedback and X-ray jets at z = 6.31

Both analytical/numerical models and simulations of early BH formation and growth postulate that a non-negligible fraction of the energy released by early QSOs can couple with the surrounding medium producing significant feedback effects on it (Dubois et al. 2013; Costa et al. 2014; Barai et al. 2018; Gilli et al. 2017). In this scenario, the diffuse X-ray emission may be related to the thermal cooling of environmental gas shock-heated by QSO outflows. This gas can be heated to temperatures higher than 108 K on scales that may extend well beyond the virial radius of the dark matter halo hosting the QSO, and reach hundreds of kpc from the QSO depending on the gas density and host halo mass (e.g., Gilli et al. 2017). Significant X-ray emission in the X-ray band is then expected (Costa et al. 2014). Also, the morphology of the hot gas may be highly asymmetric, depending on the outflow opening angle (Barai et al. 2018) and even be unipolar, depending on the gas distribution in the BH vicinity (Gabor & Bournaud 2013). The morphology of the diffuse X-ray emission suggests that at least part of it may be indeed associated with the QSO. In fact, a “bridge” of soft X-ray emission appears to originate from the QSO and extend into the south-eastern part of the diffuse X-ray structure (see Fig. 7, left-panel).

If the observed diffuse X-ray emission is interpreted as thermally emitting gas at z = 6.3 (we used again the apec model within XSPEC with 0.3 × solar metal abundances), then this should have a temperature of T ≳  10 keV, and extend asymmetrically for about 150 physical kpc from the QSO. This is consistent with the simulations above. The observed X-ray emission would then correspond to a luminosity of . As above, we computed the thermal energy of the X-ray emitting gas by assuming that it is distributed in a sphere of 75 kpc radius. We derived a total thermal energy of ≈ 1061 erg. This is within a factor of two consistent with the predictions of Gilli et al. (2017). In that paper it was calculated that an accreting BH growing to 109M by z = 6, such as that observed in SDSS J1030 + 0524, may deposit ~ 5 × 1060 erg of energy in the surrounding medium through continuous, gas outflows. Furthermore, based on the thermal model fit we obtain a total gas mass of Mgas ~ 1.2 × 1012M and hence a total dark matter halo mass of ≥8 × 1012M, which would be consistent with the idea that early luminous QSOs form in highest peaks of the density field in the Universe, as further supported by the candidate galaxy overdensity measured around SDSS J1030 + 0524 (Morselli et al. 2014; Balmaverde et al. 2017). Again, we note that many caveats apply that are related to our assumptions and uncertainties in the physical parameters derived from low S/N X-ray data, so that the above conclusions are still speculative.

The QSO may also be responsible for the extended X-ray emission through non-thermal radiation mechanisms. In particular, it has been proposed that the emission of jets and lobes in high redshift QSOs may be best probed in the X-rays rather than in the radio band. This is because the energy density of the CMB increases as (1 + z)4, causing inverse Compton scattering to dominate over synchrotron emission the energy losses of relativistic electrons (Ghisellini et al. 2014; Fabian et al. 2014). Despite the large uncertainties arising from the low photon statistics and from the image smoothing process, the bridge of soft X-ray emission originating from the QSO in the Eastern part of the diffuse structure reminds of an X-ray jet that is possibly powering a diffuse structure (X-ray lobe). Following Fabian et al. (2014) and Ghisellini et al. (2014), we considered a simple jet IC-CMB model in which the QSO produces a relativistic jet with power equal to the accretion power (1047 erg s−1). We assumed that: i) the total power of the relativistic electrons injected in the lobe is 10% of the jet power; ii) the magnetic field strength in the lobe is 15 μG (near equipartition is assumed between magnetic field and relativistic particles); iii) the lobe is a sphere of ≈ 50 kpc radius. A fiducial power-law spectrum is also assumed for the injected electrons (see Ghisellini et al. 2014). With the above assumptions, both the X-ray luminosity and hardness ratio of the diffuse emission can be reproduced by the IC scattering of CMB photons by the electrons in the lobe, which in turn may also contribute to some of the observed radio emission through synchrotron radiation. The above computation, despite being admittedly uncertain and relying on strong assumptions, provides another plausible emission mechanism for the observed diffuse X-ray emission.

5. Summary and conclusions

In this paper we have reported on the ~ 500 ks Chandra observation of the QSO SDSS J1030 + 0524. This is the deepest X-ray observation ever achieved for a z ~ 6 QSO. Our main results are the following:

  • The QSO has been detected with ~ 125 net counts in the full band with no evidence of either significant spectral or flux variability during the Chandra observations. The spectrum is well fit by a single power-law with , that is consistent with the mean value found for luminous AGN at any redshift. No evidence is found for significant absorption (), nor for other additional spectral features.

  • A comparison between the QSO X-ray spectral properties in our Chandra data with those obtained from a past XMM-Newton observation (Farrah et al. 2004; Nanni et al. 2017) revealed that the QSO significantly varied. The full band flux decreased by a factor of 2.5 from the XMM-Newton to the Chandra observations while the spectrum became flatter (ΔΓ≈−0.6). We verified that these variations are not related to calibration issues. We discussed the possibility that the hardening of the spectral slope is intrinsic and related to variations of the accretion rate. As an alternative, a variation of the obscuration level along the line of site (with Γ fixed at the XMM-Newton value) is not sufficient to explain alone the observed variations. However, because of the limited monitoring of the X-ray light curve and the poor counting statistics, we were not able to disentangle between the different scenarios.

  • We provided the SED of SDSS J1030 + 0524, that is one of the best sampled for a z > 6 QSO. The SED is consistent with the mean SED of luminous AGN at lower redshift, but it differs in the FIR and sub-millimeter bands with that found for other QSOs at z ~ 6. This difference may suggest that SDSS J1030 + 0524 is in a more evolved state (i.e., with quenched SFR) than the other luminous QSOs at that redshift. We also computed the optical-X-ray power-law slope for the Chandra observation, finding . Comparisons between the α ox and the photon index found by XMM-Newton with those found by Chandra, suggest that the properties derived from XMM-Newton data do not probably represent the typical status for SDSS J1030 + 0524, strengthening the idea that the higher flux measured by XMM-Newton is related to an episodic burst occurred during the XMM-Newton observation.

  • We detected significant diffuse X-ray emission that extends for 30″ × 20″ southward the QSO, with a S/N = 5.9, hardness ratio of and soft band flux of We verified that an implausibly high surface density of undetected point-like X-ray sources would be required to reproduce entirely the observed flux. Based on HST and MUSE date, we also excluded the presence of foreground a galaxy group or cluster. We discussed different scenarios for the origin of this diffuse emission. The first scenario considers the contribution of a radio lobe from a foreground FRII source that could emit X-ray photons via synchrotron or inverse Compton processes. The large uncertainties on the radio measurements and the low X-ray statistics prevented us from excluding or confirming either possibility. However, the absence of X-ray emission from the brightest radio lobe poses challenges to any scenario involving non-thermal processes, as we would expect stronger X-ray emission in the other lobe, which is > 6 times more powerful in the radio band but has no X-ray diffuse emission. Alternatively, as high-z radio-galaxies are often found in non-virialized large scale structures, the diffuse X-ray may arise from a reservoir of gas in this structure shock-heated by the FRII jet.

    In the second scenario, the diffuse X-ray emission may probe the feedback produced by SDSS J1030 + 0524 on its close environment. In that case, if the diffuse emission is thermal, the gas should have a temperature of T ≥ 10 keV, and extend asymmetrically for about 150 physical kpc from the QSO, in agreement with simulations of early BH formation. In addition, supposing that SDSS J1030 + 0524 is producing a relativistic jet, this would be best probed in the X-rays rather than in the radio band, as the electron energy losses would be dominated by IC scattering of the strong CMB photon field, rather than by synchrotron emission. The energetics, scales and spectral hardness of the observed X-ray emission would also be consistent with this interpretation.

We conclude that SDSS J1030 + 0524 is one of the best objects to study the spectral properties and the environment of high redshift AGN. New X-ray observations are needed to check the QSO light-curve and to constrain the origin of the extended emission via spectral analysis. In particular, for the next future Chandra and XMM-Newton monitoring of SDSS J1030 + 0524 would provide additional information on the QSO variability and the origin of the diffuse emission seen southward the QSO. This will greatly help developing the science cases for future X-ray missions, such as Athena and Lynx, that will shed new light on the high redshift frontier.

Acknowledgments

We acknowledge useful discussion with S. Ettori, G. Brunetti, G. Ghisellini, R. Decarli, E. Torresi, E. Lusso, S. Gallerani. We thank the anonymous referee for his/her useful comments and suggestions that improved the quality of this work. We acknowledge financial contribution from the agreement ASI-INAF n.2017-14-H.O and ASI-INAF I/037/12/0. The scientific results reported in this article are based on observations made by the Chandra X-ray Observatory. This work made use of data taken under the ESO program ID 095.A-0714(A). FC acknowledges funding from the INAF PRIN-SKA 2017 program 1.05.01.88.04.


1

Computed as:, where σRA and σDec are the errors on Right Ascension and Declination, respectively.

2

The “Compton-reflection hump” is radiation from the hot corona that is reprocessed by the accretion disk, and peaks at ~ 30 keV.

3

Warmabs can be used within XSPEC to fit to observed spectra the results of XSTAR, a software package for calculating the physical conditions and emission spectra of photoionized gas (Kallman & Bautista 2001).

4

Our data reduction was tuned to achieve a lower resolution (FWHM ~ 3.5″) to maximize the detection efficiency of the diffuse region emission. The sensitivity limit of our image is f1.4 GHz ~ 70 µJy/beam (3σ).

References

  1. Agarwal, B., Dalla Vecchia, C., Johnson, J. L., Khochfar, S., & Paardekooper, J.-P. 2014, MNRAS, 443, 648 [NASA ADS] [CrossRef] [Google Scholar]
  2. Ai, Y., Dou, L., Fan, X., et al. 2016, ApJ, 823, L37 [NASA ADS] [CrossRef] [Google Scholar]
  3. Arnaud, K. A. 1996, in Astronomical Data Analysis Software and Systems V, eds. G. H. Jacoby, & J. Barnes, ASP Conf. Ser., 101, 17 [NASA ADS] [Google Scholar]
  4. Balmaverde, B., Gilli, R., Mignoli, M., et al. 2017, A&A, 606, A23 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  5. Bañados, E., Venemans, B. P., Walter, F., et al. 2013, ApJ, 773, 178 [NASA ADS] [CrossRef] [Google Scholar]
  6. Bañados, E., Venemans, B. P., Decarli, R., et al. 2016, ApJS, 227, 11 [NASA ADS] [CrossRef] [Google Scholar]
  7. Bañados, E., Venemans, B. P., Mazzucchelli, C., et al. 2018, Nature, 553, 473 [NASA ADS] [CrossRef] [Google Scholar]
  8. Barai, P., Gallerani, S., Pallottini, A., et al. 2018, MNRAS, 473, 4003 [NASA ADS] [CrossRef] [Google Scholar]
  9. Barnett, R., Warren, S. J., Banerji, M., et al. 2015, A&A, 575, A31 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  10. Bennett, C. L., Larson, D., Weiland, J. L., et al. 2013, ApJS, 208, 20 [Google Scholar]
  11. Bird, J., Martini, P., & Kaiser, C. 2008, ApJ, 676, 147 [NASA ADS] [CrossRef] [Google Scholar]
  12. Brandt, W. N., Schneider, D. P., Fan, X., et al. 2002, ApJ, 569, L5 [NASA ADS] [CrossRef] [Google Scholar]
  13. Brunetti, G., Setti, G., & Comastri, A. 1997, A&A, 325, 898 [NASA ADS] [Google Scholar]
  14. Calura, F., Gilli, R., Vignali, C., et al. 2014, MNRAS, 438, 2765 [NASA ADS] [CrossRef] [Google Scholar]
  15. Carilli, C. L., Harris, D. E., Pentericci, L., et al. 2002, ApJ, 567, 781 [NASA ADS] [CrossRef] [Google Scholar]
  16. Cash, W. 1979, ApJ, 228, 939 [NASA ADS] [CrossRef] [Google Scholar]
  17. Costa, T., Sijacki, D., Trenti, M., & Haehnelt, M. G. 2014, MNRAS, 439, 2146 [NASA ADS] [CrossRef] [Google Scholar]
  18. Decarli, R., Walter, F., Venemans, B. P., et al. 2018, ApJ, 854, 97 [NASA ADS] [CrossRef] [Google Scholar]
  19. De Rosa, G., Decarli, R., Walter, F., et al. 2011, ApJ, 739, 56 [NASA ADS] [CrossRef] [Google Scholar]
  20. Dubois, Y., Pichon, C., Devriendt, J., et al. 2013, MNRAS, 428, 2885 [Google Scholar]
  21. Erlund, M. C., Fabian, A. C., Blundell, K. M., Celotti, A., & Crawford, C. S. 2006, MNRAS, 371, 29 [NASA ADS] [CrossRef] [Google Scholar]
  22. Fabian, A. C., Walker, S. A., Celotti, A., et al. 2014, MNRAS, 442, L81 [NASA ADS] [CrossRef] [Google Scholar]
  23. Fan, X., Narayanan, V. K., Lupton, R. H., et al. 2001, AJ, 122, 2833 [NASA ADS] [CrossRef] [Google Scholar]
  24. Fan, X., Strauss, M. A., Richards, G. T., et al. 2006, AJ, 131, 1203 [NASA ADS] [CrossRef] [Google Scholar]
  25. Farrah, D., Priddey, R., Wilman, R., Haehnelt, M., & McMahon, R. 2004, ApJ, 611, L13 [NASA ADS] [CrossRef] [Google Scholar]
  26. Gabor, J. M., & Bournaud, F. 2013, MNRAS, 434, 606 [NASA ADS] [CrossRef] [Google Scholar]
  27. Gallerani, S., Fan, X., Maiolino, R., & Pacucci, F. 2017a, PASA, 34, e022 [NASA ADS] [CrossRef] [Google Scholar]
  28. Gallerani, S., Zappacosta, L., Orofino, M. C., et al. 2017b, MNRAS, 467, 3590 [NASA ADS] [CrossRef] [Google Scholar]
  29. Gawiser, E., van Dokkum, P. G., Herrera, D., et al. 2006, ApJS, 162, 1 [NASA ADS] [CrossRef] [Google Scholar]
  30. Gehrels, N. 1986, ApJ, 303, 336 [NASA ADS] [CrossRef] [Google Scholar]
  31. Ghisellini, G., Tavecchio, F., Maraschi, L., Celotti, A., & Sbarrato, T. 2014, Nature, 515, 376 [NASA ADS] [CrossRef] [Google Scholar]
  32. Gilli, R., Calura, F., D’Ercole, A., & Norman, C. 2017, A&A, 603, A69 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  33. Hopkins, P. F., Hernquist, L., Cox, T. J., & Kereš, D. 2008, ApJS, 175, 356 [NASA ADS] [CrossRef] [Google Scholar]
  34. Husband, K., Bremer, M. N., Stanway, E. R., et al. 2013, MNRAS, 432, 2869 [NASA ADS] [CrossRef] [Google Scholar]
  35. Just, D. W., Brandt, W. N., Shemmer, O., et al. 2007, ApJ, 665, 1004 [Google Scholar]
  36. Kalberla, P. M. W., Burton, W. B., Hartmann, D., et al. 2005, A&A, 440, 775 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  37. Kallman, T., & Bautista, M. 2001, ApJS, 133, 221 [NASA ADS] [CrossRef] [Google Scholar]
  38. Kelly, B. C., Bechtold, J., Siemiginowska, A., Aldcroft, T., & Sobolewska, M. 2007, ApJ, 657, 116 [NASA ADS] [CrossRef] [Google Scholar]
  39. Kurk, J. D., Walter, F., Fan, X., et al. 2007, ApJ, 669, 32 [NASA ADS] [CrossRef] [Google Scholar]
  40. Lanzuisi, G., Civano, F., Elvis, M., et al. 2013a, MNRAS, 431, 978 [NASA ADS] [CrossRef] [Google Scholar]
  41. Lanzuisi, G., Civano, F., Marchesi, S., et al. 2013b, ApJ, 778, 62 [NASA ADS] [CrossRef] [Google Scholar]
  42. Lapi, A., Raimundo, S., Aversa, R., et al. 2014, ApJ, 782, 69 [NASA ADS] [CrossRef] [Google Scholar]
  43. Leipski, C., Meisenheimer, K., Walter, F., et al. 2014, ApJ, 785, 154 [NASA ADS] [CrossRef] [Google Scholar]
  44. Luo, B., Brandt, W. N., Xue, Y. Q., et al. 2017, ApJS, 228, 2 [Google Scholar]
  45. Lusso, E., & Risaliti, G. 2017, A&A, 602, A79 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  46. Lusso, E., Comastri, A., Vignali, C., et al. 2010, A&A, 512, A34 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  47. Lusso, E., Comastri, A., Vignali, C., et al. 2011, A&A, 534, A110 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  48. Lyons, L. 1991, in A Practical Guide to Data Analysis for Physical Science Students (Cambridge, UK: Cambridge University Press) [CrossRef] [Google Scholar]
  49. Madau, P., & Rees, M. J. 2001, ApJ, 551, L27 [NASA ADS] [CrossRef] [Google Scholar]
  50. Madau, P., Haardt, F., & Dotti, M. 2014, ApJ 784 L38 [NASA ADS] [CrossRef] [Google Scholar]
  51. Matsuoka, Y., Onoue, M., Kashikawa, N., et al. 2016, ApJ, 828, 26 [NASA ADS] [CrossRef] [Google Scholar]
  52. Mazzucchelli, C., Bañados, E., Decarli, R., et al. 2017, ApJ, 834, 83 [NASA ADS] [CrossRef] [Google Scholar]
  53. Moretti, A., Ballo, L., Braito, V., et al. 2014, A&A, 563, A46 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  54. Morselli, L., Mignoli, M., Gilli, R., et al. 2014, A&A, 568, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  55. Mortlock, D. J., Warren, S. J., Venemans, B. P., et al. 2011, Nature, 474, 616 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  56. Nandra, K., George, I. M., Mushotzky, R. F., Turner, T. J., & Yaqoob, T. 1997, ApJ, 476, 70 [NASA ADS] [CrossRef] [Google Scholar]
  57. Nanni, R., Vignali, C., Gilli, R., Moretti, A., & Brandt, W. N. 2017, A&A, 603, A128 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  58. Overzier, R. A., Harris, D. E., Carilli, C. L., et al. 2005, A&A, 433, 87 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  59. Overzier, R. A., Guo, Q., Kauffmann, G., et al. 2009, MNRAS, 394, 577 [NASA ADS] [CrossRef] [Google Scholar]
  60. Page, M. J., Simpson, C., Mortlock, D. J., et al. 2014, MNRAS, 440, L91 [NASA ADS] [CrossRef] [Google Scholar]
  61. Paolillo, M., Fabbiano, G., Peres, G., & Kim, D.-W. 2002, ApJ, 565, 883 [NASA ADS] [CrossRef] [Google Scholar]
  62. Paolillo, M., Papadakis, I., Brandt, W. N., et al. 2017, MNRAS, 471, 4398 [NASA ADS] [CrossRef] [Google Scholar]
  63. Papadakis, I. E. 2004, MNRAS, 348, 207 [NASA ADS] [CrossRef] [Google Scholar]
  64. Petric, A. O., Carilli, C. L., Bertoldi, F., et al. 2003, AJ, 126, 15 [NASA ADS] [CrossRef] [Google Scholar]
  65. Pezzulli, E., Valiante, R., Orofino, M. C., et al. 2017, MNRAS, 466, 2131 [NASA ADS] [CrossRef] [Google Scholar]
  66. Priddey, R. S., Isaak, K. G., McMahon, R. G., Robson, E. I., & Pearson, C. P. 2003, MNRAS, 344, L74 [NASA ADS] [CrossRef] [Google Scholar]
  67. Priddey, R. S., Ivison, R. J., & Isaak, K. G. 2008, MNRAS, 383, 289 [NASA ADS] [CrossRef] [Google Scholar]
  68. Quadri, R., Marchesini, D., van Dokkum, P., et al. 2007, AJ, 134, 1103 [NASA ADS] [CrossRef] [Google Scholar]
  69. Reed, S. L., McMahon, R. G., Martini, P., et al. 2017, MNRAS, 468, 4702 [NASA ADS] [CrossRef] [Google Scholar]
  70. Richards, G. T., Lacy, M., Storrie-Lombardi, L. J., et al. 2006, ApJS, 166, 470 [NASA ADS] [CrossRef] [Google Scholar]
  71. Risaliti, G., Elvis, M., Fabbiano, G., et al. 2007, ApJ, 659, L111 [NASA ADS] [CrossRef] [Google Scholar]
  72. Shemmer, O., Brandt, W. N., Schneider, D. P., et al. 2006, ApJ, 644, 86 [NASA ADS] [CrossRef] [Google Scholar]
  73. Shemmer, O., Brandt, W. N., Paolillo, M., et al. 2017, ApJ, 848, 46 [NASA ADS] [CrossRef] [Google Scholar]
  74. Simpson, C., Mortlock, D., Warren, S., et al. 2014, MNRAS, 442, 3454 [NASA ADS] [CrossRef] [Google Scholar]
  75. Smail, I., Blundell, K. M., Lehmer, B. D., & Alexander, D. M. 2012, ApJ, 760, 132 [NASA ADS] [CrossRef] [Google Scholar]
  76. Sobolewska, M. A., & Papadakis, I. E. 2009, MNRAS, 399, 1597 [NASA ADS] [CrossRef] [Google Scholar]
  77. Springel, V., White, S. D. M., Jenkins, A., et al. 2005, Nature, 435, 629 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  78. Steffen, A. T., Strateva, I., Brandt, W. N., et al. 2006, AJ, 131, 2826 [NASA ADS] [CrossRef] [Google Scholar]
  79. Stiavelli, M., Djorgovski, S. G., Pavlovsky, C., et al. 2005, ApJ, 622, L1 [NASA ADS] [CrossRef] [Google Scholar]
  80. Tang, J.-J., Goto, T., Ohyama, Y., et al. 2017, MNRAS, 466, 4568 [NASA ADS] [Google Scholar]
  81. Tombesi, F., Cappi, M., Reeves, J. N., et al. 2013, MNRAS, 430, 1102 [Google Scholar]
  82. Tombesi, F., Meléndez, M., Veilleux, S., et al. 2015, Nature, 519, 436 [Google Scholar]
  83. Turner, T. J., George, I. M., Nandra, K., & Turcan, D. 1999, ApJ, 524, 667 [NASA ADS] [CrossRef] [Google Scholar]
  84. Valiante, R., Schneider, R., & Volonteri, M. 2016, PASA, 33, e032 [NASA ADS] [CrossRef] [Google Scholar]
  85. Venemans, B. P., Findlay, J. R., Sutherland, W. J., et al. 2013, ApJ, 779, 24 [NASA ADS] [CrossRef] [Google Scholar]
  86. Venemans, B. P., Walter, F., Zschaechner, L., et al. 2016, ApJ, 816, 37 [NASA ADS] [CrossRef] [Google Scholar]
  87. Venemans, B. P., Walter, F., Decarli, R., et al. 2017, ApJ, 845, 154 [NASA ADS] [CrossRef] [Google Scholar]
  88. Vignali, C., Brandt, W. N., Schneider, D. P., et al. 2003, AJ, 125, 2876 [NASA ADS] [CrossRef] [Google Scholar]
  89. Vignali, C., Brandt, W. N., Schneider, D. P., & Kaspi, S. 2005, AJ, 129, 2519 [NASA ADS] [CrossRef] [Google Scholar]
  90. Vito, F., Brandt, W. N., Yang, G., et al. 2018, MNRAS, 473, 2378 [NASA ADS] [CrossRef] [Google Scholar]
  91. Volonteri, M., Lodato, G., & Natarajan, P. 2008, MNRAS, 383, 1079 [NASA ADS] [CrossRef] [Google Scholar]
  92. Volonteri, M., Habouzit, M., Pacucci, F., & Tremmel, M. 2016, in Galaxies at High Redshift and Their Evolution Over Cosmic Time, ed. S. Kaviraj, IAU Symp. 319, 72 [NASA ADS] [Google Scholar]
  93. Willott, C. J., Rawlings, S., Jarvis, M. J., & Blundell, K. M. 2003, MNRAS, 339, 173 [NASA ADS] [CrossRef] [Google Scholar]
  94. Willott, C. J., Delorme, P., Reylé, C., et al. 2010, AJ, 139, 906 [NASA ADS] [CrossRef] [Google Scholar]
  95. Wu, X.-B., Wang, F., Fan, X., et al. 2015, Nature, 518, 512 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  96. Yang, J., Fan, X., Wu, X.-B., et al. 2017, AJ, 153, 184 [NASA ADS] [CrossRef] [Google Scholar]
  97. Zamorani, G., Henry, J. P., Maccacaro, T., et al. 1981, ApJ, 245, 357 [NASA ADS] [CrossRef] [Google Scholar]

All Tables

Table 1.

Data information on J1030 + 0524.

Table 2.

Best-fit results of the Chandra data.

Table 3.

Best-fit fluxes.

All Figures

thumbnail Fig. 1.

Full-band (0.5–7 keV) Chandra ACIS-I image of SDSS J1030 + 0524. The red circle represents our extraction region (2″ radius). The grid separation is 5″ and the cutout spans 20″ × 20″ on the sky. Units on the colorbar are counts per pixel.

In the text
thumbnail Fig. 2.

Count rate of SDSS J1030+0524 in the three X-ray bands (full in the top, soft in the middle, and hard in the bottom panel) extracted from the ten Chandra observations vs. the days since the first observation. Errors are reported at the 1σ level. The red solid lines represent the weighted mean.

In the text
thumbnail Fig. 3.

Hardness ratio of SDSS J1030 + 0524 in the ten Chandra observations vs. the days since the first observation. Errors are reported at the 1 σ level. The red solid line represents the weighted mean.

In the text
thumbnail Fig. 4.

X-ray spectrum of SDSS J1030 + 0524 fitted with a power-law model (). In the bottom panel we report the residuals [(data–model)/error]. For display purposes we adopted a minimum binning of ten counts per bin.

In the text
thumbnail Fig. 5.

Comparison between the 2003 XMM-Newton (in blue) and 2017 Chandra (in red) spectrum of SDSS J1030 + 0524. Both spectra have been corrected for the effective response. The XMM-Newton spectrum is the average of the three EPIC cameras (with S/N weighting). The left panel shows the observed-frame spectra, while the right panel shows the rest-frame one.

In the text
thumbnail Fig. 6.

Long-term X-ray light curve of SDSS J1030 + 0524 in the 0.5 7 keV band. Errors are reported at the 1σ level.

In the text
thumbnail Fig. 7.

Left panel: Chandra 0.5–2 keV image (2′ × 2′), smoothed with csmooth (see text) and centered at the QSO position. North is up and east is to the left. Green circles mark point-like sources X-ray detected in the soft band. Units on the colorbar are counts per pixel. Central panel: 2′ × 2′ image of the exposure map computed at 1.4 keV and centered at the QSO position. The green 460 arcsec2 region is the one used to extract the net counts of the diffuse emission southward on the QSO in the un-smoothed image, and it lays in the most sensitive peak of the exposure map. The four green dashed circles are the regions used to extract the background. Units on the colorbar are cm2 s per pixel. Right panel: XMM-Newton-pn image in the 0.5–2 keV band (2′ × 2′), centered at the QSO position. Units on the colorbar are counts per pixel.

In the text
thumbnail Fig. 8.

Top panel: smoothed Chandra 0.5 – 7 keV image of the 1.5 × 0.8 arcmin2 field around SDSS J1030 + 0524 and the nearby FRII radio galaxy. Radio contours at 1.4 GHz are shown in white. Contour levels are a geometric progression in the square root of two starting at 60μJy. The green polygon marks the region of diffuse X-ray emission southward of the QSO. Units on the colorbar are counts per pixel. Bottom panel: 0.7′ × 0.7′ HST image in the H-band. Circles are centered at the position of MUSE detected sources. The white circle marks SDSS J1030 + 0524. Cyan circles mark sources for which a redshift was measured, yellow circles are stars, green circles mark sources for which no redshift was measured. Magenta contours mark the emission of radio sources at 1.4 GHz. The radio lobe of a FRII radio galaxy (Petric et al. 2003) falls within the region of X-ray diffuse emission (green polygon). In both panels, another radio source is visible at the edge of the diffuse X-ray emission (6″ south-west the QSO). In all images north is up and east is to the left.

In the text
thumbnail Fig. 9.

Multi-wavelength SED of SDSS J1030 + 0524. References to the points are labeled. The green curve is the combined SED for luminous lower redshift QSOs taken from Richards et al. (2006). The drop on the SED of SDSS J1030 + 0524 at λ < 1 μm is produced by the Lyman alpha forest.

In the text

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.