EDP Sciences logo
Subscriber Authentication Point
Search
Menu
Home All issues Volume 654 (October 2021) A&A, 654 (2021) A32 Full HTML
Free Access
Issue
A&A
Volume 654, October 2021
Article Number A32
Number of page(s) 7
Section Extragalactic astronomy
DOI https://doi.org/10.1051/0004-6361/202141474
Published online 07 October 2021

© ESO 2021

1. Introduction

Active galactic nuclei (AGN) are extremely luminous extragalactic objects that are located at the center of their host galaxies and are powered by the accretion of matter onto a supermassive black hole (SMBH). AGN are now considered a major force in shaping their host galaxy during its evolution because many of the galaxy properties are correlated with the mass of the central SMBH (e.g., Ferrarese & Merritt 2000; Häring & Rix 2004; Gaspari et al. 2019). Outflows are one of the main mechanisms by which the black hole is thought to transport its energy to large distances (e.g., King & Pounds 2015; Fiore et al. 2017; Cicone et al. 2018; Laha et al. 2021). These winds are commonly found at many wavelengths in AGN spectra (e.g., Gibson et al. 2009; Harrison et al. 2014; Cicone et al. 2014; Vietri et al. 2018).

In the X-ray band, absorption features are the typical signature of outflows. Low-ionization absorbers (log(ξ/erg cm s−1)≲2), characterized by a low outflow velocity (vout ∼ 100 − 1000 km s−1), are found in about 65% of the soft X-ray (E ≲ 2 keV) spectra of nearby AGN, and they are often known as warm absorbers (WAs, e.g., Halpern 1984; Blustin et al. 2005; McKernan et al. 2007; Laha et al. 2014). More than 30% of the X-ray detected AGN show evidence of ultra-fast outflows (UFOs, e.g., Chartas et al. 2002; Pounds et al. 2003a,b; Braito et al. 2007; Tombesi et al. 2010, 2015; Gofford et al. 2013; Nardini et al. 2015; Ballo et al. 2015). UFOs are extremely highly ionized (log(ξ/erg cm s−1)∼3 − 6) absorbers that are detected as blueshifted absorption lines of Fe XXV and XXVI, with typical outflow velocity of vout ∼ 0.1c, but are capable of reaching near-relativistic values of ∼0.5c (e.g., Reeves et al. 2018; Luminari et al. 2021).

While UFOs are extremely variable (e.g., Matzeu et al. 2017), variability of soft X-ray ionized absorbers is found less frequently. In some cases, some sources are found in a state with diminished X-ray flux due to an increase in column density of the obscuring medium. The absorbers are sometimes found to be persistent for about a decade (e.g., NGC 5548, Kaastra et al. 2014). In other cases, the obscurer has a much shorter variability timescale (e.g., Severgnini et al. 2015; Matzeu et al. 2016; Mehdipour et al. 2017; Middei et al. 2020). In a few other cases, a source was found in a higher flux state than usual because of a diminished obscuring power of the WA (e.g., Braito et al. 2014). All these cases suggest a clumpy structure for the ionized and possibly outflowing absorbers around the AGN, which is predicted by duty-cycle theoretical models such as chaotic cold accretion (CCA, e.g., Gaspari et al. 2013; Gaspari & Sądowski 2017). This clumpy material can be part of the multiphase rain condensing out of the hot halo, which then eventually contributes to the feeding component alongside the feedback channel. Feeding and feedback processes are indeed expected to be tightly self-regulated over cosmic time and over nine orders of magnitude in spatial scale (see, e.g., Gaspari et al. 2020, for a review).

The type 1 quasar PG 1114+445 (z = 0.144, Hewett & Wild 2010) has an estimated black hole mass of log(M/M⊙) ≃ 8.8 and bolometric luminosity of log(Lbol/erg s−1) ≃ 45.7 (Shen et al. 2011). This means that the source is accreting with an Eddington ratio of log(Lbol/LEdd) ≃ − 1.14. The source is well known to host multiple absorbers in the UV and X-ray bands. An UV observation taken in 1996 with the Faint Object Spectrograph (FOS) on board the Hubble Space Telescope (HST) was able to detect Lyα and C IV absorption lines in the spectrum of this quasar. For these lines, an outflowing velocity of ∼530 km s−1 was measured (Mathur et al. 1998). The observation was simultaneous with an Advance Satellite for Cosmology and Astrophysics (ASCA) pointing that highlighted an ionized WA and showed marginal evidence of an absorption line at E ∼ 7.3 keV (George et al. 1997). The UV absorption lines and the X-ray warm absorber have similar ionizations and therefore they likely trace the same material (Mathur et al. 1998).

The source was again observed in 2002 with XMM-Newton (Jansen et al. 2001), revealing that the absorption complex consisted of two WA layers (Ashton et al. 2004; Piconcelli et al. 2005). A further XMM-Newton campaign of 11 observations was performed in 2010. The data from this campaign, together with a reanalysis of the 2002 observation (Serafinelli et al. 2019, hereafter Paper I), found that one of the two absorbers has typical WA parameters, that is, column density NH ≃ 7.6 × 1021 cm−2 and ionization parameter log(ξ/erg cm s−1) ≃ 0.35 with velocity below the energy resolution, that are likely associated with the UV absorber (Mathur et al. 1998). The second absorber shares very similar parameters with the WA (NH ∼ 3 × 1021 cm−2 and log(ξ/erg cm s−1)∼0.5), with the exception of the outflow velocity, which is high enough to be detected with the EPIC cameras on board XMM-Newton (vout/c = 0.12 ± 0.03). Low-ionization fast outflows were also found in other sources (e.g., Longinotti et al. 2015; Pounds et al. 2016; Reeves et al. 2020). In addition, a high-ionization UFO (vout/c = 0.15 ± 0.04, consistent with the fast absorber) was found in three spectra. This evidence led to the interpretation that a UFO pushes and entrains the interstellar medium of the host galaxy to a comparable velocity, producing a so-called entrained ultra-fast outflow (E-UFO, Paper I).

We analyze the data taken during a recent Neil Gehrels Swift Observatory (hereafter Swift) X-Ray Telescope (XRT, Gehrels et al. 2004) campaign, which was proposed to study the possible variability of the absorbers found in Paper I, on timescales from days to months. In Sect. 2 we describe how the data were prepared for the analysis. In Sect. 3 we analyze the X-ray amplitude and spectral variability, and in Sect. 4 we present detailed spectroscopy of the source. In Sect. 5 we analyze the variability of the X-ray-to-UV ratio. We summarize and discuss our results in Sect. 6.

Throughout the paper, we adopt the following cosmology: Ωm = 0.3, ΩΛ = 0.7, and H0 = 70 km s−1 Mpc−1. All uncertainties are reported at a 90% confidence level.

2. Observations and data reduction

The observations were performed during Swift Cycle 15 (PI: Serafinelli) from March to July 2019 (OBSID 00011004001 to 00011004018), and then three additional observations were taken 15 months later, in December 2020 (OBSID 00011004019 to 00011004021). The observations were spaced by 7, 15, and 30 days in order to analyze possible short-time variability within the campaign. A further archival observation, OBSID 00089058001, not part of our campaign, taken on October 26, 2020, is also considered here. Most observations are about ∼4 − 5 ks long, and some observations are only ∼2 ks long. The list of Swift-XRT observations is shown in Table 1. The total exposure time is ∼90 ks.

Table 1.

X-ray observations taken by Swift-XRT, with OBSID, observation date, and exposure.

For each Swift-XRT observation, the source and background spectra were extracted using the HEASOFT task XSELECT. The source spectrum was extracted from a circular area of 40″ radius around the object, while the background spectrum was extracted from two source-free circular areas of 40″ radius each in the proximity of the source. Ancillary files were produced using the XRTMKARF task, while the response was taken from the HEASOFT CALDB repository.

Each Swift observation provided an UltraViolet and Optical Telescope (UVOT) pointing, with a single filter, centered on the source. The source monochromatic flux was measured within circular apertures of 5″ radius, while the background was extracted from an annulus region, centered on the source, with an internal radius of 15″ and an external radius of 40″ using the UVOTSOURCE task.

3. X-ray variability

The Swift-XRT count rate light curve of PG 1114+445 in the 0.5−10 keV full band is shown in Fig. 1. The average count rate is ∼1.5 × 10−2 cts s−1. We compare the current data set with archival X-ray pointings from ASCA, taken in 1996 and XMM-Newton, taken in 2002 and 2010. The products of the ASCA observation were downloaded from the Tartarus database1 (Turner et al. 2001), while details of the XMM-Newton data reduction can be found in Paper I. In order to compare data taken with different telescopes, we converted the X-ray count rates into fluxes. All fluxes were obtained with the web tool WebPIMMS2, adopting a simple AGN X-ray spectrum, composed of a typical Γ = 1.9 power law (e.g., Corral et al. 2011; Serafinelli et al. 2017), at the redshift of the source (z = 0.144), with Galactic absorption (NH, Gal = 1.87 × 1020 cm−2, HI4PI Collaboration 2016). The full band fluxes as a function of the observation time are plotted in the left panel of Fig. 2, where an evident decrease in X-ray flux can already be identified by eye.

thumbnail Fig. 1.

Swift-XRT count rates in the energy band E = 0.5−10 keV. The magenta star represents OBSID 00089058001, which was not part of our campaign.

The possible presence of obscuration can be investigated by the use of the hardness ratio (HR), which we define as (H − S)/(H + S), where S is the count rate in the soft band (E = 0.5−2 keV) and H is the count rate in the hard band (E = 2−10 keV). In order to deal with comparable HRs, which are typically different due to the different responses of the instruments, we converted all the count rates into those of Swift-XRT using WebPIMMS and again assuming a simple absorbed Γ = 1.9 power law at z = 0.144, with NH, Gal = 1.87 × 1020 cm−2. As shown in Fig. 2 (right panel), the spectrum in the Swift-XRT observations hardens on average, whereas the HR values during the whole Swift-XRT campaign remain approximately consistent within the error bars. During the observation performed on 26 October 2020, that is, the magenta point in Figs. 1 and 2, the flux was higher than during the remainder of the 2019-2020 campaign. However, the HR is still consistent with the value of the other observations of the Swift campaign. That the HR is higher than in the XMM-Newton observations suggests that the lower flux state is not due to a change in the primary continuum, but likely to a variation of the column density of one or more of the known absorbers found in Paper I.

thumbnail Fig. 2.

Full-band (E = 0.5−10 keV) light curve using ASCA data (blue triangle), XMM-Newton (red squares), our Swift-XRT campaign (black circles), and additional archival Swift-XRT observation (magenta star; left). Time variability of the hardness ratio HR = (H − S)/(H + S), where S and H are the 0.5 − 2 and 2 − 10 keV count rates, respectively (left). The spectrum in the most recent data is noticeably harder. The count rates of the ASCA and EPIC-pn observations were converted into Swift-XRT count rates following the procedure described in Sect. 3.

4. X-ray spectral analysis

Because all Swift-XRT observations have only a few photon counts, we combined all the observations into a single spectrum using the HEASOFT task ADDSPEC in order to maximize the signal-to-noise ratio. We binned the combined spectrum, which consists of ∼1300 net counts, to have at least ten counts per energy channel.

All spectral fits were performed using XSPEC v12.10 (Arnaud 1996), adopting a C-statistic, because of the low number of photons for each bin (e.g., Kaastra 2017). We first attempted to fit the combined spectrum with a simple model consisting of a power-law component and an emission line, with Galactic absorption characterized by a fixed column density NH, Gal = 1.87 × 1020 cm−2 (HI4PI Collaboration 2016): Tbabs × (powerlaw + zgauss). The fit results in an unacceptable C-statistic: C/d.o.f. = 229/106, where d.o.f. is the degree of freedom, and an unrealistic photon index, Γ = 0.4 ± 0.1.

Based on previous analyses of PG 1114+445 (George et al. 1997; Ashton et al. 2004; Piconcelli et al. 2005, Paper I), we included an ionized absorber using a partial covering model, ZXIPCF (Reeves et al. 2008). The assumed model is therefore Tbabs × zxipcf × (powerlaw + zgauss). The addition of this ionized absorption component significantly improved the goodness of fit, with a C-stat of C/d.o.f. = 106/103. The best-fit value found for the photon index is , which is consistent with the values measured in Paper I. We find that the absorber is moderately ionized, , and almost fully covering, with a covering factor , with a column density of cm−2. This value is a factor of ∼10 higher than the column density of any low-ionization absorber detected in Paper I, which confirms that the flux decrease is mainly due to a column density increase with respect to the 2010 observations. The spectrum, the best-fit model, and the data-to-model residuals are shown in Fig. 3. The best-fit values are summarized in Table 2. The confidence contour plot of the joint errors of the ionization parameter versus NH is shown in Fig. 4. In particular, the column density is higher than the NH of any soft X-ray absorber detected in Paper I at the 3σ confidence level.

thumbnail Fig. 3.

Composite Swift-XRT spectrum (black) with its best-fit model (upper panel) and data-to-model ratios (lower panel). For comparison, we also show the 2002 EPIC-pn observation (blue) and the lowest-flux spectrum of the 2010 campaign (orange).

thumbnail Fig. 4.

Contour plot for the ionization log ξ and the column density NH. The red line represents the 1σ confidence level (68%), and the green and blue lines represent the 2σ (95%) and 3σ (99.7%) confidence levels. The cross marks the best-fit values.

Table 2.

Summary of the best-fit parameters for our best model: TBabs × zxipcf × (powerlaw + zgauss).

5. UV variability

The UV observations were provided by the XMM-Newton Optical Monitor (OM) and by Swift-UVOT. The OM data were retrieved from the XMM Serendipitous Ultraviolet Source Survey (XMM-SUSS, Page et al. 2012). None of the UVOT filters show evidence of relevant variability between 2010 and 2019-2020 (see Table 3).

Table 3.

Flux densities in units of 10−15 erg cm−2 s−1 Å −1 for each Swift-UVOT pointing.

We computed the X-ray/UV ratio αox (e.g., Vignali et al. 2003; Vagnetti et al. 2010; Lusso & Risaliti 2016; Chiaraluce et al. 2018) in order to analyze the relative variability of the X-ray and UV bands, which is defined as

where ν(2 keV) and ν(2500 Å) are the frequencies (in Hz) in the rest frame, corresponding to an energy of 2 keV and a wavelength of 2500 Å, respectively. The 2 keV luminosity of the XRT observations was computed using XSPEC. We adopted the best-fit model (see Sect. 4) and froze every parameter with the exception of the column density NH of the absorber and the normalization of the power law. We fit each snapshot with this model to obtain a best-fit model as accurate as possible, and then we removed the absorption component and used the LUMIN task between 1.99 and 2.01 keV, from which we derived the intrinsic X-ray luminosity L(2 keV). Because the UVOT observations were performed with a single source-centered filter, we computed the luminosity at 2500 Å by interpolating a spectral energy distribution obtained by averaging thousands of Sloan Digital Sky Survey (SDSS) observations (Richards et al. 2006). We conservatively computed L(2500 Å) only for the observations performed with the UVM2 filter, which is centered at ∼2250 Å, corresponding to a rest-frame wavelength of ∼2510 Å, and therefore close enough to 2500 Å. The fluxes were finally corrected for reddening due to the Galactic extinction adopting E(B − V) = 0.0264 (Güver & Özel 2009).

The result is shown in Fig. 5. The Swift observations lie on the general trend found by Chiaraluce et al. (2018), which is represented by a solid line. We note that none of the points lie on the X-ray weak limit (dashed line), which is set 0.3 dex below the best-fit line (Pu et al. 2020). This is further proof that the low flux state of the source is clearly driven by the obscuration and not by an intrinsic luminosity change.

thumbnail Fig. 5.

Plot of the αox index vs. the UV luminosity using unabsorbed X-ray luminosities. The gray points are the quasars analyzed in Chiaraluce et al. (2018). The linear best fit is shown as a solid line. The dashed line is the X-ray weak limit defined by Pu et al. (2020). The red squares correspond to the XMM-Newton observations, and the black points correspond to those performed by Swift.

6. Summary and discussion

We have presented the analysis of the Swift observations of the type 1 quasar PG 1114+445, performed ten years after the XMM-Newton observations analyzed in Paper I. The source is found in a strongly reduced flux state (Fig. 2), which can be ascribed to an absorption increase caused by an obscuring material. The spectral analysis highlights the presence of ionized (log ξ/erg cm s−1 ∼ 1.4) material, characterized by a column density NH ∼ 7 × 1022 cm−2. No substantial variations in the unabsorbed X-ray luminosity of the source were found.

In Paper I, two absorbers were found in the soft X-rays, a slow and constant WA with NH ∼ 7 × 1021 cm−2, and a mildly relativistic absorber, identified with an E-UFO, with a variable column density with a median value NH ∼ 3 × 1021 cm−2 and a dispersion of ∼6 × 1020 cm−2. Both these column densities are a factor of 10 lower than the density found in this work. If we allow for an outflowing velocity of this ionized absorber, we obtain that the best-fit value of the redshift of the absorber in the observer frame is zobs ∼ 0.09, which corresponds to vout ∼ 0.05c. However, the low statistics of the dataset does not allow us to successfully measure both the ionization parameter and the velocity, which are notoriously affected by degeneracy, as shown by their contour plot in Fig. 6. We note that lower values of the absorber redshift are allowed at the 1σ confidence level, down to zobs ≃ −0.03 (vout ≃ 0.16c), consistent with both the E-UFO and the WA measured in Paper I. In addition, the ionization parameter can be as low as log ξ ≲ 0.4 at the 3σ confidence level, consistent with the E-UFO median value of 2010. This suggests that Swift-XRT is not able to resolve the complex structure of the absorbers measured in Paper I, but it may be hidden by the dominant absorber that we observe in this campaign.

thumbnail Fig. 6.

Contour plots between the redshift of the absorber in the observer frame zobs and the ionization parameter log ξ. The red, green, and blue line represent the 1σ (68%), 2σ (95%) and 3σ (99.7%) confidence levels, respectively.

We can estimate a lower limit for its distance using the equation by Risaliti et al. (2002),

(1)

where MBH ≃ 6.3 × 108 M⊙ is the black hole mass, n is the density of the clump (n9 in units of 109 cm−3), NH ≃ 7 × 1022 cm−2 is the column density of the clump, and t is the duration of the obscuration. We assume here that the obscuration lasts for the whole campaing, including the times between 2019 and 2020 when the source was not observed. Therefore we adopt t ≳ 560 days (rest-frame) as the duration of the obscuration, which is only a lower limit because it might last beyond the end of the campaign. The density of the clump can be replaced by inverting the definition of the ionization parameter,

(2)

where Lion ≃ 1.5 × 1044 erg s−1 is obtained from the unabsorbed best-fit model, and erg cm s−1 is the best-fit value (see Table 2). Combining Eqs. (1) and (2), we obtain an upper limit for the density of the obscuring clump n = (2.7 ± 0.5)×106 cm−3, which means that it is located at least at r = (1.5 ± 0.5)×1018 cm = (0.50 ± 0.15) pc = (8 ± 2)×103rs, where rs = 2GM/c2 is the Schwarzschild radius. The estimated minimum size of the clump is therefore R ≃ NH/n = (6 ± 2)×10−3 pc.

An estimate of the maximum distance of the cloud from the central source might be given by noting that the size of the clump cannot be larger than its distance from the X-ray source. Therefore we assume NH = nR < nrmax (e.g., Crenshaw & Kraemer 2012; Tombesi et al. 2013). Substituting this into the definition of ionization parameter, we obtain

(3)

Assuming again Lion ≃ 1.5 × 1044 erg s−1, and the best-fit values of NH and ξ (Table 2), we obtain .

The increase in column density is likely due to a new clump of absorbing material, either due to a superposition of the WA and the E-UFO observed in Paper I, with an increased column density NH, or an additional absorber located between the central source and the previously known absorber. The values of the minimum and maximum distance of the clump found in Eqs. (1) and (3) strongly suggest that the absorbing clumps are located outside the typical boundaries of the broad line region for a quasar of this luminosity. We estimate a broad line region radius of RBLR ≃ 0.07 pc, considering the luminosity log(L5100Å/erg s−1) ≃ 44.77 of PG 1114+445 (Shen et al. 2011), when the relation between RBLR and L5100Å derived by Bentz et al. (2009) is assumed. A comparison between the parameters of the absorber detected here and those measured in Paper I is shown in Fig. 7.

thumbnail Fig. 7.

Time variability of the absorber parameters NH (top) and ξ (middle). For all observations prior to 2019, the black points refer to the variable E-UFO, while the constant WA is represented by the dashed red line. In the lower panel, the light curve of soft X-ray (E = 0.5−2 keV) count rates is shown, where the XMM rates were converted into Swift-XRT count rates using WebPIMMS.

While no strong flux variability is detected between the Swift-XRT observations, minor variations are present (Figs. 1 and 2) and may be tied to the observed increased clumpiness. For instance, CCA predicts fractal variations with a power spectral density proportional to f−1, where f is the time frequency. In other words, smaller clumps are expected to contribute to the micro variations observed in the light curves (e.g., Gaspari et al. 2017). The distance estimate places the cloud at the meso scale in the CCA self-regulation framework (Gaspari et al. 2020). This is the typical transition scale at which CCA clouds become more clustered and collide frequently, thus generating flickering absorbers along the line of sight, starting from the X-ray/hot phase and potentially down to the radio/molecular phase (e.g., Tremblay et al. 2018; Rose et al. 2019).

In the past decade, many obscuring variable absorbers were found in the X-ray spectra of nearby AGN (e.g., Markowitz et al. 2014), with timescales ranging from decades (e.g., Kaastra et al. 2014) to months or weeks (e.g., Matzeu et al. 2016; Mehdipour et al. 2017; Middei et al. 2020) and even days (e.g., Braito et al. 2014; Severgnini et al. 2015). These winds, with velocities than can be higher than typical WA velocities, are important because they may carry sufficient kinetic power to contribute to possible AGN feedback on the host galaxy. It is therefore important to continue monitoring these sources and possibly several others with current facilities such as Swift or eROSITA (Merloni et al. 2012), and with future missions such as the enhanced X-ray Timing and Polarimetry mission (eXTP, Zhang et al. 2019), XRISM/Xtend (e.g., Yoneyama et al. 2020), or the Athena Wide Field Imager (WFI, Meidinger et al. 2015) in order to search for both long-term and transient obscuration from ionized clumps. In the case of PG 1114+445, given the long-term nature of its obscuration, a monthly monitoring could help to identify lowest and highest states, to study them with current X-ray telescopes such as XMM-Newton. In the future, forthcoming microcalorimeters such as Resolve on board XRISM (XRISM Science Team 2020) and Athena/X-IFU (Barret et al. 2016) will be able to measure the X-ray spectrum with unprecedented energy resolution, letting us measure the outflow velocity of these obscurers with much higher accuracy and allowing us to observe the full range of absorbers in AGN.

Acknowledgments

We thank the referee for improving the paper with useful comments. We acknowledge financial contribution from the agreement ASI-INAF n.2017-14-H.0. EP acknowledges support from PRIN MIUR project “Black Hole winds and the Baryon Life Cycle of Galaxies: the stone-guest at the galaxy evolution supper”, contract n. 2017PH3WAT. MG acknowledges partial support by NASA Chandra GO8-19104X/GO9-20114X and HST GO-15890.020-A grants. AT acknowledges the financial support from FONDECYT Postdoctorado for the project n. 3190213. We acknowledge the use of public data from the Swift data archive. This research has made use of data and software provided by the High Energy Astrophysics Science Archive Research Center (HEASARC), which is a service of the Astrophysics Science Division at NASA/GSFC and the High Energy Astrophysics Division of the Smithsonian Astrophysical Observatory.

References

  1. Arnaud, K. A. 1996, in Astronomical Data Analysis Software and Systems V, eds. G. H. Jacoby, & J. Barnes, ASP Conf. Ser., 101, 17 [Google Scholar]
  2. Ashton, C. E., Page, M. J., Blustin, A. J., et al. 2004, MNRAS, 355, 73 [NASA ADS] [CrossRef] [Google Scholar]
  3. Ballo, L., Severgnini, P., Braito, V., et al. 2015, A&A, 581, A87 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  4. Barret, D., Lam Trong, T., den Herder, J. W., et al. 2016, in Proc. SPIE, SPIE Conf. Ser., 9905, 99052F [CrossRef] [Google Scholar]
  5. Bentz, M. C., Peterson, B. M., Netzer, H., Pogge, R. W., & Vestergaard, M. 2009, ApJ, 697, 160 [Google Scholar]
  6. Blustin, A. J., Page, M. J., Fuerst, S. V., Branduardi-Raymont, G., & Ashton, C. E. 2005, A&A, 431, 111 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  7. Braito, V., Reeves, J. N., Dewangan, G. C., et al. 2007, ApJ, 670, 978 [NASA ADS] [CrossRef] [Google Scholar]
  8. Braito, V., Reeves, J. N., Gofford, J., et al. 2014, ApJ, 795, 87 [NASA ADS] [CrossRef] [Google Scholar]
  9. Chartas, G., Brandt, W. N., Gallagher, S. C., & Garmire, G. P. 2002, ApJ, 579, 169 [Google Scholar]
  10. Chiaraluce, E., Vagnetti, F., Tombesi, F., & Paolillo, M. 2018, A&A, 619, A95 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  11. Cicone, C., Maiolino, R., Sturm, E., et al. 2014, A&A, 562, A21 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  12. Cicone, C., Brusa, M., Ramos Almeida, C., et al. 2018, Nat. Astron., 2, 176 [Google Scholar]
  13. Corral, A., Della Ceca, R., Caccianiga, A., et al. 2011, A&A, 530, A42 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  14. Crenshaw, D. M., & Kraemer, S. B. 2012, ApJ, 753, 75 [NASA ADS] [CrossRef] [Google Scholar]
  15. Ferrarese, L., & Merritt, D. 2000, ApJ, 539, L9 [Google Scholar]
  16. Fiore, F., Feruglio, C., Shankar, F., et al. 2017, A&A, 601, A143 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  17. Gaspari, M., & Sądowski, A. 2017, ApJ, 837, 149 [NASA ADS] [CrossRef] [Google Scholar]
  18. Gaspari, M., Ruszkowski, M., & Oh, S. P. 2013, MNRAS, 432, 3401 [NASA ADS] [CrossRef] [Google Scholar]
  19. Gaspari, M., Temi, P., & Brighenti, F. 2017, MNRAS, 466, 677 [NASA ADS] [CrossRef] [Google Scholar]
  20. Gaspari, M., Eckert, D., Ettori, S., et al. 2019, ApJ, 884, 169 [NASA ADS] [CrossRef] [Google Scholar]
  21. Gaspari, M., Tombesi, F., & Cappi, M. 2020, Nat. Astron., 4, 10 [Google Scholar]
  22. Gehrels, N., Chincarini, G., Giommi, P., et al. 2004, ApJ, 611, 1005 [NASA ADS] [CrossRef] [Google Scholar]
  23. George, I. M., Nandra, K., Laor, A., et al. 1997, ApJ, 491, 508 [NASA ADS] [CrossRef] [Google Scholar]
  24. Gibson, R. R., Brandt, W. N., Gallagher, S. C., & Schneider, D. P. 2009, ApJ, 696, 924 [NASA ADS] [CrossRef] [Google Scholar]
  25. Gofford, J., Reeves, J. N., Tombesi, F., et al. 2013, MNRAS, 430, 60 [Google Scholar]
  26. Güver, T., & Özel, F. 2009, MNRAS, 400, 2050 [Google Scholar]
  27. Halpern, J. P. 1984, ApJ, 281, 90 [NASA ADS] [CrossRef] [Google Scholar]
  28. Häring, N., & Rix, H.-W. 2004, ApJ, 604, L89 [Google Scholar]
  29. Harrison, C. M., Alexander, D. M., Mullaney, J. R., & Swinbank, A. M. 2014, MNRAS, 441, 3306 [Google Scholar]
  30. Hewett, P. C., & Wild, V. 2010, MNRAS, 405, 2302 [NASA ADS] [Google Scholar]
  31. HI4PI Collaboration (Ben Bekhti, N., et al.) 2016, A&A, 594, A116 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  32. Jansen, F., Lumb, D., Altieri, B., et al. 2001, A&A, 365, L1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  33. Kaastra, J. S. 2017, A&A, 605, A51 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  34. Kaastra, J. S., Kriss, G. A., Cappi, M., et al. 2014, Science, 345, 64 [NASA ADS] [CrossRef] [Google Scholar]
  35. King, A., & Pounds, K. 2015, ARA&A, 53, 115 [NASA ADS] [CrossRef] [Google Scholar]
  36. Laha, S., Guainazzi, M., Dewangan, G. C., Chakravorty, S., & Kembhavi, A. K. 2014, MNRAS, 441, 2613 [NASA ADS] [CrossRef] [Google Scholar]
  37. Laha, S., Reynolds, C. S., Reeves, J., et al. 2021, Nat. Astron., 5, 13 [NASA ADS] [CrossRef] [Google Scholar]
  38. Longinotti, A. L., Krongold, Y., Guainazzi, M., et al. 2015, ApJ, 813, L39 [Google Scholar]
  39. Luminari, A., Nicastro, F., Elvis, M., et al. 2021, A&A, 646, A111 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  40. Lusso, E., & Risaliti, G. 2016, ApJ, 819, 154 [NASA ADS] [CrossRef] [Google Scholar]
  41. Markowitz, A. G., Krumpe, M., & Nikutta, R. 2014, MNRAS, 439, 1403 [NASA ADS] [CrossRef] [Google Scholar]
  42. Mathur, S., Wilkes, B., & Elvis, M. 1998, ApJ, 503, L23 [NASA ADS] [CrossRef] [Google Scholar]
  43. Matzeu, G. A., Reeves, J. N., Nardini, E., et al. 2016, MNRAS, 458, 1311 [NASA ADS] [CrossRef] [Google Scholar]
  44. Matzeu, G. A., Reeves, J. N., Braito, V., et al. 2017, MNRAS, 472, L15 [NASA ADS] [CrossRef] [Google Scholar]
  45. McKernan, B., Yaqoob, T., & Reynolds, C. S. 2007, MNRAS, 379, 1359 [NASA ADS] [CrossRef] [Google Scholar]
  46. Mehdipour, M., Kaastra, J. S., Kriss, G. A., et al. 2017, A&A, 607, A28 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  47. Meidinger, N., Nandra, K., Plattner, M., et al. 2015, J. Astron. Telescopes Instrum. Syst., 1, 014006 [Google Scholar]
  48. Merloni, A., Predehl, P., Becker, W., et al. 2012, ArXiv e-prints [arXiv:1209.3114] [Google Scholar]
  49. Middei, R., Tombesi, F., Vagnetti, F., et al. 2020, A&A, 635, A18 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  50. Nardini, E., Reeves, J. N., Gofford, J., et al. 2015, Science, 347, 860 [Google Scholar]
  51. Page, M. J., Brindle, C., Talavera, A., et al. 2012, MNRAS, 426, 903 [NASA ADS] [CrossRef] [Google Scholar]
  52. Piconcelli, E., Jimenez-Bailón, E., Guainazzi, M., et al. 2005, A&A, 432, 15 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  53. Pounds, K. A., King, A. R., Page, K. L., & O’Brien, P. T. 2003a, MNRAS, 346, 1025 [NASA ADS] [CrossRef] [Google Scholar]
  54. Pounds, K. A., Reeves, J. N., King, A. R., et al. 2003b, MNRAS, 345, 705 [NASA ADS] [CrossRef] [Google Scholar]
  55. Pounds, K. A., Lobban, A., Reeves, J. N., Vaughan, S., & Costa, M. 2016, MNRAS, 459, 4389 [NASA ADS] [CrossRef] [Google Scholar]
  56. Pu, X., Luo, B., Brandt, W. N., et al. 2020, ApJ, 900, 141 [CrossRef] [Google Scholar]
  57. Reeves, J., Done, C., Pounds, K., et al. 2008, MNRAS, 385, L108 [NASA ADS] [CrossRef] [Google Scholar]
  58. Reeves, J. N., Braito, V., Nardini, E., et al. 2018, ApJ, 854, L8 [NASA ADS] [CrossRef] [Google Scholar]
  59. Reeves, J. N., Braito, V., Chartas, G., et al. 2020, ApJ, 895, 37 [Google Scholar]
  60. Richards, G. T., Lacy, M., Storrie-Lombardi, L. J., et al. 2006, ApJS, 166, 470 [NASA ADS] [CrossRef] [Google Scholar]
  61. Risaliti, G., Elvis, M., & Nicastro, F. 2002, ApJ, 571, 234 [NASA ADS] [CrossRef] [Google Scholar]
  62. Rose, T., Edge, A. C., Combes, F., et al. 2019, MNRAS, 489, 349 [NASA ADS] [CrossRef] [Google Scholar]
  63. Serafinelli, R., Vagnetti, F., & Middei, R. 2017, A&A, 600, A101 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  64. Serafinelli, R., Tombesi, F., Vagnetti, F., et al. 2019, A&A, 627, A121 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  65. Severgnini, P., Ballo, L., Braito, V., et al. 2015, MNRAS, 453, 3611 [Google Scholar]
  66. Shen, Y., Richards, G. T., Strauss, M. A., et al. 2011, ApJS, 194, 45 [Google Scholar]
  67. Tombesi, F., Cappi, M., Reeves, J. N., et al. 2010, A&A, 521, A57 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  68. Tombesi, F., Cappi, M., Reeves, J. N., et al. 2013, MNRAS, 430, 1102 [Google Scholar]
  69. Tombesi, F., Meléndez, M., Veilleux, S., et al. 2015, Nature, 519, 436 [NASA ADS] [CrossRef] [Google Scholar]
  70. Tremblay, G. R., Combes, F., Oonk, J. B. R., et al. 2018, ApJ, 865, 13 [NASA ADS] [CrossRef] [Google Scholar]
  71. Turner, T. J., Nandra, K., Turcan, D., & George, I. M. 2001, in X-ray Astronomy: Stellar Endpoints, AGN, and the Diffuse X-ray Background, eds. N. E. White, G. Malaguti, & G. G. C. Palumbo, AIP Conf. Ser., 599, 991 [NASA ADS] [CrossRef] [Google Scholar]
  72. Vagnetti, F., Turriziani, S., Trevese, D., & Antonucci, M. 2010, A&A, 519, A17 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  73. Vietri, G., Piconcelli, E., Bischetti, M., et al. 2018, A&A, 617, A81 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  74. Vignali, C., Brandt, W. N., & Schneider, D. P. 2003, AJ, 125, 433 [NASA ADS] [CrossRef] [Google Scholar]
  75. XRISM Science Team 2020, ArXiv e-prints [arXiv:2003.04962] [Google Scholar]
  76. Yoneyama, T., Noda, H., Hanaoka, M., et al. 2020, in Space Telescopes and Instrumentation 2020: Ultraviolet to Gamma Ray, eds. J. W. A. den Herder, S. Nikzad, K. Nakazawa, et al., International Society for Optics and Photonics (SPIE), 11444, 315 [Google Scholar]
  77. Zhang, S., Santangelo, A., Feroci, M., et al. 2019, Sci. China Phys. Mech. Astron., 62, 29502 [Google Scholar]

All Tables

Table 1.

X-ray observations taken by Swift-XRT, with OBSID, observation date, and exposure.

In the text
Table 2.

Summary of the best-fit parameters for our best model: TBabs × zxipcf × (powerlaw + zgauss).

In the text
Table 3.

Flux densities in units of 10−15 erg cm−2 s−1 Å −1 for each Swift-UVOT pointing.

In the text

All Figures

thumbnail Fig. 1.

Swift-XRT count rates in the energy band E = 0.5−10 keV. The magenta star represents OBSID 00089058001, which was not part of our campaign.
In the text
thumbnail Fig. 2.

Full-band (E = 0.5−10 keV) light curve using ASCA data (blue triangle), XMM-Newton (red squares), our Swift-XRT campaign (black circles), and additional archival Swift-XRT observation (magenta star; left). Time variability of the hardness ratio HR = (H − S)/(H + S), where S and H are the 0.5 − 2 and 2 − 10 keV count rates, respectively (left). The spectrum in the most recent data is noticeably harder. The count rates of the ASCA and EPIC-pn observations were converted into Swift-XRT count rates following the procedure described in Sect. 3.
In the text
thumbnail Fig. 3.

Composite Swift-XRT spectrum (black) with its best-fit model (upper panel) and data-to-model ratios (lower panel). For comparison, we also show the 2002 EPIC-pn observation (blue) and the lowest-flux spectrum of the 2010 campaign (orange).
In the text
thumbnail Fig. 4.

Contour plot for the ionization log ξ and the column density NH. The red line represents the 1σ confidence level (68%), and the green and blue lines represent the 2σ (95%) and 3σ (99.7%) confidence levels. The cross marks the best-fit values.
In the text
thumbnail Fig. 5.

Plot of the αox index vs. the UV luminosity using unabsorbed X-ray luminosities. The gray points are the quasars analyzed in Chiaraluce et al. (2018). The linear best fit is shown as a solid line. The dashed line is the X-ray weak limit defined by Pu et al. (2020). The red squares correspond to the XMM-Newton observations, and the black points correspond to those performed by Swift.
In the text
thumbnail Fig. 6.

Contour plots between the redshift of the absorber in the observer frame zobs and the ionization parameter log ξ. The red, green, and blue line represent the 1σ (68%), 2σ (95%) and 3σ (99.7%) confidence levels, respectively.
In the text
thumbnail Fig. 7.

Time variability of the absorber parameters NH (top) and ξ (middle). For all observations prior to 2019, the black points refer to the variable E-UFO, while the constant WA is represented by the dashed red line. In the lower panel, the light curve of soft X-ray (E = 0.5−2 keV) count rates is shown, where the XMM rates were converted into Swift-XRT count rates using WebPIMMS.
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.