adsabs/FOCAL huggingface dataset:
The gravitational microlensing of lensed quasars has proven to be an effective tool for measuring the properties of quasar accretion disks, and is starting to become useful for studying the X-ray corona as well. The time-dependent microlensing magnification (or demagnification) of one or more images of a lensed quasar is moderated by the finite size of the source, which smooths the complicated caustic pattern of microlensing magnifications as the quasar passes over it. This allows us to use the microlensing magnifications to estimate the source size, and such work has shown that in general the accretion disks are larger than would be expected from either thin disk modeling or total flux arguments (Pooley etÂ al. 2007; Anguita etÂ al. 2008; Morgan etÂ al. 2010; Hainline etÂ al. 2012; JimÃ©nez-Vicente etÂ al. 2012). Since the effective temperature of the disk depends on radius, its apparent size depends on wavelength, leading to a chromatic dependence of the microlensing magnification, with the same quasar image experiencing larger variability at blue wavelengths than at red wavelengths. Several studies have used this to constrain the power-law slope of the size-wavelength relation, and the results have been consistent with each other and with the thin disk prediction that the size goes like the four-thirds power of the wavelength, mostly because of their large uncertainties (Poindexter etÂ al. 2008; Bate etÂ al. 2008; Eigenbrod etÂ al. 2008; Floyd etÂ al. 2009; Blackburne etÂ al. 2011; Mosquera etÂ al. 2011). Finally, efforts to put upper limits on the size of the X-ray regions have also been successful (Pooley etÂ al. 2006, 2007; Chartas etÂ al. 2009; Dai etÂ al. 2010), and recently there have been attempts to constrain the direct and reflected components' sizes separately using color cuts or spectral decomposition (Chen etÂ al. 2011; Blackburne etÂ al. 2014; Morgan etÂ al. 2012; Chen etÂ al. 2012; Chartas etÂ al. 2012; Mosquera etÂ al. 2013).
Citation: Morgan etÂ al. 2010
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Upper Scorpius (hereafter UpSco) is part of the nearest OB association to the Sun, Scorpius Centaurus. The region is nearby, with a distance of ∼145 pc from Hipparcos (de Bruijne et al. 1997) and a recent update from Gaia (144.2 ± 17.6 pc: Fang, Herczeg & Rizzuto 2017). UpSco is young, with different age determinations and a possible spread among its members (Preibisch & Zinnecker 1999; Preibisch, Guenther & Zinnecker 2001; Pecaut, Mamajek & Bubar 2012; Song, Zuckerman & Bessell 2012; Pecaut 2016; Rizzuto et al. 2016). The members of UpSco exhibit a significant mean proper motion (μαcos δ = −10.5 and μδ = −23.2 mas yr−1, with a standard dispersion of about 6.4 mas yr−1: de Bruijne et al. 1997; de Zeeuw et al. 1999; Fang et al. 2017). The bright end of the UpSco population has been examined in X-rays (Walter et al. 1994; Kunkel 1999; Preibisch et al. 1998), astrometrically (de Bruijne et al. 1997; de Zeeuw et al. 1999) and spectroscopically (Preibisch & Zinnecker 2002). The low-mass and substellar population has been investigated over the past decade in great detail with the advent of modern detectors, permitting wide and/or deep surveys (Ardila, Martín & Basri 2000; Martín, Delfosse & Guieu 2004; Slesnick, Carpenter & Hillenbrand 2006; Lodieu, Hambly & Jameson 2006; Lodieu et al. 2007, 2011b; Kraus et al. 2008; Béjar et al. 2008; Slesnick, Hillenbrand & Carpenter 2008; Lafrenière, Jayawardhana & van Kerkwijk 2010; Dawson, Scholz & Ray 2011; Lafrenière et al. 2011, 2014; Dawson et al. 2013, 2014; Lodieu 2013; Peña Ramírez, Béjar & Zapatero Osorio 2016; Best et al. 2017). The first transiting systems in the association have been announced in the last years, thanks to the Kepler K2 mission (Borucki et al. 2010; Lissauer, Dawson & Tremaine 2014; Batalha 2014), including a triple system composed of an F star and two solar-type stars (Alonso et al. 2015) and several M dwarf eclipsing binaries (Kraus et al. 2015; Lodieu et al. 2015; David et al. 2016). These systems are of prime importance, because they provide the first mass and radius measurements independent of models in this region. Finally, we should note the existence of a transiting Neptune-size planet candidate around an M3 member of UpSco (Mann et al. 2016; David et al. 2016).
Citation: Preibisch et al. 1998
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To complement our study, we also analyzed GLOSTAR continuum images toward sites with maser emission. A full description of the GLOSTAR continuum data calibration and imaging is given in Brunthaler et al. (2021), while the full analysis of continuum images of Cygnus X will be presented in a forthcoming paper. Here, we briefly discuss the imaging strategy. The calibration and imaging of the continuum data was performed with the Obit package (Cotton 2008). The 2 GHz bandwidth was first rearranged into nine frequency subbands, which were used to image each pointing individually. Then, for each frequency subband the pointings were combined into large individual mosaics to cover the entire observed area. Finally, we combined the different frequencies to obtain the image at the reference frequency, which has circular beams of 19″ and 1.5″ in the D and Bconfiguration, respectively. Continuum and methanol line maps from Effelsberg observations have also been obtained as part of the GLOSTAR survey (Brunthaler et al. 2021, Rugel et al., in prep.) We note that continuum images were constructed for Effelsberg data, the VLA D configuration, the VLA B configuration, a combination of the VLA D and B (D+B) configurations, and a combination of the VLA D configuration and Effelsberg observations. The central frequency of these images is 5.8 GHz. Here, we only use B-configuration continuum maps to study the region of the investigated methanol maser positions and D+B maps of the region around DR21 (see Sect. 4.5). Methanol line data from Effelsberg were also inspected to look for flux variations in the VLA-detected masers (Sect. 4.4). The noise in the continuum images is not uniform, but rather varies across the mapped region, and can be high around strong sources with complex or extended emission. We locally measured the noise in regions close to the maser locations, resulting in 1σ values in the range from 0.056 to 0.43 mJy beam−1 for B-configuration images. For the D configuration, the 1σ rms noise ranges from 0.10 to 2.6 mJy beam−1. The higher values measured in D-configuration data are due to bright extended emission, which is present across the Cygnus X region, and are resolved out by the array in the B configuration. The highest local rms noise occurs around the strong radio source, DR21, a compact HII region.
Citation: Brunthaler et al. 2021
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Citation: Brunthaler et al. (2021)
Citation function: Uses
It is interesting to note that for wind originating within 





, their value of Be becomes almost constant when 





. This corresponds to the slight decrease of the poloidal velocity beyond 





 shown in the left panel of Figure 8. The reason why Be does not change beyond 





 is because in this region turbulence has not well developed within our simulation time. Note that this radius is different from the inflow equilibrium radius, which is 





. Within 





, everything, especially the radial density profile, is fully reliable. Beyond this radius, the density profile is not reliable, but other properties, such as the level of turbulence and subsequently outflow properties, are still reliable up to a much larger “turbulence radius,” the limiting radius of turbulence steady state. This radius can be estimated as follows. Turbulence in accretion flow is because of MRI. The fastest growth rate of MRI at radius r is 





. More precisely, it takes 3–4 orbits for MRI to develop and ∼10 orbits to saturate (Hawley et al. 1995). For our simulation time of 





, taking a timescale of 3 orbits, we can obtain that the “turbulence radius” is 





. This is close to the value of 800 rg mentioned above. Another way to understand the “turbulence radius” is as follows. For a geometrically thick disk, the largest turbulence eddies have size of order r. The corresponding eddy turnover time is 





, where 





 is the rms turbulent velocity. Our simulation data show 





. If at a certain radius the eddy turnover time is substantially smaller than the duration of the simulation, then the local turbulence is likely to have reached quasi-steady state. Therefore, the “turbulence radius” should be some fraction of 





, which gives a similar result to the above estimation. Therefore, we think that the results beyond 





 are not reliable. It is very likely that Be will keep changing and the poloidal velocity still remains constant beyond 





. This implies that wind can at least escape beyond the outer boundary of accretion flows. Simulations with longer run times can check this point.
Citation: Hawley et al. 1995
Citation function: Uses
Several predictions for planet(s) shaping the disk of SAO 206462 have been proposed (Muto et al. 2012; Garufi et al. 2013; Fung & Dong 2015; Bae et al. 2016; van der Marel et al. 2016a. Using linear equations from the spiral density wave theory, Muto et al. (2012) suggested two planets with separations beyond ~50 au by fitting independently the two spiral arms seen in Subaru/HiCIAO data and with masses of ~0.5 MJ by using the amplitude of the spiral wave. Garufi et al. (2013) proposed that one planet of mass 5–13 MJ located inside the cavity at a separation of 17.5–20 au could be responsible for the different cavity sizes measured for the small and large dust grains. Fung & Dong (2015) presented scaling relations between the azimuthal separation of the primary and secondary arms and the planet-to-star mass ratio for a single companion on a circular orbit with a mass between Neptune mass and 16 MJ around a 1 M⊙ star. They predicted with 30% accuracy that a single putative planet responsible for both spiral features of SAO 206462 would have a mass of ~6 MJ. Bae et al. (2016) presented dedicated hydrodynamical simulations of the SAO 206462 disk and proposed that both the bright scattered-light feature (Garufi et al. 2013) and the dust emission peak (Pérez et al. 2014) seen for the southwestern spiral arm result from the interaction of the spiral arm with a vortex, although a vortex alone can account for the S1 brightness peak. They suggested that a 10–15 MJ planet may orbit at 100–120 au from the star. However, ALMA observations at two different frequencies seem to contradict a dust particle trapping scenario by a vortex (Pinilla et al. 2015). Stolker et al. (2016) performed new fitting of the spiral arms observed in SPHERE data and found a best-fit solution with two protoplanets located exterior to the spirals: r1 ~ 168 au, θ1 ~ 52° and r2 ~ 99 au, θ2 ~ 355°. van der Marel et al. (2016a) proposed that the features seen in thermal emission in ALMA data and the scattered-light spiral arms are produced by a single massive giant planet located inside the cavity at a separation of ~30 au. Recently, Dong & Fung (2017) used the contrast of the spiral arms to predict a giant planet of ~5–10 MJ at ~100 au.
Citation: Fung & Dong 2015
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Citation: Fung & Dong (2015)
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We have presented the analysis of the current X-ray observations of the disc wind in MCG-03-58-007. Here, multiple and variable wind components with velocities ranging from $\sim \! -0.08\, c$ to $\sim \! - 0.2\, c$ (and potentially up to $0.35\, c$) are seen at different times. Multi-epoch observations of disc winds, like the one presented here, are crucial for revealing all the possible phases of the disc wind. For example, over a decade worth of observations of PDS 456 revealed that the wind is most likely clumpy and/or stratified with the ionization ranging from log ($\xi /\rm {erg\, cm \, s^{-1})}\sim 2$ erg cm s−1 up to log ($\xi /\rm {erg\, cm \, s^{-1})}=6$ erg cm s−1 and velocities ranging from $\sim \! -0.2\, c$ up to $\sim \! -0.46\, c$ (Reeves et al. 2016, 2018a, 2020). It is possible, as suggested in other examples of ultra fast disc winds, that we are looking at a stratified wind, where multiple components are launched at different disc radii, but not all of them are always detected. This adds MCG-03-58-007 to the small but growing list of multiphase fast X-ray winds. Other examples of AGN with at least two variable phases of the X-ray winds are PG 1211+143 (Pounds et al. 2016; Reeves et al. 2018b), IRAS 13224-3809 (Parker et al. 2017; Chartas & Canas 2018; Pinto et al. 2018), 1H 0707-495 (Kosec et al. 2018), IRAS 17020+4544 (Longinotti et al. 2015), and PG 1114+445 (Serafinelli et al. 2019). In those cases, multiple phases with a common or different outflowing velocities are detected in the X-ray band. In contrast to most of the cases reported so far, neither of the two phases seen in MCG-03-58-007 requires a different ionization (aside from slice B) suggesting that we are seeing different streamlines of the same highly ionized flow. The only exception could be the eclipsing event seen in 2015, where a solution is found with a lower ionization for the Fe K intervening absorber. However, what we most likely see during this occultation event is a higher density and lower ionization clump of the wind, which could be faster because its higher opacity makes it easier to accelerate (Waters et al. 2017). Note that this does not imply that the soft X-ray wind components, like the ones seen for example in PDS 456 or PG 1211+143, are not present; in contrast to the other examples, MCG-03-58-007 is seen through a relatively high column density (NH ∼ 2 × 1023 cm−2, see Table 2) neutral absorber, therefore these phases may be hidden behind it. MCG-03-58-007 is not the only example where multiple Fe-K zones with the same ionization and outflowing with different velocities had been detected in a single observation. For instance, two simultaneous Fe-K phases were detected at least twice in PDS456 (Reeves et al. 2018a, 2020) and possibly in PG 1211+143 (Pounds et al. 2016) and IRAS 13349+2438 (Parker et al. 2020).
Citation: Longinotti et al. 2015
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Citation: Serafinelli et al. 2019
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In the second part of the paper, we propose the set of self-similar solutions as tests for MHD numerical codes with ambipolar diffusion capabilities. To show their usefulness and validity, a battery of tests was carried out for the Bifrost code in two spatial dimensions starting from initial conditions with cylindrical symmetry (Sect. 4). We showed that the ambipolar diffusion module in Bifrost can cope with the passage of the solutions through the current sheets, with the level of accuracy increasing the higher the spatial resolution and in spite of the intrinsic singularity in them. Vice versa, the tests show that these functions can probe the capabilities of ambipolar diffusion modules to a larger extent than the simple ZKBP solution that has been used thus far (e.g. Masson et al. 2012; Viganò et al. 2019; Nóbrega-Siverio et al. 2020b). As test functions, the various harmonics proposed in our paper have the comparative advantage that they combine the B ∝ | δ |1/3 singularity at the internal nulls (with δ the distance to the null) with the finiteness of the nonlinear diffusive flux, and this combination must be sufficiently well reproduced by the code if it is to pass the test. The ZKBP solution, instead, has a null just at the outer front, and the singularity there is of a lower order (∝|δ|1/2), with zero diffusive flux across it. On the other hand, the scarcity of tests for the ambipolar diffusion term until now is in contrast with, for instance, the case of HD shocks, for which a whole category of exact solutions is available (the solutions of the Riemann problem) that have been used to develop sophisticated numerical schemes and tests (see Laney 1998; Toro 2009). The contrast to shocks, in fact, is interesting because of the differences in their mathematical and physical nature: in shocks, it is the (magneto)hydrodynamic evolution of the hyperbolic components of the PDE that leads to the formation of the singularities, which is then smoothed through simple diffusive phenomena (typically viscosity). In the ambipolar diffusion problem, wherever there is a null, it is the diffusive phenomenon itself that creates and maintains the singularity.
Citation: Nóbrega-Siverio et al. 2020b
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Stellar properties were determined using three different techniques to model the oscillation frequencies extracted from the data. The first method relies on a dense grid of stellar models computed with the GARching STellar Evolution Code (GARSTEC; Weiss & Schlattl 2008) including the effects of microscopic diffusion, and on theoretical frequencies calculated using the Aarhus aDIabatic PuLSation code (Christensen-Dalsgaard 2008a). The results were obtained implementing a Bayesian scheme that uses the spectroscopic constraints and frequency ratios as the parameters in the fit (V. Silva Aguirre et al., submitted), the latter being almost insensitive to the surface effects in solar-like oscillators (Roxburgh & Vorontsov 2003; Silva Aguirre et al. 2011). Central values are given as the estimates of the stellar properties obtained in this manner. We also computed models using the ASTEC and YREC codes. In these cases, the fit was made to the individual frequencies after correcting for the surface effect with, respectively, an appropriately scaled version of the observed solar surface correction (Christensen-Dalsgaard 2012) and a solar-type correction as described in Carter et al. (2012). The stellar properties derived using the three techniques described above are consistent within the returned formal errors. Therefore, we added in quadrature the difference in central values of each property to the formal uncertainties determined from the GARSTEC Bayesian scheme as a measurement of the systematic spread arising from different codes and fitting techniques. In Table 2, we provide a precise estimate of the stellar age, t, from detailed frequency modeling. Values for the remaining fundamental stellar properties are consistent, within errors, with those obtained from grid-based modeling. In particular, no gain in precision was obtained for the stellar radius. On the other hand, the precision on the stellar mass is improved by nearly a factor of two, from 5.7% to 3.2%.
Citation: Roxburgh & Vorontsov 2003
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The initialization of solar flares remains an unsolved problem. Early ideas on how the initialization might occur were described by Norman & Smith (1978). They argued that flare process cannot start in the entire flare volume at one instant, and proposed that the flare onset was localized in a small part of an active region, from which the energy release extends as dissipation spreading process throughout the flare volume. Two types of agents that may lead to this kind of a dissipation process were addressed: electron beams and shock waves. These agents can trigger flares at large distances from their initial locations, causing sympathetic (simultaneous) flares or leading to a sequential flare energy release in one active region (Liu et al. 2009; Zuccarello et al. 2009). These triggering processes were numerically studied by Karlický & Jungwirth (1989) and Odstrčil & Karlický (1997). Karlický & Jungwirth (1989) assumed that electron beams, penetrating into the current sheet in the magnetic reconnection region, generate Langmuir waves. Then, using the particle-in-cell model, the authors studied the effects of these electrostatic waves on the plasma system. Sufficiently strong Langmuir waves were found to be able to generate ion-sound waves through the three-wave decay process (Bárta & Karlický 2000). These ion-sound waves increase electrical resistivity in the current sheet system, which results in the onset of the energy dissipation. Thus, the electron beams are able to cause magnetic reconnection. Odstrčil & Karlický (1997) studied the mechanism for the flare trigger by shock waves. They used a 2D magnetohydrodynamic model with the MHD shock wave propagating towards the current sheet. A portion of the shock wave passed through the sheet, and the rest was reflected. Nothing occurred at the very beginning of the wave-current sheet interaction. However, after some time, specific plasma flows around the current sheet were formed, which led to the start of magnetic reconnection. This shows that for reconnection to be triggered, the enhanced electrical resistivity as well as the plasma flows are important.
Citation: Odstrčil & Karlický (1997)
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Citation: Odstrčil & Karlický (1997)
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Active galactic nuclei (AGN) have drawn a lot of attention over the last decades because they have been beacons for the existence and demographics of super-massive black holes (BHs) throughout the history of the Universe (e.g., Soltan 1982; Kollmeier et al. 2006; Greene & Ho 2007; Schulze & Wisotzki 2010; Kelly & Shen 2013; Kormendy & Ho 2013; Schulze et al. 2015; Bañados et al. 2018). The release of gravitational binding energy via accretion of matter onto these BHs is expected to have a profound impact on the evolution of their host galaxies (e.g., Silk & Rees 1998; Granato et al. 2004; Di Matteo et al. 2005; Springel et al. 2005; Hopkins et al. 2008; Somerville et al. 2008; Fabian 2012; Harrison 2017; Harrison et al. 2018; Gaspari et al. 2019; Nelson et al. 2019). Large spectroscopic surveys such as the Sloan Digital Sky Survey (SDSS, York et al. 2000; Abazajian et al. 2009), the 2df QSO redshift survey (2QZ, Croom et al. 2004), the VIMOS VLT Deep Survey (VVDS, Le Fèvre et al. 2013) or the VIMOS Public Extragalactic Redshift Survey (VIPERS, Scodeggio et al. 2018) in combination with several deep X-ray surveys taken with ROSAT (Voges et al. 1999), Chandra (Elvis et al. 2009; Xue et al. 2011), XMM-Newton (Pierre et al. 2016), and eROSITA (Predehl et al. 2021) as well as large radio surveys (e.g., Becker et al. 1995; Condon et al. 1998; Smolčić et al. 2017; Shimwell et al. 2019; Lacy et al. 2020; Gordon et al. 2021) have provided an enormous data set to characterize the AGN population and its evolution with redshift in great detail. While the standard model for the AGN central engine has been successful in unifying the various classes of AGN appearance (Antonucci 1993; Urry & Padovani 1995; Padovani et al. 2017), some aspects such as changing-look AGN (CLAGN, e.g. MacLeod et al. 2016; Ruan et al. 2016; Graham et al. 2017; Noda & Done 2018) and tidal-disruption events (e.g., Gezari et al. 2009; Merloni et al. 2015; Auchettl et al. 2017) are just being explored more extensively in the time domain.
Citation: Harrison et al. 2018
Citation function: Motivation