adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020MNRAS.494.5056L (fulltext)
( 1 ) returned best-fitting parameters of α = 2.29 ± 0.08 and log ( N ) = −7.3 ± 0.1. The total number of degrees of freedom was 10 970. These values along with the measured Whittle statistic are provided in Table 4 . Allowing both N and C to vary between data sets did improve the fit by Δ S = 90, but there was no change to the measured slope. Additionally, allowing the fit to be driven by the low-frequency ‘red-noise slope’ by truncating the PSDs at 10 −4 Hz returns best-fitting parameters that are consistent with those listed in Table 4 . We also fitted equation ( 1 ) to PSDs generated in ‘soft’ and ‘hard’ energy bands (0.3–1 and 1–10 keV, respectively). One may expect to observe differences in the PSD at different energies based on the energy dependence of the rms variability at low and high frequencies, as shown in Fig. 7 . For example, it is clear that larger variations are observed between the slow and fast variations in the soft band compared to the hard. Subsequently, we may expect the soft band to have a steeper PSD. However, we found that the PSD does not exhibit any significant energy-dependence with best-fitting values typically remaining consistent within the uncertainties. As such, these variations are most likely contained within the uncertainties of the PSD fitting. These values are reported in Table 4 . Table 4. The best-fitting parameters of the two models (power-law and bending-power-law) fitted to the PSDs of NGC 3227 in three energy bands: 0.3–10, 0.3–1, and 1–10 keV. The fits are applied to all six XMM–Newton EPIC-pn observations from 2016. See Section 3.5 for details. We then searched for evidence of a break (or bend) in the PSD by fitting a bending power law (plus a constant): where N is the normalization, ν b is the break frequency, and and are the spectral indices below and above the bend, respectively (see González-Martín Vaughan 2012 for further details, who find that a significant bend is detected in the PSDs of 15 out of their sample of 104 AGN observed with XMM–Newton ). Fitting equation ( 3 ) to the 0.3–10 keV PSD improves the fit by Δ S = 10 for two additional free parameters, suggesting the marginal detection of a bend. The low-frequency slope, , is consistent with a value of 1, consistent with the X-ray PSD slopes observed in other Seyfert galaxies. Meanwhile, the high-frequency slope above the bend is steeper, with , where the best-fitting bend frequency is Hz, largely consistent with previous results (Uttley McHardy 2005 ; González-Martín Vaughan 2012 ; Arévalo Markowitz 2014 ). Extending this fit to the soft and hard bands, we find consistent results (Δ S = 9 and Δ S = 10, respectively) with break frequencies of and Hz and high-frequency slopes of and , respectively. Again, the bend detection is marginal and we find no evidence of any energy-dependence of the PSD. The best-fitting values are summarized in Table 4 and the bending-power-law fit is shown in Fig. 9 . In Fourier-based PSD analysis, it is
Paper: 2019ApJ...883...64L (fulltext)
panels. In the labels we show the average cross-power above 20′′. Figure 2. Stacked CIB–CXB cross-power spectra: between 3.6 μ m and [0.5–1], [1–2], [2–4], and [4–7] keV (top panels); between 4.5 μ m and CXB (bottom panels). Open circles with dashed error bars denote the absolute values of negative results. Note that the shown spectra are subtracted by the cross-power between IR and the stowed X-ray map, that is, the potential contamination from the X-ray particle background is removed from our final results. The final mean powers (used for further SED analysis) are summarized in Table 1 . As demonstrated in Cappelluti et al. ( 2013 , 2017 ), Mitchell-Wynne et al. ( 2016 ), and Li et al. ( 2018 ) the CIB versus CXB cross-power is in excess over shot noise at angular scales larger than 20′′. Since at those scales known foregrounds are significantly weaker than our signal (Helgason et al. 2014 ; Cappelluti et al. 2017 ; Kashlinsky et al. 2018 ), we therefore calculate the weighted mean cross-power above 20′′ asbetween each IR and X-ray band, for each field and for the stacked cross-power spectrum. As shown in Table 2 , the [1–2] keV band is significantly correlated with all IR wavelengths at ∼5 σ significance level, while the [0.5–1] and [2–4] keV bands are correlated with the IR wavelengths at ∼1–3 σ levels. There is no significant cross-power signal between the [4–7] keV band and any IR wavelengths so we cannot exclude an intrinsic correlation. By including the COSMOS field with a larger area, our new analysis extends the CIB–CXB cross-power analysis to ∼3000′′, doubling the largest scale reached in Cappelluti et al. ( 2017 ). The reason the highest significance is in the [1–2] keV band is due the higher S/N due to the peak in effective area of the Chandra mirror in that spectral region. Stacked results in the hard X-ray band (i.e., [2–4] keV + [4+7] keV) are not as significant as in Li et al. ( 2018 ) since deep fields add data affected by strong cosmic variance on very large scales. In the hard band most of the events are particles, so the low astrophysical hard X-ray photon statistics is another source of noise. As a cross-check we compared the significance of our stacks with those shown in Figure 17 of Li et al. ( 2018 ) for the [2–7] keV band versus 3.6 and 4.5 μ m and found consistent results. Table 2 Cross-power Spectrum Amplitude (>20′′), Calculated as a Note. a In units of 10 −11 photon s −1 cm −2 nW m −2 sr −1 . 4.2. Cross-power Hardness Ratios Similar to the X-ray color–color diagram, we look into three hardness ratios for the mean cross -power, which are the equivalent of photometric color indices, following the typical definition (Brunner et al. 2008 ) The statistical 1 σ hardness ratio errors are calculated from the mean power (Table 2 ) by error propagation. The mean 1 σ errors for the hardness ratios are ∼0.3 for HR1 and HR2 and ∼0.8 for HR3. Due to the larger errors in HR3, resulting from the uncertainties from harder
Paper: 2021ApJ...914..101W (fulltext)
region that is magnetically connected to the bow shock and is filled with ions or electrons backstreaming from the bow shock (Eastwood et al. 2005 ). The backstreaming particles can excite many instabilities, generating various waves (Johlander et al. 2018 ; Shan et al. 2018 ; Shi et al. 2020 ) and transient structures (Schwartz et al. 2018 ; Bai et al. 2020 ; Turner et al. 2020 ). In recent years, a new type of magnetic structure, electron-scale magnetic holes, has been revealed to exist upstream of the terrestrial bow shock (Yao et al. 2019a ; Wang et al. 2020b , 2020c ) as well as in the magnetosheath (Huang et al. 2017a ; Yao et al. 2017 ) and current sheet (Ge et al. 2011 ; Sun et al. 2012 ). The sizes of these holes are within 2 ρ i (ion gyroradii) (Sun et al. 2012 ; Wang et al. 2020d ), and their cross sections have complicated shapes (Liu et al. 2020b )—some are circular (Liu et al. 2019 ) or like a two-dimensional sheet (Wang et al. 2020d ). Bipolar electron velocities are often observed in the cross section, suggesting that there are electron vortices inside the structures (Huang et al. 2017a ; Wang et al. 2020c ). In contrast, electron-scale magnetic holes have little effect on the ion population (Balikhin et al. 2012 ; Sundberg et al. 2015 ). These structures can trap electrons, and have rich wave activity (Huang et al. 2017b , 2019 ; Yao et al. 2019b ). Several mechanisms have been provided to explain their generation, such as electron mirror instabilities (Yao et al. 2019a ), tearing-mode instabilities (Balikhin et al. 2012 ), solitary waves (Ji et al. 2014 ; Li et al. 2016 ), and electron vortex magnetic holes (Haynes et al. 2015 ). Since they can survive up to 100 electron gyroperiods (Haynes et al. 2015 ) and they can propagate with the ambient flow (Wang et al. 2020d ), it is difficult to identify whether an electron-scale magnetic hole in the terrestrial foreshock is locally generated or originates from the upstream undisturbed solar wind. Thus, the generation mechanism of electron-scale magnetic holes upstream of the bow shock is still not fully understood. Magnetic peaks are another kind of magnetic structure, characterized by an enhancement of the magnetic field strength (Balikhin et al. 2010 ). They widely exist in the planetary magnetosheath (Horbury et al. 2004 ; Balikhin et al. 2010 ) and in the heliosheath (Burlaga et al. 2006 ). In the heliosheath, their sizes are of the order of 20 ρ i (Burlaga et al. 2006 ). They mainly appear in the mirror-unstable region, and they are suggested to be generated by ion mirror instabilities (Soucek et al. 2008 ). Using observations of the four Cluster spacecraft, Horbury et al. ( 2004 ) studied the propagation of magnetic peaks based on timing analysis (Harvey 1998 ). The authors found that their propagation velocities are scattered around zero within the timing errors in the plasma frame, and suggested that they are stationary in the plasma frame. The authors also found that their
Paper: 2020ApJ...896..145A (fulltext)
amplitude than a denser sample (gray region). Second, if the galaxy sample is sufficiently sparse, then the genus amplitude becomes sensitive to galaxy bias (or more precisely the combination b 2 A s )—the width of the filled regions indicates the sensitivity of to bias, for given number density and fixing all other cosmological parameters. For our fiducial number density a ∼10% variation in the galaxy bias generates a ∼0.5% uncertainty in the genus amplitude (see red shaded area). One can also observe a faint redshift evolution in the red shaded region—this is due to the amplitude of the matter power spectrum decreasing with redshift relative to the shot-noise term, which we have fixed to be constant at each redshift. However, if we increase the number density of the galaxy sample by an order of magnitude (gray shaded region), the sensitivity of the genus amplitude to the bias becomes negligible—as the shot noise decreases, the genus amplitude becomes insensitive to the amplitude of the power spectrum. The redshift evolution is also suppressed by selecting a denser sample. A more general word of caution is required: the shot-noise contribution is not perfectly represented by a white-noise power spectrum with . In fact, as we stray into scales at which shot noise affects our results, the expansion of the genus in Hermite polynomials will not provide a good representation of the Poissonian signal, as the Poisson distribution has a different moment-generating function. , where is the mean galaxy separation of the catalog, is an important condition on our analysis. Shot noise has been discussed in Appleby et al. ( 2017 ) and further in S. Appleby et al. (2020, in preparation). We note that both the redshift-space distortion effect and shot noise introduce a redshift dependence to the genus amplitude. For the smoothing scales and redshifts used in this work, the redshift-space distortion effect systematically decreases the genus amplitude with increasing redshift. Conversely, the shot-noise contribution increases the genus amplitude with redshift. Both effects are and will act to practically cancel one another. To completely suppress the shot noise and bias effects, the condition is required. For the smoothing scales and number densities considered in this work, the genus amplitude is only weakly sensitive to the combination of b , A s , and P SN . Nevertheless, we must include the P SN contribution to the power spectrum to avoid systematic bias in our cosmological parameter reconstruction. Appendix B Effect of Variation of Cosmological Parameters on Measured Genus Amplitudes At numerous points in our analysis when measuring the genus curves from the BOSS galaxy catalog, we have been forced to fix the distance–redshift relation. Specifically, we smoothed the field with constant comoving scale R G = 20 Mpc, which corresponds to angular smoothing on the unit sphere. We measured the genus per unit area, and so divided the genus by the total area
Paper: 2017MNRAS.472.4327S (fulltext)
to evolve only a slice spanning ( r , θ), and with constant φ, thus assuming that the system will remain rotationally symmetric during the evolution. In order to justify to what extent and under which conditions this assumption is valid the comparison with 3D computation was needed. We will comment on that in Section 4.3 . However, within the 2.5-D approach, the φ component of the four-velocity is considered. We prescribe this component in Boyer–Lindquist coordinates according to relations given by equations ( 4 ) and ( 5 ). We chose several exemplary simulations and show their results in the form of snapshots. Every snapshot contains four panels with the slices of and its equatorial profile, ρ in arbitrary units, and λ in geometrized units, and it is labelled by the time t in geometrized units, where M is the mass of central black hole. The axes show the position in geometrized units. The red colour in the slice of radial Mach number corresponds to the supersonic motion, and blue regions indicate the subsonic accretion. The shock front is located at the place where the abrupt change from supersonic to subsonic motion occurs, hence it is represented with the white curve separating red region farther from the centre and blue region closer to the centre. The sonic curves are also white, but they lie between the blue region farther and red region closer to the centre. On top of the radial Mach number map the velocity streamlines are plotted in blue. These streamlines are computed from the velocity field at the given instant of time, hence they do not represent the actual history of a certain fluid parcel. 4.2.1 Bondi solution equipped with angular momentum At the beginning we check that for pure Bondi solution with λ = 0 we get the stationary solution of the flow with the properties consistent with the analytical solution. After that, we start to study the slowly rotating flows. For the first simulation, we pick the parameters such that λ eq belongs to the multicritical region (the region in the parameter space where both the inner and outer sonic points exist in the 1D model), but it is very close to its upper bound, in particular , λ eq = 3.6M and γ = 4/3. In that case, even when the shock solution is possible, it is not expected to appear, because the initial configuration is closer to the outer branch (there is no inner sonic point in the Bondi initial data.). In other words, we expect that the rotation of the gas affects the profile of the Mach number in the innermost region in such a way that it gets very close to 1, but does not touch it. In Fig. 2 , the initial conditions at t = 0 M are shown and the final state at t f = 10 4 M of the gas is depicted in Fig. 7 . At the later time, the simulation is already relaxed to the stationary state, which resembles the outer branch. It is interesting to note that for t = t f the Mach number actually crosses the line, however only on a very short radial range. Usually, the presence of the
Paper: 2017ApJ...849L..17W (fulltext)
power-law template used by Wik et al. ( 2014b ) to actually fit the harder high-mass binary dominated sources) gives essentially the same results. Figure 2. NuSTAR spectrum (black) within a circular region of 30′′ centered on the X-ray peak joint-fitted with the Chandra ICM (blue) and LMXB (red) spectra. The dashed lines are the individual components discussed in the text. The vertical dashed blue lines below 3 keV are the Gaussian models, to take into account the line emission. Beyond ∼5 keV, the X-ray emission is dominated by the AGN-related activities (core, knots, and jet) and is completely dominated by these activities beyond ∼15 keV. Error bars are at 1 σ . The 3–40 keV NuSTAR spectrum was joint-fitted with the Chandra ICM and LMXB spectra by including these two component models as well as a single power-law model to represent the combined emission from the core, knots, and the jet. The same Galactic absorption model was also used for all the components. We tied all the parameters for the ICM and LMXB models, except for the ICM component. The ratio of the two APEC normalizations is tied to that of the Chandra model but not the overall normalizations to allow for the cross-calibration uncertainty of NuSTAR and Chandra and the difference in response due to the much larger PSF of NuSTAR . The difference in best-fit normalizations turns out to be small, with less than a 1% difference when constraining the overall hard X-ray emission from the AGN activities (this section) and about 6% when decomposing the core emission from the knots/jet (Section 4 ). The best-fit spectra with all the model components are shown in Figure 2 . The best-fit power-law index of the overall AGN-related emission is and the associated 20–40 keV flux, which completely dominates the spectrum, is erg cm −2 s −1 . The spectrum is well characterized by a single power law with no evidence of cut-off in energy below ∼100 keV (3 σ ). 11 11 The lower limit of the cut-off energy is determined by the fitting of a cut-off power-law model ( cutoffpl). The 20–40 keV flux measured is an order of magnitude lower than that determined by de Jong et al. ( 2015 ) with the Suzaku observation taken in 2006. It is believed that the X-ray emission above 20 keV detected near the end of 2006 was due to the HST -1 flare (de Jong et al. 2015 ; Prieto et al. 2016 ). The Chandra observations in 2017 show that the soft emission (≲10 keV) from HST -1 has become fainter by a factor of ∼40 since 2006 November. By simple scaling, the contribution from HST -1 in 20–40 keV should be a factor of a few lower than that from the core. This is indeed the case where HST -1 is a factor of six fainter than the core in 2017 measured in this band (Section 4 ). Therefore, for the first time, we have detected hard X-ray emission above ∼10 keV dominated by the core, which also gives the upper limit of its quiescent emission in hard X-rays. 4. Decomposing the Hard X-Ray Emission of the Core,
Paper: 2018MNRAS.475L..35M (fulltext)
We perform this analysis for the outer and the inner (if present) spirals. On the one hand, we observe that all the outer spirals have similar pitch angles (|φ| ∼ 16°), but the simulation with T s = 20 kyr (|φ| ∼ 20°). On the other hand, we find that the inner spirals for the moving shadows (|φ| ∼ 23°) are bit larger than the planetary ones (|φ| ∼ 14°). Again, we see that the pitch angle increases with T s (see Table 1 ). In Fig. 3 , we show a synthetic image of the emission of the disc at a wavelength equal to 1 μm, where the intensity has been normalized and scaled by r 2 . This calculation is done by means of the radmc-3d code (Dullemond et al. 2012 ), modifying it to mimic the shadows by blocking the photons sent from the star in the shadowed regions. It is based on the density field for the simulation with prograde shadows with T s = 10 kyr, assuming a dust-to-gas ratio of 0.01. Figure 3. Radiative transfer calculation at 1 μm for the simulation of prograde shadows with T s = 10 kyr, shown in the third column of Fig. 2 . We assume a dust-to-gas ratio equal to 0.01. 4 DISCUSSION Moving shadows dramatically changes the disc structure and evolution. For retrograde rotations, we do not observe any spirals, despite the fact that there are two shadows cast in the disc. Because the shadows are always confronting the gas motion, there is not enough time for it to cool down in order to create a gradient pressure to act as a symmetry breaking. This could be different if a more efficient cooling mechanism is taken into account (e.g. β cooling prescription, Gammie 2001 ). Realistically, it is expected that upper layers of the gas cool down faster, resulting in a colder shadowed region able to produce a gradient pressure, promoting sharper spiral features for this mode. For static shadows, we obtain similar results as in Montesinos et al. ( 2016 ). However, in the prograde rotation case, a gas portion under the shadow moving at the same shadow's speed will always be in the shade. This corotating position can be obtained by equating the azimuthal velocity with the shadow speed v s = (2π/ T s ) r . For a Keplerian disc, where , we obtain R co = ( GM ⋆ ( T s /2π) 2 ) 1/3 . In Fig. 4 , we plot R co as a function of the shadow's period and the azimuthal velocity along with the shadow speed for the different periods T s . The intersection between both curves gives the corotating location for each model (red dots). These points are in excellent agreement with the location of the local perturbations observed in the density fields shown in Fig. 2 (see also Table 1 ). We also found that the perturbation development is independent of the stellar luminosity or the gas density. Figure 4. Left-hand panel: Shadow rotation speed ( v s = 2π/ T s r ) for different periods T s (colored dotted lines) along with the azimuthal velocity (solid curve). The intersections (red dots) give the corotating locations, in remarkable agreement with the location of the perturbations
Paper: 2016MNRAS.455.1905H (fulltext)
consuming and may suffer from a low S/N (Grier et al. 2013 ) caused by the combination of weak absorption lines, surface brightness dimming, instrument sensitivity and atmospheric transmission. In the rest-frame optical regime, the QSO and host galaxy contribution can be separated in high S/N single-aperture spectra by directly modelling them with template spectra for each component (Shen et al. 2008a , 2015 ). While a large sample can be studied with this technique, the fixed aperture size prevents such studies from measuring σ * at a physical size matched to the spheroid component. Furthermore, the reliability of the measurements decreases with increasing AGN luminosity and contrast ratio, thus limiting this technique to AGN with L 5100 ≲ 10 45 erg s −1 . To probe more luminous AGN, deep off-axis spectroscopy a few arcsec away from the QSO has been obtained to reduce the contrast between QSO and host galaxy emission (Wolf Sheinis 2008 ; Hughes et al. 2000 ; Wold et al. 2010 ). While σ * can be reliably derived from these off-axis spectra (Wolf Sheinis 2008 ), an uncertain aperture correction needs to be applied to infer comparable measurements representative for the spheroidal component. A direct decomposition the stellar continuum of the host galaxy underneath very luminous QSOs with deep optical integral-field unit observations was previously attempted with VIMOS for HE 1029−1401 (Husemann et al. 2010 ). Although HE 1029−1401 is much closer with a redshift of z = 0.086 but has a similar luminosity as PG 1307+085, the achieved S/N in the host spectrum was significantly lower than what we obtained with MUSE for PG 1307+085. This can be clearly attributed to the superior sensitivity, spatial resolution/sampling, and image quality of MUSE compared to VIMOS. These capabilities allow direct measurements of σ * with MUSE even in a high contrast regime that has not been accessible to any other techniques so far. 4.2 The nature of the large extended emission line region 4.2.1 Ionized gas shells driven by AGN outflows? A number of recent studies report kpc-scale high-velocity outflows to be common around luminous AGN with line widths of about 1000 km s −1 (e.g. Cano-Díaz et al. 2012 ; Liu et al. 2013 ; Harrison et al. 2014 ; Liu, Zakamska Greene 2014 ). Those outflows have been interpreted as spherically symmetric radiation pressure-dominated winds (Liu et al. 2013 ) and are possibly common for AGN with L bol > 2 × 10 45 erg s −1 (Zakamska Greene 2014 ). However, not all QSOs above this luminosity threshold exhibit such powerful superbubble-like outflows (e.g. Husemann et al. 2010 , 2013 ). The ionized gas kinematics of the ENLR around PG 1307+085 exhibits only small line widths with a velocity dispersion of 50 km s −1 in all the bright extended knots without any asymmetric component. This confidently rules out the AGN-driven superbubble scenario at least for this particular QSO. Although PG 1307+085 is classified as radio-quiet QSO with R 0.1