adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020MNRAS.494.3095A (fulltext)
field lines and I B / I T = O (10 −6 ) for the closed field line. Field line integrals reveal the internal structure of magnetic flux tubes. By inspecting the order of magnitude of the ratio I B / I T within the flux tube, it is possible to (i) explore the internal structure of the flux tube and (ii) verify that the field-line-averaged magnetic tension inside the tube is small. Under condition (ii), the angular velocity of the fluid enclosed in the flux tube is determined by the boundary conditions on the surface of the flux tube, which are set by the surrounding fluid. The matching to the flux tube surface occurs via viscous forces on a time-scale longer than t A . 7.3 Non-axisymmetry Non-axisymmetry arises naturally even in unmagnetized spherical Couette flow (e.g. see the phase plane in fig. 1 in Nakabayashi et al. 2002a , among many other results), and in superfluid spherical Couette flow (Melatos Peralta 2007 ). In our paper, we restrict the simulations to relatively low Reynolds number ( Re 10 3 ) and to parallel rotation and magnetic axes. We do this partly for simplicity in response to computational constraints and partly because our main application (neutron stars) is arguably a low-Reynolds-number problem with 10 −10 ≤ ΔΩ/Ω ≤ 10 −5 from measurements of rotational glitches (Espinoza et al. 2011 ). However, the low- Re assumption is certainly debatable. One can also argue that the problem is high-Re because the poorly known effective viscosity of the neutron superfluid may be lower than expected, and glitches may only relax a small fraction of the underlying shear. In that case, the flow may be fully turbulent (Melatos Peralta 2007 ), and a different style of study would be needed. Assuming low Re and parallel axes, the solution is axisymmetric, and the problem is effectively bidimensional. To test this assertion, we have performed three-dimensional runs, and verified that the solution is independent of the azimuthal coordinate φ . We have performed test runs with 180 points in the radial direction, 150 points in the θ direction, and 20 points in the φ direction for the configuration ΔΩ = 0.1 and B 0 = 1.5 × 10 −2 . Both the radial velocity and the azimuthal velocity (not plotted here) are independent of the φ variable at r = 1.04 and θ = 0.61, 0.81, and 1.21 rad. So are all the other dependent variables, e.g. magnetic field components. The system starts axisymmetric and remains so as it evolves. There are two astrophysically realistic ways to drive non-axisymmetry in the flow pattern and magnetic geometry. One way would be to tilt the magnetic axis with respect to the rotational axis, as implied by the radio polarization swings observed in pulsars. The numerical complexity of the problem depends strongly on B 0 , Re , the tilt angle α , and the grid resolution in θ and φ required to resolve turbulent meridional flows and non-axisymmetries. We perform some three-dimensional test runs with resolution 202 × 176 × 50 (in r, θ , and φ ,
Paper: 2021ApJ...909...23T (fulltext)
of the former ones at higher redshifts. Wang et al. ( 2019 ) reported, for their sample of H-dropout-selected dusty galaxies, a space density (∼2 × 10 −5 Mpc −3 at z > 3) similar to that of our high-redshift UV-dark galaxies, in the same redshift range. However, comparing the two populations, we find that our radio-selected galaxies produce stars at a rate that is on average three times higher. In fact, the median L IR of our galaxies at z > 3 is (6.3 ± 0.5) × 10 12 , which is about three times higher than the median infrared luminosity of (2.2 ± 0.4) × 10 12 reported by Wang et al. for their sample. This means that their contribution to the cosmic SFRD is also higher. In particular, it reaches 7.0 × 10 −3 M ⊙ yr −1 Mpc −3 in the redshift range 3.0 z 4.5, and 5.4 × 10 −3 M ⊙ yr −1 Mpc −3 at 4.5 z 7.7, with uncertainties of the order of 20% (Figure 4 and Table 3 ), therefore doubling the contribution of the population of H-dropouts previously cited at the same cosmic epoch (Wang et al. 2019 ). A similar result was reported for a sample of HST-dark galaxies identified at submillimeter wavelengths in the ALPINE fields Gruppioni et al. ( 2020 ). We compared our own selection criteria also with those by Wang et al. ( 2019 ), again to estimate the possible overlap between the two samples (see the Appendix ), and we concluded that at least ∼82% of the UV-dark radio-selected galaxies are not consistent with the H-dropout selection by Wang et al. ( 2019 ) and therefore constitute a different galaxy population. It is worth noting that we find a similar percentage (83%) when focusing only on the common redshift range between the two samples (z > 3). We compared the trend toward high redshift of the cosmic SFRD of our radio-selected UV-dark galaxies with the estimate based on galaxies identified in the rest-frame UV regime (Madau Dickinson 2014 ; Bouwens et al. 2020 ). 15 15 Bouwens et al. ( 2020 ) include the SFRD contribution from ULIRG-type galaxies in the ASPECS volume, derived from various submillimeter surveys. At 3.0 z 4.5, the contribution of UV-dark radio galaxies to the SFRD corresponds to ∼10-25% of the SFRD of UV-bright galaxies. This fraction rises to ∼25%–40% at z > 4.5, where we identified 22 very-high- z candidates, whose SEDs are illustrated in Figure 5 along with their redshift likelihood distributions. We stress that the actual contribution of radio-selected UV-dark galaxies to the cosmic SFRD could be even higher, because our estimates are derived without any extrapolation to lower fluxes (hence SFRs) than our selection, which would have been extremely uncertain. In fact, the correction for incompleteness, which we apply only takes into account how representative the primary sample is with respect to our total sample of radio-selected UV-dark galaxies. Figure 5. Observed (points as in Figure 1 ) and best-fit (black line) SEDs of the highest-redshift galaxies from our primary subsample (z ≥ 4.5). Error bars are plotted for each data
Paper: 2019ApJ...878...13P (fulltext)
the Ω 0 increases, the propensity for convection (colored red) within the postshock region clearly decreases. This inhibited convection results in weakened turbulent mass motion within the gain region, thereby reducing the GW amplitude at later times. Figure 6. Spherically averaged anisotropic velocity of the postshock region for the 12 M ⊙ progenitor. Brighter colors correspond to increased convection in the postshock region according to Equation ( 5 ). As rotational velocity increases, convective activity is inhibited. Traced in red is the radius of the PNS. Figure 7. Slices along the equator of the 12 M ⊙ progenitor at each rotational velocity. Colors correspond to the Solberg–Høiland stability criterion, R SH , from Equation ( 6 ). As rotational velocity increases, not only does the convectively stable band in the core grow (seen in blue), but the amount of convection within the postshock region (seen in red) decreases as well. The differences in shock radius evolution between Figure 6 and this figure arise because Figure 6 uses an angular average over the domain, whereas this figure uses equatorial slices. Furthermore, we recast our analysis by focusing on regions within the CCSN that emit GWs. The lower panel of Figure 8 displays the inhibited convective signal with increasing rotation, as the GW signal in the gain region becomes increasingly muted. The typical convective signals in the early postbounce regime are then quickly washed out by the postbounce ring-down of the PNS, as rotation increases. Figure 8. Time domain waveforms for the 20 M ⊙ progenitor. Each panel corresponds to the region from which the GWs are emitted. The large contribution in the top panel indicates the main source of GWs during the accretion phase is from the vibrating PNS. The lower panel displays the inhibited convective signal ∼50–100 ms postbounce that is characteristic of this quiescent phase. Under nonrotating conditions, the shock can grow unstable due to nonradial deformations exciting a vortical-acoustic cycle that leads to the growth of large-scale shock asymmetries, that is, the SASI (Blondin et al. 2003 ; Blondin Mezzacappa 2006 ; Scheck et al. 2008 ; Marek Janka 2009 ). In 2D simulations, the SASI excites large, oscillatory flows along both poles that drive changes in entropy capable of causing postshock convection. It is worth noting in 3D simulations that the SASI can excite “spiral” modes that correspond to nonzero m values (Blondin Shaw 2007 ; Kuroda et al. 2016 ). The high degree of nonlinearity among the hydrodynamic flows, neutrino interactions, and gravitational effects can yield matter flow that is quadrupolar, thereby resulting in GW emission. However, when the shock becomes restricted in the polar direction, due to centrifugal effects, SASI development is inhibited. To quantify the role of SASI, we decompose the shock front into coefficients based on the spherical harmonics, Y l m , according to Equation ( 9 ). Figure 9 illustrates the
Paper: 2020A&A...644A.103H (fulltext)
1997, would not be significantly contaminated by magnetic activity during a minimum. While active region inflows were not discovered at this time, the authors were still aware that magnetic activity would affect the results. Nevertheless, during this observation window, cycle 23 was ascending and the monthly sunspot number was growing from 10 to 45 (see Fig. 1 ). We investigated a similar period in cycle 24 (2009, one year after Fig. 7 ) and found inflows into active regions that persisted for a number of months. These inflows are at a weaker level than in Fig. 6 and, after three rotations, have a spatial correlation coefficient of 0.25. The EW component of these inflows is what is observed in the correlation functions of Beck et al. (1998) . We note that there is a very small window (one year centered at the minimum) in which inflows do not dominate the low-latitude flow field. Interestingly, the studies of Labonte et al. (1981) and Snodgrass Howard (1984) did not report on these long-time correlations, despite the data covering cycles 20 and 21 and the supposed sensitivity of ∼1 m s −1 per wavenumber. The reported observations from Beck et al. (1998) are consistent with ours, with the exception that they incorrectly identified the large velocity cells as convective in origin. Specifically, the EW correlations have an e-folding lifetime of approximately four months, and the extent of the flows is greater in the longitude direction. Beck et al. (1998) reported EW flows elongated in longitude with an aspect ratio greater than four. We also find EW flows elongated in longitudes, however with an aspect ratio around two (Fig. 6 ). We attribute this difference to the averaging scheme. We were careful to avoid selecting large converging flow features multiple times to avoid smearing, thus our aspect ratio is smaller. The elongation of the spatial correlation of EW flows in the longitudinal direction is not surprising given the geometry of active regions. In an argument against the convective nature of these cells, Ulrich (2001) suggested that the long-lived signals in the EW flows might be due to Rossby or inertial waves. The solar equatorial Rossby waves, discovered and subsequently characterized, are not seen in the EW flow maps at low latitudes and thus cannot cause the signals originally observed by Beck et al. (1998) and Ulrich (2001) . We find that the active region inflows generate a flow pattern with azimuthal modes up to m = 9, which is consistent with the spectral analysis of Ulrich (2001) . Ulrich (2001) found that while the rotational frequencies of these modes were consistent with magnetic features, a couple modes were slower. However, in our study, we find that, at each latitude, all modes have frequencies consistent with the rotational frequency of magnetic features and that they are absent during the solar minimum, thus indicating a magnetic association for these signals. Aware of the potential affect in Doppler images, Ulrich
Paper: 2016MNRAS.457.1522A (fulltext)
correlations between the measurements when using a common analysis aperture, the intrinsic scatter between X-ray and lensing masses, and the cosmology dependence of both. For a fixed cosmology and a sample of 12 clusters, we measure a lensing-to-X-ray mass ratio of r = 0.96 within the Chandra -determined r 2500 aperture with a statistical precision of 9 per cent and additional systematic uncertainty from lensing of 9 per cent, for a combined precision of ≈12 per cent. These results are for the specific Chandra calibrations tested, and cannot be easily extrapolated to other versions of the Chandra calibration. We find that our results are robust to perturbations in the sample selection and lensing analysis. We interpret the measured lensing-to-X-ray mass ratio as ruling out large departures from hydrostatic equilibrium, on the order of tens of percent, at the measurement radii in our relaxed target clusters. Since the effects of biases in the X-ray effective area calibration are degenerate with biases due to non-thermal pressure support, more robust constraints on the level of non-thermal support from lensing and X-ray data will not be possible without an independent constraint on the X-ray calibration, e.g. from precisely measured emission line ratios. By measuring the lensing-to-X-ray mass ratio within the cosmological framework of Paper II , we are able to render the results of that work independent of overall shifts in the Chandra temperature calibration. We are also able to naturally capture the cosmology dependence of the lensing-to-X-ray mass ratio by simultaneously modelling the lensing signal for the 12 clusters in this sample with the rest of the f gas data set. The improvements to cosmological constraints that the lensing enables are significant, as discussed in detail in Paper II . There are clear opportunities for improvement of the lensing-based calibration for the f gas experiment (as well as cluster counts; see discussion in Mantz et al. 2015a ) in the next few years. Efforts are under way to improve key lensing systematics, most notably with measuring shear (Kitching et al. 2011 ) and with mass modelling of clusters (LSST Dark Energy Science Collaboration 2012 ). With these improvements coming into place, the remaining limitation to an improved measurement of the lensing to X-ray mass ratio is the number of clusters with sufficient optical filter coverage for P ( z ) measurements, which eliminate a key systematic in the lensing masses used in this work (see Applegate et al. 2014 ). Expanding the sample of P ( z ) cluster masses from the current six clusters to ∼24 clusters would already improve the mass ratio precision, and thus constraints on Ω m , by ≈30 per cent. APPENDIX A: RATIO MODEL WITH INTRINSIC SCATTER AND COMMON APERTURES There are multiple, mathematically equivalent ways to implement a ratio model that includes intrinsic scatter and accounts for correlations induced from a shared measurement aperture. For
Paper: 2016MNRAS.462.3053B (fulltext)
heating term varies with radius, the equilibrium is different at different radii in the disc. This variation changes the minimum density required to maintain a two-phase structure in the ISM. 2.2 Star formation and feedback The star formation recipe used here is as described in Stinson et al. ( 2006 ) with some modifications. In the recipe, stars form following a Schmidt law: where ρ * is the density of new stars formed, ρ g is the density of eligible gas, is the dynamical time and c * is the chosen efficiency (see Section 3 ). Gas is considered eligible for star formation if it lies above a set density threshold, below a maximum temperature and belongs to a converging flow. The original recipe required that gas particles be below a set temperature threshold to form stars. We now compare the effective temperature associated with the total energy (including thermal and non-cooling, as discussed below) to the threshold value. This ensures that feedback will always locally limit star formation. In this work, the temperature threshold value is taken at 100 K. The key star formation model parameters that apply here are the minimum density threshold for gas to form stars ( n th ), the star formation efficiency per dynamical time ( c * ) and the feedback efficiency (ε fb ) relative to the standard amount from SNe, as described below. We employ the SN feedback method of Agertz et al. ( 2013 ). This model differs from the older blastwave model of Stinson et al. ( 2006 ) and the newer superbubble model of Keller et al. ( 2014 ) in that it is a simpler way to deposit the energy from SNe that still produces similar results and robust feedback regulation. Specifically, rather than being a complex function of local properties, the conversion time for injected energy is a set parameter. This gives us direct knowledge of how long feedback energy remains available as effective pressure and removes the associated uncertainty in interpreting our results. In more detailed models, feedback energy might take the form of pockets of hot gas, cosmic rays, radiation pressure, winds and small-scale turbulence, all injected on initially very small scales (∼pc; see e.g. Hopkins et al. 2012 ). Many of these processes are difficult to model, convert energy rapidly from one form to another and suffer from numerical effects including excess dissipation. The associated effective pressures become important for driving dynamics when they act on scales comparable to the scaleheight. A simple model ensures a well-characterized coupling of feedback energy to the dynamics on these scales. It also allows us to sidestep the unresolved issue of the relative importance of different types of feedback energy and how efficiently it converts into different forms. We normalize our energy input relative to that of SNe. A single SN deposits 10 51 erg of energy into the surrounding ISM. Based on the stellar initial mass function of Chabrier ( 2003 ), approximately one in 100 stars
Paper: 2017ApJ...844...42X (fulltext)
by a factor of 10. b The two photon indexes shown in bold, i.e., and , are for cases with and 10 −3 , respectively, where they are estimated from the relationship reported in Yang et al. ( 2015 ; see their Equation (5)), with (Ho 2008 ). Besides this statistical investigation of for LLAGNs, we have also calculated the Compton temperature of several individual LLAGNs. Such an investigation is a little too ambiguous, since most of these sources lack the hard X-ray and soft γ -ray observations to constrain the value of E c . So we use the theoretical SED results for such information, which is obtained by the accretion flow modeling to the sources (see Yuan Narayan 2014 for details of the accretion model of LLAGNs). We select several representative sources from Yuan et al. ( 2009 ). The results are shown in Table 3 . Again, we have tried three different electron temperatures. The Compton temperature is systematically higher than the “normal case” shown in Table 2 , but more consistent with the results of the “reduced-IR” case. The reason is that the IR flux calculated from the theoretical model is significantly weaker than that shown in Figure 1 , since we only consider the radiation from accretion flow and the jet. In other words, the Compton temperature derived here is applicable to the regions close to the black hole, as we argue above. Table 3 Compton Temperature of Individual LLAGNs Note. Observational data and theoretical modeling are taken from Yuan et al. ( 2009 ). We caution that the hard X-rays and soft γ -rays in these sources are actually from SED modeling because there are no direct observations at these energy bands. 4. The Total Radiative Heating and Cooling in LLAGNs In addition to Compton scattering, there are several additional radiative processes that can heat or cool the gas. In this section, we compare their relative magnitude. One process is the bremsstrahlung radiation ( and ), which always plays a cooling role. The other processes relate to the atomic energy-level transition, i.e., the photoionzation/recombination and line emission ( and ). The total radiative heating/cooling rate per unit volume can be expressed as (e.g., Proga et al. 2000 ; Sazonov et al. 2005 ) Here and n e are the number density of hydrogen atomics and electrons, respectively. For hot gas (i.e., temperature K) with solar abundance, . Consequently, the radiative heating/cooling rate can be expressed as The value of depends on both the metallicity (fixed to solar abundance in this work) and the ionization parameter ξ . The definition of ξ isHere R is the distance to the central black hole, is the ionization luminosity, and is the ionization luminosity factor. As shown in Figure 2 , we derive the ionization luminosity factors, i.e., , respectively, for the composite SEDs shown in Figure 1 with and . Detailed formulae of is provided in the Appendix (see Equation ( 17 )). It is a heating (cooling) term when ξ is large (small). Figure 2. Ionization
Paper: 2020ApJ...903...90A (fulltext)
aspects of the influence of rotation on the convective flows. Therefore, the impact of the Coriolis force on the stochastic excitation of GIWs can be evaluated. In this context, recent work has established an estimate of the GIW energy flux (André et al. 2017 ). It can be used to estimate the rotational scaling of the amplitude of the wave energy flux arising from the modified properties of the convective driving. From Equation (61) of André et al. ( 2017 ), the vertical GIW energy flux can be computed from the horizontal average of the product of the vertical velocity and pressure perturbation that, given the linearization of the Boussinesq equations for monochromatic waves propagating in a selected horizontal direction, can be evaluated to bewhere v w is the magnitude of the vertical velocity of the wave. Moreover, the solution for the vertical velocity implies that the dispersion relationship iswhere f and f ss are defined above in Equation ( 20 ). Note that a reference table is given to help identify the many parameters in this section (Table 2 ). Table 2 Frequently Used Symbols in the Models of Interfacially Excited and Reynolds-stress-induced Wave Excitation Note. The abbreviations used in the table are V for vertical, H for horizontal, and PH for projected horizontal. Following P81 , further assumptions are necessary to complete the estimate of the wave energy flux. The convection is turbulent. So the fluctuating part of the velocity field is of the same order of magnitude as the convective eddy turnover velocity , which implies that convective pressure perturbations are approximately . Assuming that the pressure is continuous across the interface between the convectively stable and unstable regions, the horizontally averaged pressure perturbations of the propagating waves excited at the interface must then be equal to the turbulent pressure on the convective side of the interface. Those pressure perturbations follow from the solution for the vertical velocity and the nondiffusive Boussinesq equations (André et al. 2017 ). For plane wave solutions, the magnitude of those perturbations can then be written asNote however, the pressure matching condition fails for these modes at the pole for sub-inertial waves (). The reason is that the propagation domain of sub-inertial GIWs excludes the pole and it becomes increasingly concentrated toward the equator for faster rotation rates (e.g., Dintrans Rieutord 2000 ; Prat et al. 2016 ). Using the dispersion relationship, and equating the two pressures, yields the following equation for the vertical wave velocity:Therefore, the wave energy flux density becomes Flows in a gravitationally stratified convective medium tend to have an extent in the direction of gravity that is much larger than their extent in the transverse directions. Therefore, the horizontal wavenumber of the convective flows is much greater than the vertical wavenumber. This implies that . For efficient wave excitation, the