adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018MNRAS.477..412Y (fulltext)
the system cannot fulfil thermal and mechanical equilibrium conditions simultaneously unless some further assumptions are implemented. Since the initial state is in mechanical equilibrium and the upper and the lower layers have different densities, their temperatures cannot be the same. Thermal equilibrium, however, implies that both layers have the same temperature. In order to resolve this contradiction, JK proposed that one layer is optically thin and the second layer is optically thick. They also assumed that the system is adiabatic. In the optically thin regime, where opacity on one or both sides of the interface is negligible, continuity of E r holds on whereas T g is independent of E r . There are, however, further alternative ways to specify background state self-consistently. Jiang et al. ( 2013 ), for instance, argued that even when the background state is not strictly in thermal equilibrium, its evolution affects subsequent thermal evolution very slightly so long as the thermal time-scale is much longer than the instability time-scale. Under these circumstances, therefore, the initial configuration can be imagined to be in thermal equilibrium for exploring RT instability. Jiang et al. ( 2013 ) constructed an initial state with zero absorption opacity and effectively infinite thermal time-scale. As we mentioned earlier, JK assumed that one side of interface is in the adiabatic regime, and the other side is in the optically thin regime. Here, we follow JK assumptions for specifying the initial equilibrium state, which are applicable to some astrophysical systems. The momentum and the energy equations are written as where . Equation ( 34 ) is written based on the radiative equilibrium assumption. The parameter E represents the Eddington limit of the background state. We note that the introduced parameter E is then replaced by E 1 and E 2 for the lower and the upper layers, respectively. Also, we have Introducing the ratio of the radiation pressure to the gas pressure as r = p r / p g , we can then write and We note that the Eddington limit parameter E is a fixed input parameter; however, the parameter r is not a constant under our imposed conditions. 4.2 Linear perturbations After deriving the above equilibrium solutions, we can now linearly perturb RMHD equations. Following JK , we also apply adiabatic approximation by which we mean perturbed energy flux is neglected. It means that we require F ́ ≪ E t u ́. Using this approximation, we can assume that the opacity is a constant. Therefore, the parameter E becomes a constant, which is actually our implemented assumption. This parameter, however, may be different in the upper and lower layers. We thereby arrive at the following equations: As before, we assume that all the perturbed quantities are proportional to exp (ω t + i k x x ), and the above equations after lengthy but straightforward calculations reduce to the following equations: where parameters B and C depend on the input
Paper: 2020A&A...642A.125C (fulltext)
with redshift in terms of the density parameters of both SF and AGN components of the RLF. In terms of luminosity evolution, parameters of both the SF and AGN components are consistent with being constant with redshift. We note that the normalization of the radio luminosity function of optically selected quasars by Kimball et al. (2011) and Condon et al. (2013) , shown in the first panel of Fig. 6 , is more than an order of magnitude lower than that constrained by fitting our data with the analytic RLF form of Eq. ( 8 ). The main cause of this difference is most likely different sample selection. While optically-selected quasar samples by Kimball et al. and Condon et al. contain only the unobscured quasars, sampling quasars with very high bolometric luminosities, our XQSO sample contains a combination of both obscured and unobscured quasars. By examining the hardness ratios (HRs) of XQSOs ( Civano et al. 2016 , Marchesi et al. 2016 , Pierre et al. 2016 ) and applying a threshold of HR = −0.2 to separate obscured from unobscured sources (e.g., Civano et al. 2012 ), we found that most of XQSOs (190/274) are obscured based on their X-ray emission and most likely would not be included in samples of optically-selected quasars. 5. Discussion In this section, we discuss the possible contributions to the detected radio emission: star-forming processes within the host galaxy in Sect. 5.1 and AGN activity in Sect. 5.2 . In Sect. 5.3 we define thresholds in 1.4 GHz radio luminosity below and above which more than 80% of the XQSOs are dominated by the star formation and AGN-related radio emission, respectively. This enables us to disclose the true composite nature of the XQSOs in which both star formation and AGN activity make a significant contribution to the radio luminosity. 5.1. Host galaxies of XQSOs The sample of XQSOs studied in this work was drawn from the moderate-to-high radiative luminosity AGN sample in COSMOS and high-excitation radio galaxy sample of AGN in XXL-S. Host galaxy properties of these AGN have been studied by Delvecchio et al. (2017 ; COSMOS) and in Paper XXXI (XXL-S). Both studies found that the host galaxies of these AGN have star formation rates (SFRs) and stellar masses consistent with typical main-sequence galaxies. The same conclusion was reached by Stanley et al. (2015) from an analysis of a sample of ∼2000 X-ray selected AGN (10 42 erg s −1 L 2−8 keV 10 45.5 erg s −1 ) detected in the Chandra Deep Field North and South and the COSMOS field. They studied the distribution of star formation-related IR luminosity and X-ray luminosity over 0.2 z 2.5, as proxies of SFRs and AGN activity, respectively. They found a strong evolution of their average L IR, SF with redshift, similar to that observed for the main-sequence galaxies. These results were confirmed by multiple other studies of quasar and AGN host galaxy properties (e.g., Rosario et al. 2012 , 2013 , Lanzuisi et al. 2017 , Stanley et al. 2017 , Suh et al. 2017 , 2019 ).
Paper: 2020ApJ...905..119F (fulltext)
from isolated cold globules to rich clusters. There are two common observational measures of environment. First, there are the environmental conditions set by the properties of the natal molecular gas, particularly the dense gas. Second, there are environmental variations traced by the protostars, including the densities of young stars and the systematic changes in their properties. These protostars and their properties have been quantified with Spitzer data (Megeath et al. 2012 , 2016 ) and Herschel data from HOPS (Furlan et al. 2016 ). With Herschel observations at longer wavelengths and of wider fields than included in HOPS, Stutz Kainulainen ( 2015 ) measured the column density ( N H ) and mass distributions across Orion A, quantifying environmental differences. They found that the column density probability distribution functions vary with location. Furthermore, Stutz Gould ( 2016 ) and Stutz ( 2018 ) found that the mass per unit length ( M / L ) varies significantly across Orion A, with the Orion Nebula Cluster (ONC) having a higher M / L than the Integral-Shaped Filament (ISF), and the ISF having a higher M / L than LDN 1641. These column density and mass variations imply variations in the volume density and gravitational potential across the cloud. Environmental variations are also found in the radial velocities of the gas. González Lobos Stutz ( 2019 ) showed that gas radial velocities in both high- and low-density tracers show significant variations within the ISF. In particular, the northern portion has more centrally concentrated gas with stronger overall velocity gradients compared to the southern portion, which transitions to the LDN 1641 region. Systematic variations of the temperatures, line widths, and densities of cores across Orion A were also found by Wilson et al. ( 1999 ). Turning to the stellar content, the observed properties of the protostars vary with environment. Kryukova et al. ( 2012 ) found variations in the Orion clouds, where protostars are more luminous in dense regions. Stutz Kainulainen ( 2015 ) found that the fraction of protostars in the young Class 0 phase varies systematically with changes in environment as indicated by variations in the slope of the dust column density probability distribution function ( N -PDF) across the region. The variation in this fraction may result from differences in the star formation history between these regions, as also suggested by Fischer et al. ( 2017 ), the influence of feedback (Sadavoy et al. 2014 ), or variations in infall rates of the protostars due to variations in the densities of their birth environments (Kryukova et al. 2012 , 2014 ; Dunham et al. 2014 ). This paper is part of a series describing results from HOPS. These papers include evolutionary studies of protostars via modeling of their SEDs (Furlan et al. 2016 ; Fischer et al. 2017 ), the analysis of far-IR protostellar spectra (Manoj et al. 2013 , 2016 ), the discovery and characterization of the
Paper: 2018ApJ...852..116K (fulltext)
The photometric and log g pairs and final adopted , log g , ξ , and v sin i of the stars are tabulated in Table 2 . Figure 1. Comparison between the observed (black points) and synthetic (colored solid lines) H β profiles of the stars for the adopted atmospheric parameters given in Table 2 . Figure 2. Determination of the microturbulences by minimizing the standard deviation of . Table 2 Derived Atmospheric Parameters and Rotational Velocities of the Stars Notes. a The data are from Paunzen ( 2015 ) for HD 23193, and Hauck Mermilliod ( 1998 ) for HD 170920. b The data are from Hauck Curchod ( 1980 ). c The data are from Høg et al. ( 2000 ). d E ( B−V ) = 0.07 for HD 170920. 3.2. Chemical Abundances from Spectrum Synthesis For the spectrum synthesis, we used the atomic line list of Kılıçoğlu et al. ( 2016 ) compiled from Kurucz’s Line Database, 4 4 http://kurucz.harvard.edu/linelists/gfhyperall/gfhyperall.dat VALD (Piskunov et al. 1995 ; Ryabchikova et al. 1997 ; Kupka et al. 1999 , 2000 ), the NIST atomic database (Kramida et al. 2015 ), and various available sources of the oscillator strengths. We initially selected unblended (or slightly blended) lines of the elements (141 lines for HD 23193, 281 lines for HD 170920) in the high-resolution spectra of the stars. We then fitted these selected lines with synthetic line profiles produced by SYNSPEC49 and its SYNPLOT interface. The code was modified to minimize the between the model and the observed points, using the Levenberg–Marquardt algorithm (Markwardt 2009 ). The abundances of the elements were adjusted until the best-fit between the synthetic and observed line profiles. The synthetic spectra were broadened by the rotational velocity, microturbulence, and instrumental profile. We computed the ATLAS9 model atmosphere to derive the abundances of both stars. For the chemically peculiar Am star HD 23193, we also computed an ATLAS12 (Kurucz 2005 ) model atmosphere based on the derived abundances with ATLAS9, and refined its elemental abundances. 4. Evolutionary Status Using the effective temperatures (derived in this study) and the luminosities (calculated from the parameters given in Table 3 ), we plotted the target stars on a theoretical HR diagram (Figure 3 ). We used the evolutionary tracks having various masses (i.e., 2.2, 3.0 M ⊙ ) and solar metallicity to estimate the masses of the stars (Salasnich et al. 2000 ). For their ages, we considered the isochrones with various ages (i.e., 430, 530, 600, and 665 Myr) and solar metallicity (Bressan et al. 2012 ). We found a mass of 2.3 ± 0.1 M ⊙ for HD 23193, and 2.9 ± 0.1 M ⊙ for HD 170920. The estimated age of HD 23193 is 635 ± 33 Myr, and the age of HD 170920 is 480 ± 50 Myr. Figure 3. Location of the stars on the HR diagram. Table 3 Calculated Parameters of the Stars for the H-R Diagram Note. , π , and BC are from Høg et al. ( 2000 ), van Leeuwen ( 2007 ), and Torres ( 2010 ), respectively. As shown in Figure 3 , the age (635 ± 33 Myr)
Paper: 2017MNRAS.466.2402S (fulltext)
and the shape noise simultaneously, while Ling etal. ( 2015 ) have focused on the surface mass density at z 0.1. Furthermore, we find that the difference of the lensing MFs between the CDM and the HS model has the similar trend to a change of 8 (e.g. see fig. 2 in Shirasaki Yoshida 2014 ). Figure 4. Impact of f ( R ) gravity on the Minkowski functionals (MFs). The three panels represent the results of V 0 , V 1 and V 2 . In every panel, the error bars represent the standard error of the average (i.e. the standard deviation divided by ). Also, coloured points represent the average MF over 100 realizations for the three models, while the bottom portion shows the relative difference of MF between CDM and f ( R ) models. The dashed line shows the Gaussian prediction for CDM model. 5.2 Detectability of imprint of <inline-formula><tex-math id="TM0082" notation="LaTeX"></tex-math></inline-formula> gravity In order to quantify the detectability of f ( R ) gravity in a given statistical quantity, we start by writing a measure of a goodness of fit: where represents a theoretical model of cosmic shear statistic at the i th bin of x for a cosmological model that one wishes to test, is an observed statistic drawn from the true unknown cosmology and C is the covariance matrix of the observed data vector . In our case, corresponds to either the power spectrum, bispectrum, peak counts or MFs, while x refers to the multipole , the peak height or the normalized convergence depending on the statistics. In what follows, we also consider a data vector composed of different statistics when we examine parameter constraints from joint analyses of more than one statistic. In such cases, we properly take into account the off-diagonal components relevant to the two statistics of interest in the covariance matrix. When follows a multivariate Gaussian distribution and if we assume the correct model in , the quantity defined by equation ( 38 ) follows the 2 distribution with the degree of freedom of N bin 1 as the name suggests, where N bin represents the total number of bins for the observables . Borrowing the idea behind equation ( 38 ), which compares the levels of estimated (in the form of a covariance matrix) and measured (the actual scatter around the mean) cosmic variances, we define a similar quantity to assess the statistical power to constrain f R 0 by replacing the numerator with the difference of the expected statistics in two models: where we consider the f ( R ) cosmology characterized by f R 0 and the fiducial CDM cosmology. One can assess the discriminating power of the statistic by the (S/N) 2 defined above. Note that this quantity does not depend on the binning scheme explicitly, as long as we take a binning fine enough not to miss important features in the statistics. In the absence of degeneracy between different cosmological parameters, one can straightforwardly convert the (S/N) 2 to the expected level of constraint on f R 0 ; (S/N) 2 4 for f R 0 10 5
Paper: 2017MNRAS.468.3381A (fulltext)
both the expansion history, due to the scalar field, and the evolution of the density perturbation amplitudes, due to the coupling. 3 SIMULATIONS For our study, we produced simulations of the following three cosmologies: a reference CDM, an uncoupled quintessence model and a CDEdark matter model with a coupling parameter 0 . Following Elahi etal. ( 2015 ), we choose ( h , m , b , 8 )(0.67, 0.3175, 0.049, 0.83), ensuring all cosmologies have parameters consistent with z 0 CDM Planck data (Planck Collaboration XVI 2014a ; Planck Collaboration XIII 2016a ). The coupling parameter, 0 0.05, in our simulation is chosen to test the boundaries of allowed coupling (see Pettorino etal. 2012 ; Xia 2013 ) in order to maximize any observational differences that may exist between this cosmology and the standard CDM model. The linear power spectrum and growth factor f dln D ( a )/dln a are calculated using first-order Newtonian perturbation equations and the publicly available Boltzmann code cmbeasy (Doran 2005 ). We note that although these simulations have neglected star formation and feedback physics, baryons do not appear to significantly change the void population. Large underdense regions remain underdense, regardless of whether the simulation includes dark matter only, or accounts for hydrodynamic or full baryonic physics (Paillas etal. 2016 ). Initial conditions are generated by perturbing particles placed on a Cartesian grid with the first-order ZelDovich approximation using a modified version of the publicly available n-genic code. The modified code uses the growth factors calculated by cmbeasy to correctly calculate the particle displacements in the non-standard cosmologies. All the simulations are started at z 100 with the same phases in the density perturbations. Choosing the same initial phases results in underdense regions forming in the same locations in all simulations. Each underdense region in a given simulation has a corresponding underdense region in other simulations (perhaps with different properties such as density or size, depending on cosmology). This is the case despite potentially different exact profiles of density perturbations between cosmologies with different power spectra, such as CDM and CDE. We use dark-gadget , a modified version of the p-gadget -2 N -body code (for more details, see Carlesi etal. 2014a ). The key modifications are the inclusion of a separate gravity tree to account for the additional long-range forces arising from the scalar field, and an evolving dark matter N -body particle mass that models the decay of the dark matter density. The code requires the full evolution of the scalar field , the mass of the dark matter N -body particle and the expansion history. Our simulations are 500 h 1 Mpc boxes containing 2512 3 particles (dark matter and gas) giving a mass resolution of m dm ( m gas )6.9(1.3)10 10 h 1 M . 1 Though the mass resolution is poor for identifying haloes, this resolution is more than enough to
Paper: 2017MNRAS.468..751E (fulltext)
between the two models, though the BHWR model has a slightly higher duty cycle than the onyWind model that matches the correspondingly higher SMBH mass growth (compare Fig. 3 ). Figure 5. Time distribution of different accretion rates in units of the Eddington limit (so-called BH duty cycles) for the different models (see legend) compared to observations (square: Ho 2009 , circles: Greene Ho 2007 , upward-pointing triangles: Kauffmann Heckman 2009 , downward-pointing triangles: Heckman etal. 2004 , all taken from Novak etal. 2011 ). Both simulated models fall above all of the observed values at Eddington ratios of 1 and 10percent. 4 BH-GOVERNED ISM EVOLUTION IN THE GALACTIC CENTRE In the previous section, we have shown that feedback from the central SMBH is both necessary to, and capable of keeping an isolated ETG quiescent. The more physically complete feedback model, including both wind and radiative feedback, is more efficient at this than the pure wind-feedback model. In this section and the next, we will now examine how the feedback achieves the quiescence through its influence on the galactic gas, and how it creates metal-rich, large-scale outflows that enrich the CGM. We begin with Fig. 6 , which shows colour maps of the surface density (top row) and the density-weighted average temperature (central row) in a 10kpc thick, 10 10kpc wide region around the galactic centre (and therefore around the central SMBH), as well as the temperaturedensity phase diagrams of all the galactic gas (bottom row). In the maps, the line of sight is chosen to be parallel to the initial angular momentum of the hot halo. The solid vertical and horizontal lines in the phase diagrams mark the star formation thresholds in density and temperature, respectively, i.e. gas in the bottom-right corner of the diagrams is star forming. The individual temperaturedensity bins are colour-coded for gas particle number density, which is rising from blue to red colours. The dashed line marks the resolution of the simulation limit following the Jeans mass criterion. Above the dashed line, the Jeans mass of the gas is resolved, i.e. it is larger than the kernel mass M ker N ngb m gas 10 7 M , while it is unresolved below the line, meaning that gas in this region could collapse under self-gravity due to numerical noise. Almost all of the gas particles with unresolved Jeans mass are in the star-forming region of the phase space, and will be turned into star particles if they stay in this region. Hence, while this resolution does not allow us to make predictions about the substructure of the dense, cool gas, it is high enough to investigate the global and larger scale effects of AGN feedback that interest us in this paper. The three left columns show the state of the three models (from left to right: no-BH, BH-W, BH-WR) at the end of the simulation time, while the last column to the right shows the state of the BH-WR model about 30Myr before the end of the simulation. Figure 6. Maps
Paper: 2018MNRAS.475.5231L (fulltext)
relative to the forcing from m p is Even with the uncertainties of various parameters, the inner two planets are weakly coupled to the stellar spin. The stellar obliquity (relative to ) therefore varies between |θ ⋆ p − θ 12, p | and θ ⋆ p + θ 12, p (see equations 12 – 13 ; note that since L p ≫ L 12 = L 1 + L 2 for this system, is aligned with ). The observed stellar obliquity (λ ≥ 37°) then implies θ ⋆ p + θ 12, p ≥ 37° (see Li et al. 2014 , for a previous analysis). 5 DISCUSSION The main goal of this paper is to inform the readers that there is a simple way to assess the dynamical role of external perturbers on the spin–orbit misalignments of planetary systems. The key is to evaluate the star–planet spin–orbit coupling parameter (equation 9 ) in response to the differential precession induced by the external companion. We have presented analytic results in various limiting regimes, and discussed how the general results (see Fig. 1 ) scale with different physical parameters (Section 2). Similar analysis can be done for multiplanet systems (Section 4), with the generalised spin–orbit coupling parameter given by equation ( 31 ). As alluded to in Section 1 , continuing observations on spin–orbit misalignments and external companions in HJ and other compact planetary systems may shed light on the formation mechanism of close-in planets. Several recent works of such systems have either neglected some key physical ingredients (spin–orbit coupling) or made such a relatively simple problem unnecessarily complex or obscure. We hope that the results of this paper (see also Lai 2016 ) will make future analysis simpler and more transparent. A recent analysis on spin–orbit alignments of HJs around cool stars concluded that the exterior companions are coplanar (Becker et al. 2017 ). While this may be the case of a few systems with gas giant companion at ∼1 au, similar conclusions cannot be drawn for systems with a slightly more distant companion (see Section 3 , especially Fig. 2 ) since the spin–orbit coupling parameter ε ⋆1 depends sensitively on the perturber’s semimajor axis (; see equation 24 ). Certain HJ formation mechanisms (e.g. LK migration) require an external companion with a high inclination (>40°). As a result, constraints placed on the mutual inclination via the method discussed in this paper may in principle lead to a constraint on the migration mechanism. However, we emphasize that the inclination of giant planet companion may only be constrained when it is relatively close (at ∼1 au, so that ε ⋆1 ≳ 1, see equation 24 ), such as for WASP-41 and HAT-P-13. These systems clearly could not have formed in the classic LK migration picture (with the HJ originating at ∼1 au or beyond), because the initial configuration would be unstable. LK migration requires a much more distant companion (at, say ∼5 − 10 au, so that the initial configuration is stable). Such a companion will satisfy ε ⋆1 ≪ 1, so that it is unable to affect the star-HJ