adsabs/WIESP2022-NER huggingface dataset:
Paper: 2021A&A...649A..41A (fulltext)
is evaluated. The θ = 0 plane corresponds to the equatorial plane of the system. Bottom : angular ejection distribution after the most energetic interaction. Other elements in the plots are the same as in the top three plots. In contrast, we find that Pops. II and III are more isotropically distributed. These are the stars that come from outer regions on centrophilic orbits, and therefore the effect of random motion of the black holes does not play a role, like it does in the case of Pop. I. We notice that the third and especially second population show a prevalence of ejections along the SMBH binary orbital plane. This is in agreement with previous studies which find that HVSs are primarily ejected near the SMBH binary orbital plane when their SMBH orbital plane is aligned with the rotational place of the system (e.g., Sesana et al. 2006 ; Wang et al. 2014 ; Zhong et al. 2014 ; Lezhnin Vasiliev 2019 ; Rasskazov et al. 2019 ). Our findings show that not all of our three populations are uniformly distributed in the azimuthal direction. While there is some disagreement in the literature over the existence of a preferential ejection direction in eccentric binaries ( Rasskazov et al. 2019 ), in the case of a circular SMBH binary like ours, previous studies report a uniform distribution of ejected stars in the orbital plane ( Rasskazov et al. 2019 ; Lezhnin Vasiliev 2019 ; Sesana et al. 2006 ). We argue that the motion of black holes around the center can result in anisotropic ejections for the central population of stars (Pop. I), while stars originating from outside of the SMBH binary influence sphere are uniformly distributed (Pop. II and III). From the apocenter distribution in Fig. 11 , we conclude that Populations II and III correspond to stars on centrophilic orbits that are responsible for repopulating the loss cone. Depending on the shape and degree of nonsphericity of the nuclei (e.g., axisymmetric or triaxial), different orbital families may fulfil this role ( Merritt 2013b ). We can distinguish between the different orbital families if we look at their angular momentum at a time significantly before the interaction. Namely, because of the symmetries of different geometries, we can distinguish between spherical, axisymmetric, and triaxial orbits. Spherical orbits have conserved angular momentum, and in order to satisfy Eq. ( 12 ) are necessarily contained at all times within the bottom dashed square region of the top panel of Fig. 12 . Axisymmetric orbits, such as the saucer and tube orbits, in turn have conservation of the z -component of angular momentum and are therefore necessarily contained in the | L z / L crit | 1 region of phase space, denoted by vertical dashed lines in the same figures. Finally, triaxial orbits (pyramids, boxes or chaotic orbits) can have arbitrarily small or large values in the ( L , L z ) plane as a result of a lack of an integral of motion in that plane. This simple classification scheme is inspired
Paper: 2016A&A...594A..85M (fulltext)
This holds true in particular for CO line emission which is optically thick. A second parameter to explore is , the power-law index of the surface density distribution. Models with different values are presented separately in the lower panels of Fig. A.1 . The line luminosities produced by models with and are almost indistinguishable, while differences can be found comparing these with models with . In particular in the intermediate disk mass regime, models for show three different populations that reflect models with different values. This effect is less clear for models with . Finally, it is interesting to investigate how much the central star influences the CO isotopologues emission in the disk. Models with the same physical disk parameters, but irradiated by a T Tauri-like and a Herbig-like star are presented in Fig. 2 (respectively in the left and in the right panel) and in Fig. 3 . For the Herbig disks, the derived line intensities are higher for more massive disks and lower for less massive disks compared with those obtained for the T Tauri disks. Since Herbig disks are warmer, less material is frozen-out onto grains in the higher mass regime and can contribute to the total emission enhancing the line luminosity. Also, the higher temperatures enhance the moderately optically thick CO lines. On the other hand, the strong UV flux coming from the Herbig star is very efficient in photodissociating CO isotopologue molecules for lower mass disks (, ) where the column densities are not high enough for shielding the photons. This combination of high UV field and low column densities reduces the line luminosities, thus they are lower than those from T Tauri disks. 3.2.4. Dependence on gas-to-dust ratio To test the sensitivity of the results to the gas-to-dust ratio for a fixed gas mass, we ran two T Tauri disk models ( au, , , , , ) with reduced gas-to-dust ratios of ten and one. The ray-traced line intensities for CO isotopologues are presented in Fig. A.3 as functions of the gas-to-dust ratio, normalized to the value at gas-to-dust =100. For the disk the optically thin CO intensities increase by a factor of 3.5 if the gas-to-dust ratio is lowered by a factor of 100. This can be understood by the fact that if the dust mass is enhanced for a fixed gas mass, the FUV flux will be more efficiently attenuated. Accordingly CO and its isotopologues survive more easily and their lines are brighter for low gas-to-dust ratios, especially if their emission is optically thin. In addition, if the dust opacity dominates over the line mutual shielding, isotope-selective processes are minimized. The CO and CO emission on the other hand stay the same within 30% because they are more optically thick. There are also small temperature differences of order 20% between the models in the emitting layer which can affect their line intensities. For the disk the trend is opposite: line intensities decrease if the gas-to-dust ratio is lowered for all three
Paper: 2019MNRAS.486.3524K (fulltext)
et al. 2017 ). This implies that low- and intermediate-mass stars in such a late evolutionary phase are often old and hence metal-poor on average. The distributions of [Fe/H] and [S, Zn/H] are very similar in the LMC and in our Galaxy (see Fig. 7 ), indicating that depletion and hence dust-gas segregation and subsequent gas accretion, is very similar in both environments. On average the LMC has a lower metallicity than the Galaxy but it does not affect the efficiency of the depletion process. Additionally, in none of the systems, there is an evidence that the abundances of the s-process elements, systematically deviate from the abundance of elements with similar dust-condensation temperature. A good tracer of the depletion which is independent of the initial metallicity is the [Zn/Ti] abundance ratio. Zn and Ti are both Fe-peak elements and have the same nucleosynthetic history. If only nucleosynthesis determines the abundances ratios, (i.e. no selective depletion of elements), then [Zn/Ti] will remain zero. Since Ti is likely to be more strongly depleted than Zn, [Zn/Ti] is an ideal indicator of depletion. Therefore, we define the efficiency of depletion as a function of [Zn/Ti]. In principle [Zn/Fe] can also be used as a proxy for depletion; however, Ti has a higher dust condensation temperature and hence more easily depleted than Fe. So we define the efficiency of depletion as a function of [Zn/Ti]. Strongly depleted sources are defined such that [Zn/Ti] ≳ 1.5. Mildly depleted sources are defined as those with 0.5 ≲ [Zn/Ti] ≲ 1.5 while sources with [Zn/Ti] ≲ 0.5 are considered to show little or no depletion. In Fig. 8 we show the spread in [Zn/Ti] abundance ratios as a function of T eff for the LMC post-AGB sources (left-hand panel) and the depleted Galactic sources (right-hand panel). We find that the LMC as well as Galactic post-AGB stars show a large spread in depletion patterns, ranging from strongly depleted to stars with mild and no depletion. The plots also show that there is no clear trend in the efficiency of depletion with respect to T eff . The advantage of the LMC sample is their accurately derived luminosities. The derived photospheric luminosities of the LMC sample of depleted objects show that chemical depletion is active over a range of luminosities (see Section 5 and Fig. 8 , left-hand panel). Hence we can conclude that there is also no clear trend in the efficiency of depletion with respect to luminosity ( L ph ) of the star. Figure 8. Left: The [Zn/Ti] abundance as a function of T eff for the depleted sources in the LMC. Right: The [Zn/Ti] abundance as a function of T eff for the depleted post-AGB stars in the LMC (black filled circles) and their Galactic analogues (magenta filled squares). The dashed line represents the limit of what we call ‘strongly depleted’ while the dotted line marks our definition of ‘mild depletion’. In the case of the Galactic sources, majority of these objects show the presence of a stable
Paper: 2019A&A...629A...8T (fulltext)
not correspond to the velocity expected from the kinematical model presented in this work. We thus conclude that the clumps of the EHVO seen in Fig. 7 correspond to material that was swept up by a different outflow, which is faster and likely older than the HVO. Additionally, there seems to be traces of entrained material of the EHVO outflow that appear as “horns” of the HVO and confined within the region traced by the CO( J = 1 → 0) emission. Therefore, it is likely that the extension of the region traced by the CO( J = 1 → 0) emission determines the radius of CO photo-dissociation by interstellar UV radiation. 4.3 Kinetic energy and scalar momentum of molecular outflow Since our model is fundamentally different from the one presented by Sahai et al. (2017) , here we re-calculate the scalar momentum and kinematic energy of the molecular outflow. To obtain the kinetic energy and scalar momentum, in principle, it is possible to use the mass spectrum shown in Fig. 6 . However, the velocity offset in this plot is simply the component of the expansion velocity on the line-of-sight. The 3D expansion velocity is necessary for the calculations. To circumvent this problem, one can take advantage of the morphology adopted for the emitting region, which has an elongated, narrow shape. In such a case, it is possible to de-project the velocity vectors by multiplying the velocity offsets by a factor of 1/sin θ inc , with θ inc = 45°. This is not strictly true for all the velocity vectors, since the gas is not really moving along one single line. Particularly, for the gas located close to the central source, which might be expanding along the line-of-sight, the expansion velocity will be overestimated by a factor of 1/sin θ inc . For such gas, this leads to an overestimation of the scalar momentum by a factor of and for the kinetic energy by a factor of 2. In addition, there is an uncertainty due to the optical depth of the CO( J = 3 → 2) emission since it was assumed to be optically thin to obtain the mass spectrum of Fig. 6 . If the emission is optically thick the estimated value of the mass simply represents a lower limit. Having this in mind, we proceeded to calculate the kinetic energy and scalar momentum in the manner introduced by Bujarrabal et al. (2001) and described inthe following. Firstly, we multiplied the mass in each channel by the square of the corresponding velocity to obtain the kinetic energy per channel. By adding the kinetic energy of all the channels we obtain K LVC = 8 × 10 44 erg and K HVC = 1 × 10 45 erg for the LVC and HVC, respectively. Similarly, the derived scalar momentum is 1.6 × 10 38 gm cm s −1 and 6 × 10 37 gm cm s −1 for the LVC and HVC, respectively. The total kinetic energy and scalar momentum in the outflow are of the same order of magnitude as the values calculated by Sahai et al. (2017) . Subsequently, the scalar momentum in the molecular outflow transferred by the jet, Δ P , is obtained by subtracting the scalar
Paper: 2019MNRAS.484.5509E (fulltext)
the maximal error is roughly 6 times smaller than this upper limit. Table 2. In this table we compare nonlinear correction spectra computed with EuclidEmulator (EE) on the one hand and with an emulator based on Takahashi’s HALOFIT (THF) on the other hand to nonlinear correction spectra of full N -body simulations for 36 cosmologies outside the ED over the entire k and range. The numbers in the table correspond to the relative errors in per cent between the emulated and simulated nonlinear corrections. We find that the errors predicted with the THF emulator are broadly consistent with the EE errors. This implies that the configuration of the EE based on a HALOFIT emulator is actually valid and that there is no need to make a huge investment to run N -body simulations just for the configuration study of an emulator. This result is particularly important for finding the size of the ED (see Fig. 5 ). 4.2 Sensitivity analysis Sobol’ indices (Sobol’ 1993 , 2001 ) measure how sensitive the eigenvalues λ i (ω b , ω m , n s , h , 0 , σ 8 ) (introduced in equation 9 ) are to each single input parameter as well as to any of their interactions. For an introduction into Sobol’ sensitivity analysis, we refer to the previous references or to Marelli et al. ( 2017 ) and Le Gratiet, Marelli Sudret ( 2016 ). Sobol’ indices are based on the Hoeffding-Sobol’ decomposition, which states that any square-integrable function over a hypercube input parameter space can be cast as a sum of a constant, a set of univariate functions of each input parameter, another set of bivariate functions, etc. This decomposition is unique and the various terms are orthogonal with each other (with respect to the uniform probability measure over the hypercube). The variance of the output can then be apportioned to each input parameter, each pair, triplets, etc.: these contributions are called Sobol’ sensitivity indices S . Although their classical estimation relies on costly Monte Carlo simulations, Sobol’ indices can be computed analytically from a PCE as in equation ( 9 ), see Sudret ( 2008 ). In our case, we get one such Sobol’ expansion for each principal component. Each Sobol’ index corresponds to the fraction of the total variance of the respective eigenvalue that is caused by the parameter(s) under consideration. The bigger this number, the more λ i depends on the considered set of input parameters. Fig. 7 shows the first-order (no interactions) Sobol’ index plots for the first and fifth principal components (see Fig. A1 in Appendix A ). It is interesting to notice that the input parameter ω b has no leading-order effect on the output nonlinear correction. The nonlinear correction starts to show non-vanishing sensitivity to this parameter only at fifth order and higher. This is explained by the fact that baryons are treated as a background quantity that only come into the simulation data via the transfer function. As they are not directly evolved themselves in PKDGRAV3 , their
Paper: 2016MNRAS.462.2246B (fulltext)
disc. Fig. 7 confirms that the time lag increases with increasing R acc and with increasing ξ, and does not change significantly with τ r and τ w . Observations show time lags of about 50–250 Myr which are reproduced by most of our simulations, but a few of our simulations produce time lags that have values of up to 550 Myr. Such large time lags have not been directly observed yet. However, Wild et al. ( 2010 ) only measure the average time lag of a sample of about 400 galaxies with a result of 250 Myr, thus some of the galaxies involved will have time lags that exceed this average value. Fig. 8 shows SFR, BHAR and AGNAR versus time for different levels of resolution. We increase the number of particles by a factor of 2 and a factor of 4, respectively. For the former resolution level the gravitational softening length for the halo particles is 0.64 kpc and 0.4 kpc for all other particles, for the latter resolution level the gravitational softening length for the halo particles is 0.5 kpc and 0.32 kpc for all other particles. Figure 7. Time lag between starburst and AGN activity with the values of R acc , ξ, τ r and τ w from our parameter study (Table 2 ) and our reference model. Figure 8. SFR, AGNAR and BHAR as functions of time for different resolution levels. Left: particle number increased by a factor of 2, right: particle number increased by a factor of 4. The vertical lines mark the peak of the SFR and the peak of the BHAR, respectively. Both resolution levels show the same features as our reference model: the simulations produce a starburst and a peak in the BHAR, and a time lag between these phenomena of about 200 Myr. However, there are still small differences between these simulations, to further reduce these deviations additional adjustments to the respective resolution levels would be necessary. For instance, Hopkins, Narayanan Murray ( 2013 ) argue that the critical density for the onset of star formation ρ crit has to be adjusted for resolution. But as the above mentioned key features are already reproduced we refrain from such a fine tuning of our simulation parameters. 5 SUMMARY With only one model setup we were able to reproduce three observational findings that have been identified in galaxies. The observed time lag between starburst and AGN activity is, in our work, principally caused by a viscous time lag the gas needs to flow through the AGN's accretion disc until it reaches the central black hole. Our results match the observed M BH -σ correlation, but additionally include the aforementioned time lags. As, e.g. Di Matteo et al. ( 2005 ) or Debuhr et al. ( 2011 ) have already shown, AGN feedback is responsible for this relation. The large scatter of the M BH -σ correlation is, in our work, caused by the continuing evolution of the black hole mass after the merging event. The time lag does not only occur between starburst and AGN activity, but also between the peak in the AGNAR and the peak in the BHAR. Thus it is
Paper: 2018ApJ...865...63S (fulltext)
in the range 0.4%–0.5%. For the dwells with mHz detections, the symbol size is proportional to the fractional amplitude (rms). 3. Properties of mHz Oscillations Since the power spectra (Figure 3 ) from the two 2017 September dwells with strong mHz oscillations are consistent with rather coherent oscillations during these intervals, we computed phases for all events in each of these dwells separately using the frequencies corresponding to the peak Fourier power obtained from each dwell. To combine the two dwells we first found a constant phase offset for the second dwell that maximized the signal power of the sum of both dwells (Buccheri et al. 1983 ). The resulting phase-folded pulse profile in the 0.4–7.5 keV band is shown in Figure 8 . We fit a model of the form and find an amplitude B / A = 1.12 ± 0.05%. The fit is good with a minimum χ 2 = 11.0 for 13 degrees of freedom, and the best-fitting model is also plotted in Figure 8 (red curve). To explore the energy dependence we also computed profiles for different energy bands and then fit them with the same model as above. Figure 9 shows the derived amplitudes, B / A , and phases, φ 0 , versus energy. The amplitude shows a modest increase from about 0.7% at 1 keV to ≈2% at 7 keV. There is no strong evidence for a variation in φ 0 with energy. Figure 8. Average, phase-folded mHz oscillation profile from the two NICER dwells shown in Figure 2 . The profile includes all events in the 0.4–7.5 keV band. The best-fitting model, , is also shown (red curve). Phase ranges used to extract spectra are indicated by the vertical lines. See Section 3 for additional discussion. Figure 9. Fractional pulsed amplitude (top) and reference phase, φ 0 (bottom), obtained from fits to the phase-folded mHz oscillation profile are shown as a function of energy. The dashed line in the bottom panel marks the mean reference phase value. This constant value provides an acceptable fit to the distribution of values, with a χ 2 = 6.9 for 5 degrees of freedom. To explore the pulse-phase spectrum we extracted two spectra, from regions around the pulse minimum and maximum. The phase ranges used for these extractions are indicated by the dashed and dashed–dotted vertical lines in Figure 8 , respectively. In each case, we used a range of pulse phase equal to 0.25 cycles, so that each spectrum has an exposure of 0.25 × (2(2200)) = 1100 s. We used XSPEC version 12.9.1 with version 1.02 of the NICER instrument response, and we fit the spectra in the range 0.6–9 keV. We use a model comprising thermal comptonization ( comptt in XSPEC) for the persistent accretion-driven emission and a blackbody ( bbodyrad in XSPEC) to capture the neutron star thermal emission (Thompson et al. 2008 ; Chenevez et al. 2016 ). To model Galactic absorption we employ the tbabs model with abundances from Wilms et al. ( 2000 ), and we fix (freeze) the value of n H = 0.4 × 10 22 cm −2 based on prior measurements, since the source is in the same (soft) state
Paper: 2019A&A...630A..31S (fulltext)
low-density conditions ( Westermann et al. 2001 ). It allows operation at 10 −10 –10 −6 mbar and can be used in a static background mode in molecular-beam mode to simulate the outflow of cometary gases ( Graf et al. 2004 ). To calibrate aliphatic and aromatic hydrocarbons in the DFMS laboratory model, the compounds were transferred from liquid into gas phase by heating the sample tube to 323 K at low pressure in the range of 10 −6 mbar. The gas was introduced through a leak valve into CASYMIR/DFMS where measurements were performed. This procedure was repeated at three different pressure levels p = 3 × 10 −9 mbar, 2 × 10 −8 mbar, and 1 × 10 −7 mbar to cover a maximum density range. In addition, background measurements were performed and subtracted from the actual measurements. Calibration of DFMS on aliphatic and aromatic hydrocarbons was necessary for several reasons: (I) in addition to charging neutral molecules positively, the electron impact ionization used in mass spectrometry also breaks molecular chains and thus leads to a detection of the fragments and the parent molecule in the mass spectra. This process is called fragmentation and depends on the electron energy used for ionization. It results in a fragmentation pattern with species-dependent abundances, which can be used to identify or exclude species in mass spectra. However, the pattern also depends on the molecular structure of the ionized species and thus can be used to distinguish isomers such as n-butane and isobutane. Compounds such as pentane, heptane, and octane with higher masses are significantly affected, leading to complex fragmentation patterns. (II) The DFMS sensitivity on a compound is calculated from the detected number of ions. Therefore pressure conditions must be stable over the measurement period. This can be especially challenging for volatile organic compounds. Thus, a thermal valve-control system was used to stabilize the partial pressure of the component in CASYMIR during the calibration cycle. The DFMS calibration campaign included the saturated aliphatic compounds up to octane (integer mass/charge 114 u/e). The calibration campaign on butane, pentane, hexane, heptane, and octane was made with unbranched molecule version (e.g., n-pentane), while for methane, ethane, and propane, no differentiation in terms of structural isomerism appears. For benzene, the DFMS calibration was not performed because the compound is highly toxic in the gas phase. In addition to the fragmentation, the DFMS calibration campaigns were performed to derive the sensitivity of DFMS for measured compounds. Hereby the ratio of the ion current to the molecular density was derived for each pressure level. The slope between ion current and density for each of the three pressure levels is the sensitivity factor. As an alternative, when no calibration could be obtained, the sensitivity was calculated based on the ionization cross-section of the molecule. The sensitivities used in