adsabs/WIESP2022-NER huggingface dataset:
Paper: 2021MNRAS.503.5740K (fulltext)
α and are the polar and azimuth angles of the wave vector of a transverse electromagnetic wave. In this case, the angle α is the observation angle. The spectral power of radiation at the emission frequency Q ω can be obtained in accordance with the expression for the total radiation power: Comparing expressions ( 4 ) and ( 5 ) we obtain: In our work, we will not apply head-on approximations, which, as noted in the work of Willes et al. ( 1996 ), can be violated at small wavenumbers of Langmuir waves and their narrow spectra. We will also not use the narrow-spectrum approximation proposed in Willes et al. ( 1996 ). The cited work also contains a scheme with the spatial orientation of the wave vectors of the merger of Langmuir waves and the generated transverse wave. In order not to limit ourselves to particular cases, we present the results of calculations according to direct expressions ( 2 ), ( 3 ), without using the simplifications usually inherent in these models, which are described in detail in Willes et al. ( 1996 ). Let us now consider radio emission at different spectra of Langmuir waves. Taking into account the work of Willes et al. ( 1996 ), we consider the case of axial symmetry of the radiating region and the spectrum of Langmuir waves in the form: where B is the normalizing coefficient in accordance with the normalization condition: W is the total energy density of Langmuir waves; and φ are the polar and azimuth angles of the Langmuir wave, where the angle is calculated relative to the axis of symmetry of the radiating region, δ is an even positive number. Let us first consider the case when , i.e. when does not depend on the Langmuir wave vector. Fig. 1(a) shows the distribution of radiation ( Q k ) over the wavenumber for different viewing angles α at δ = 2, k max = ω pe /(3 V T e ), k min = ω pe /(15 V T e ), and T e = 3 × 10 6 K, n = 10 16 m −3 . These values of k max and k min are chosen for the following reasons: Langmuir waves with a value greater than k max are effectively absorbed by thermal electrons as a result of Landau damping, and therefore we will not consider them. Langmuir waves in plasma are usually generated by beams of superthermal electrons, so we consider the region of phase velocities up to 15 V T e and assume that there are no Langmuir waves with large phase velocities (smaller wavenumbers). The observation angle α is the angle between the symmetry axis of the radiating region and the vector k . In this case, we assume that k 0 = ( k min + k max )/2, and σ is less than the value of k 0. Fig. 1(b) shows the distribution of radiation ( Q ω ) as a function of the frequency of the transverse electromagnetic wave. The calculation results for this case are shown in Figs 1 and 2 for different σ. At a small standard deviation (e.g. σ = 0.1 k 0 , Figs 1a , b ) the radiation is concentrated in a narrow frequency range. A similar type of Langmuir spectra can be generated, for example, by a fast electron beam. With
Paper: 2017ApJ...849L..34P (fulltext)
and the fraction of mass of formed stars that are NSs iswith being the IMF, Ψ( t ⋆ ) is our best-fit SFH, and Θ NS ( M ⋆ ) is 1 for star mass ranges of 8 M ⊙ M 20 M ⊙ , and zero otherwise. We drop the metallicity dependence in Θ NS because we only consider a solar metallicity for the galaxy, as a result of our spectroscopic fit. t ⋆ is the time when the progenitor of the NS was formed, therefore satisfying t ′ = t ⋆ + t life , with t life being the lifetime of the progenitor before becoming an NS. We assume a t life = 0.02 Gyr, but our calculation is insensitive to this choice as the typical lifetime of these massive stars (∼0.01–0.03 Gyr) is much shorter than the timescale over which the SFH found for NGC 4993 is changing at late times. We assume a Chabrier IMF, but this choice is not relevant, as we are only exploring the high-mass end of the IMF. Assuming α = 0.002 and the distribution of Δ t NSM from Vangioni et al. ( 2016 , their Figure 3 for solar metallicity), and our best-fit SFH from Equation ( 1 ) with t 0 = 3 Gyr and τ = 0.3, we get an NS formation rate of and a BNS merger rate of for the whole galaxy. Errors reflect the uncertainty on the SFH, which dominates our errors: they represent the two central quartiles of the rates distribution computed with the SFHs of the pixel SED fitting over the galaxy. Given the sensitivity of the BNS merger event rate to the recent SFR of a galaxy, it is somewhat surprising that GW170817 occurred in an old, early-type galaxy. We therefore ask what is the probability of observing such an event in any early-type galaxy within the LIGO-detectable volume. To make this estimate, we integrate the stellar mass function of early-type galaxies from Weigel et al. ( 2016 ) and scale the per-solar-mass rate from Equation ( 2 ) to the mass contained within the LIGO-detectable volume (radius 80 Mpc). We find resulting in expected events. This calculation assumes that the SFH of NGC 4993 is representative of local early-type galaxies. In fact, much of the mass will be contained in more massive, and on average older and less star-forming, galaxies. We contrast this with a similar calculation for all galaxy types, using the cosmic SFR density from Gladders et al. ( 2013 ), finding and ∼0.5 expected events. This result shows that it is unlikely that we observed one such BNS merger with LIGO over the combined nine months of operations in an early-type galaxy. The assumptions in the calculation include the fraction of NS that form in binaries ( α = 0.002) and the delay time distribution, both coming from binary star models (where the progenitors of the BNS were already a bound system) and satisfying Milky Way constraints. If the BNS formation mechanism is via dynamical interaction, our result could point to a higher value of α or a shorter Δ t NSM for systems that recently underwent a galaxy merger, more so for those that have high stellar density (such as early-type galaxies). It is therefore of interest to know the
Paper: 2018MNRAS.474.1206B (fulltext)
(e.g. Narayan et al. 2012 ; Yuan, Wu Bu 2012b ; Yuan et al. 2015 ). In the hydrodynamic (HD) simulation, there is no magnetic field, wind is found to be driven by the combination of gas pressure gradient and centrifugal forces. In the magneto-hydrodynamic (MHD) simulation, it is found that wind is driven by the combination of gas pressure gradient, magnetic pressure gradient and centrifugal forces (Yuan et al. 2015 , see also Moller Sadowski 2015 ). Recently, observations of both LLAGNs (e.g. Tombesi et al. 2010 , 2014 ; Crenshaw Kramemer 2012 ; Wang et al. 2013 ; Cheung et al. 2016 ) and the hard state of black hole X-ray binaries (Homan et al. 2016 ) show that winds are present in hot accretion flows. The reason for the existence of wind is also investigated by analytical works (e.g. Begelman 2012 ; Gu 2015 ; Mosallanezhad, Bu Yuan 2016 ). Bu et al. ( 2016a , b ) study whether wind can be produced beyond the Bondi radius. In these studies, the gravity of nuclear star cluster is taken into account. It is found that the radial profile of the gravity becomes flatter when stars gravity is taken into account. Also, it is found that wind cannot be produced in the presence of star gravity. Wind production can occur only within the Bondi radius of hot accretion flows. The simulations introduced above neglect radiative cooling. Non-radiative simulations of hot accretion flows can only be applied when the black hole accretes at very low rate, , where is the Eddington accretion rate (Sadowski et al. 2017 ). With the increase of mass accretion rate, radiative cooling effects will become important. Strong radiative cooling can decrease the gas pressure. As mentioned above, gas pressure gradient is an important force for driving wind. We can expect that when radiation becomes strong, wind should become weak. In this paper, we study how the wind strength changes with mass accretion rate. We are interested in wind due to the following two reasons. First, wind can take away mass, which can change the density profile of accretion flows. The change of density profile can affect the emitted spectrum of an accretion flow (e.g. Quataert Narayan 1999 ; Yuan, Quataert Narayan 2003 ). Secondly, it is now widely believed that AGN feedback plays an important role in the evolution of their host galaxies (e.g. Ciotti Ostriker 1997 , 2001 , 2007 ; Proga, Stone Kallman 2000 ; Proga 2007 ; Kurosawa Proga 2009 ; Novak, Ostriker Ciotti 2011 ; Gan et al. 2014 , 2017 ; King Pounds 2015 ; Ciotti et al. 2017 ). Wind can push away the gas outside an AGN. This can reduce the feeding rate of the central black hole. Also, the star formation rate may also be changed (e.g. Ciotti et al. 2017 ). In order to study wind feedback, we first need to study the strength of wind. The effects of radiative cooling on hot accretion flow have been taken into account recently (e.g. Wu et al. 2016 ; Ryan et al. 2017 ). In these papers, the effects of radiative cooling on wind strength have not
Paper: 2020MNRAS.498.1983T (fulltext)
we do not consider the spherical reprocessor further. Figure 6. The spherical reprocessor of Brightman Nandra ( 2011 ), compared to the data. Colours as for Fig. 5 . 5 MYTORUS The toroidal X-ray reprocessor model, mytorus (Murphy Yaqoob 2009 ; Yaqoob et al. 2010 ), is illuminated by a central X-ray continuum source. The global covering factor of the reprocessor is 0.5, corresponding to a solid angle subtended by the structure at the central X-ray source of 2 (an opening half-angle of 60 o ). mytorus self-consistently calculates the Fe K α and Fe K β fluorescent line emission and includes the effects of absorption and Compton scattering on both the continuum and lines. mytorus offers the ability to leverage constraints on the Fe K line emission and spectral curvature together, to obtain both the global and line-of-sight column density. The model is therefore one of the most powerful available for fitting X-ray spectra. Although the nominal covering factor is fixed at 50 per cent of the sky as seen by the continuum source, one can model other physical possibilities using the ‘decoupled’ mode that allows for a different line-of-sight column to the gas responsible for reflection (the Fe line emission strength can still be linked self-consistently to the column of the reflector). Such a decoupling is often required in spectral analyses of high-quality broad-band X-ray spectra, because the column density in and out of the line-of-sight column density are not always the same (e.g. Risaliti et al. 2009 ; Marchese et al. 2012 ; Yaqoob et al. 2015 ). In our fits, a single power law was assumed to be seen in transmitted and scattered light, and this was implemented by linking the photon indices of these components in all fits. The general model form was where MYTZ designates the line-of-sight absorption, MYTS the scattered X-rays, and MYTL the line emission (both from material out of the line of sight, hereafter referred to as the ‘global’ gas), from mytorus . zg(10) represents 10 individual Gaussian lines (Table 2 ) present in the data that are not accounted for by the mytorus model. gs is a Gaussian smoothing component convolved with the torus neutral Fe line profile, using a kernel of σ = 30 eV at 6 keV. The power-law continuum is split over two sightlines, one is absorbed my MYTZ and the other is absorbed only by N H , a full-covering column of gas whose lower limit is set at the Galactic line-of-sight absorption, but whose upper limit is not constrained. 5.1 Coupled model fits Taking the ‘Coupled Model’ to mean one for which the line of sight and global column densities are tied together, we performed two variations on this fit. First, the angle of the global gas was fixed at θ = 90 o and the normalization of the global-scattered light and line emission were directly tied to the illuminating continuum. This model did not provide an acceptable fit to the data, yielding χ 2 = 709/438 dof with , while the full covering gas component gave , exceeding
Paper: 2017ApJ...841...17O (fulltext)
light reflections. To further reduce the negative effects of the background filament radiation, its spectrum was recorded under high vacuum conditions (5 × 10 −8 mbar) and subtracted from measured molecular spectra. The electron energy was calibrated by measurement of the cross section of the (0, 0) band of (C 3 Π u –B 3 Π g ) at 337 nm (Zubek 1994 ; Országh et al. 2012 ) and the He i (1s2p 3 –1s4d 3 D 1,2,3 ) 447.14 nm emission line (NIST 2015 ). The (C 3 Π u –B 3 Π g )(0, 0) cross section exhibits a pronounced peak at 14.1 eV. The cross section for the He i (1s2p 3 –1s4d 3 D 1,2,3 ) transition exhibits a step at 23.736 eV. The electron energy calibration was performed in the mixture of C 2 H 2 :N 2 :He 1:1:1 (established by an absolute pressure gauge) to avoid problems with different charging effects of the electron monochromator electrodes in different gases. The difference between energy scales calibrated by nitrogen and helium was less than 0.015 eV. Two different measurement modes were used in the present study: (i) fluorescence spectra induced by monoenergetic electrons at fixed electron energy, and (ii) PECs—the intensity of photons of a specific wavelength detected as a function of the electron energy, sometimes referred to as the relative excitation function. PECs are proportional to a dissociative excitation-emission cross section (DEE CS) and can be calibrated to its absolute values. Additionally, PEC curves can be used for determination of the apparent thresholds in PEC, which are related to the thresholds for the dissociative excitation reactions (Stano et al. 2003 ). 3. Results and Discussion 3.1. Spectrum We have recorded the electron-induced UV–VIS spectrum of acetylene in the spectral range from 190 to 700 nm. The spectral range 190–257 nm was recorded at an electron energy of 70 eV and low resolution of the optical monochromator (2 nm FWHM), and the rest was recorded at 50 eV and higher resolution of the optical monochromator (0.4 nm FWHM). The spectral range 305–530 nm is presented in Figures 2 (a) and (b). All the spectra presented in this paper were corrected for the spectral response of the apparatus. The intensities in the spectra were calibrated to absolute values according to the known emission cross sections for the dissociative excitation-emission channel, which we will give more details on later. The spectra exhibit lines and bands corresponding to dissociative excitation of acetylene by electron impact (Beenaker et al. 1975 ; Tsuji et al. 1975 ). Figure 2. Acetylene spectrum in the wavelength region of (a) 310–420 nm and (b) 420–660 nm at electron energy 50 eV with slits set to 100 μ m (optical resolution ∼0.4 nm). Due to the lower sensitivity of the optical system in the region 190–257 nm, it was necessary to decrease the resolution of the optical monochromator (using 500 μ m for both slits). We were able to identify two C i lines at 193.1 nm (C i (2s 2 2p3s 1 P 0 –2s 2 2p 2 1 D)) and 248.4 nm (C i (2s 2
Paper: 2018MNRAS.479.4535S (fulltext)
the spectrum, particularly below 4400 Å; in the data of WR102, a broad peak in the degree of polarization (∼1 per cent deviation) and polarization angle (∼8° deviation) is seen between 7100 and 7600 Å. The latter feature cannot be a line effect as it is located in a region of the spectrum that is devoid of strong lines, and although its origin remains unknown (the details of our investigations are given in Section 3.2 ), we are confident it is of spurious nature. Given these considerations, the analysis presented in the rest of this study was performed without the spectral region below 4400 Å in WR93b, and the range 7100–7600 Å in WR102. Figure 1. Spectropolarimetric data of WR93b (left) and WR102 (right). The panels, from top to bottom, respectively, the degree of polarization (red) and best Serkowski law fit (black); the residuals after Serkowski fit removal (orange); the polarization angle (blue); Stokes I (grey) and line identification. The grey areas represent the discrepant regions of the spectrum described in Section 3.1 . Excluding these wavelength ranges, the polarization in the direction of WR93b and WR102 rises up to ∼4.5 per cent and ∼10 per cent, respectively. Their polarization angles have distinct behaviours: WR93b exhibits a constant polarization angle of θ = 28.2°, whereas the polarization angle of WR102 shows a downward trend that can be fit with a line of the form θ = −1.08 × 10 −3 λ + 18.4 deg. A non-zero Δθ/Δλ can be explained either by the superposition of intrinsic and interstellar polarization (Coyne 1974 ) or by the presence in the line of sight of dust clouds with different particle sizes and alignments (Coyne Gehrels 1966 ). The merit of these interpretations is discussed in Section 3.5 . 3.2 7300 Å feature in WR102 In Section 3.1 , we highlighted a prominent feature in WR102 spanning the wavelength range 7100–7600 Å. This peak was labelled as spurious and disregarded in our analysis. Here, we detail the steps taken in investigating the nature of this feature. First, we explored whether the spike in polarization is intrinsic to the data. The fact that the feature is not associated with any strong line (as would be expected for a line effect present in the WR star) is, however, inconsistent with this idea. Additionally, plotting the polarization, calculated for each of the 12 sets of observations at four half-wave retarder plate angles (see Fig. 2 ), reveals that the shape and orientation (peak or trough) are highly variable from set to set, and no one set is consistent with another. To formally check whether the data are spurious, we look at the difference in instrumental polarization corrections (Δε, see Maund 2008 ) in this region of the spectrum. This property is expected to be 0 for polarization signals ≤20 per cent, and deviations indicate that the polarization does not reflect a real signal (Maund 2008 ). In Fig. 2 , it is clearly visible that Δε is consistent with 0 across the spectrum but deviates
Paper: 2017MNRAS.471..740O (fulltext)
photometry listed in Table 1 alone, the range of quoted spectral types translates to a reddening of E ( B V ) 0.09mag. Combining this estimate along with the two previous independent reddening estimates from Section 2 , we adopt a mean reddening estimate of E ( B V ) 0.070.02 and V band extinction of A V 0.240.07mag. Adopting the mean distance to the Ori OB1a from Hernndez etal. ( 2005 ) as representative for PDS110, we can now calculate stellar parameters like absolute magnitude ( M V = 2.540.11), apparent bolometric magnitude ( m bol = 10.140.08), absolute bolometric magnitude ( M bol = 2.520.11), luminosity (log(L/L ) = 0.890.05 dex) and radius ( R = 2.230.18R ). Interpolating between the pre-MS isochrones from Siess, Dufour Forestini ( 2000 ), the stellar mass is 1.6M and its age of 11Myr, consistent with the rest of Ori OB1a. 4.2 SED disc model To model the SED of PDS 110, we used the self-consistent irradiated, accretion disc models of DAlessio etal. ( 2006 ) to create a model grid using the stellar parameters of PDS 110 in Table 2 . We adopted a dust sublimation temperature of 1400K to set the inner radius of the disc. We included outer disc radii of 50, 150 and 300au, viscosity parameters () of 0.01, 0.001 and 0.0001 and dust settling parameters (; i.e. the dust-to-gas mass ratio in the upper disc layers relative to the standard dust-to-gas mass ratio) of 1.0, 0.5, 0.1, 0.01, 0.001 and 0.0001. The minimum grain size in the disc atmosphere was held fixed at 0.005 m while we varied the maximum grain size between 0.25, 1.0 and 2.0m to reproduce the 10 m silicate emission feature. The inclination angle was fixed at 60. Table 2. Determined Stellar Parameters for PDS 110. Based on the -squared values, the best-fitting model has a max = 0.25 m , = 0.5, = 0.01 and an outer radius of 300au. Uncertainties are beyond the scope of this analysis. This disc model has a mass of 0.006 using equation (38) in DAlessio etal. ( 1999 ). While there are no millimeter data available to provide spatial constraints, a large outer radius of 300au is consistent with the significant MIR and FIR excess of this object given that the strength of the disc emission is related to the disc mass, which, in turn, depends on radius (DAlessio etal. 1999 ). We also note that = 0.5 corresponds to a relatively flared disc. Here, we measure a disc height at 2 of 0.3au. 4.3 Photometry Some out-of-eclipse variability is seen with peak-to-peak amplitudes on the order of 3percent. From the measured v sin( i ) (64kms 1 ), we would naively estimate a P rot of 1.7d. However, searches with lomb-scargle periodograms (Press Rybicki 1989 ) on both the entire data set and shorter segments do not detect any coherent period of variation attributable to rotation, with the signals dominated by day- and month-aliases from the ground-based surveys. This suggests that variations are stochastic or quasi-periodic, as has been seen for T-Tauri stars before (Rucinski etal. 2008 ; Siwak etal. 2011 ).
Paper: 2021A&A...648A..90K (fulltext)
model component, given as with the electron and hydrogen densities n e and n H , respectively, and the filling factor f . We used the ratio of n e ≈ 1.21 n H for the LMC abundance of 0.5 solar ( Sasaki et al. 2011 ). With this, we can estimate the hydrogen density from the normalization with Assuming an ideal gas, the pressure can then be estimated from the temperature of the plasma, which we obtained from the spectral fits: with the X-ray temperature T x and the Boltzmann constant k B . And with the pressure P we can estimate the thermal energy E stored in the plasma with Additionally, the mass of the plasma can be calculated using with the m H and μ being the hydrogen mass and molecular weight of the ionized gas, respectively. We used the spectral fit results of the tessellates for the calculations, as those results cover most of the X-ray spur and the surroundings. The derived values are given in Table 4 . We derived the results for each region individually and then took the average of the resulting map over regions which correspond to the large scale structures in the vicinity. The upper and lower limits were calculated by taking the average of the upper and lower limit map of the respective derived parameter. The volume V was estimated by calculating the area of each tessellate polygon region using the Shapely 7 python package. Multiplied with the depth of the X-ray emitting plasma, we obtain the volume of each region. We varied the depth for the volumes, depending on the region the tessellates are located in, to account for the complex geometry of the ISM. We took the values given by Points et al. (2001) and additionally, we estimated the depth of the 30 Dor region with z = 300 pc based on the extent of the structure. The assumed depth is by far the largest factor of uncertainty in the calculations, however, the order of magnitude should be approximately correct. The thermal energy E and mass M of the plasma was also normalized to the volume in cm 3 . Otherwise we would obtain the total numbers for each tessellate region, which would not be comparable between different regions. We give the results without assuming any specific filling factor, as this parameter is largely unknown to us. Based on the true value of the factor the results could change slightly. If we compare, for example, a filling factor of f = 1 versus f = 0.3, the pressure, hydrogen density and mass would increase by a factor ~ 3 while the thermal energy would decrease by that factor for f = 0.3. To gain further insight into the geometry of the hot plasma, we compared the absorption column from the spectral fits with the column density N H of the H I data. This ratio is a measure how deeply the plasma is embedded inside the X-ray absorbing material ( Maggi et al. 2016 ). A value smaller or close to unity suggests that the plasma is located more to the front and consequently a value larger then unity that the plasma is located to the back. We obtain the column density N H