adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017A&A...607A.125N (fulltext)
one typically utilizes higher resolution grid elements in regions of higher stellar volume emissivity and dust density. Although reasonable to expect a more rapid variation of the radiation field in those regions, there is no guarantee that the spatial resolution is adequate everywhere on the grid. In the absence of iterative procedures to increase the spatial resolution during the RT calculation, the effect of the grid discretization on the numerical accuracy can only be checked by repeating the calculations at progressively higher spatial resolutions. Similarly, the finite number of angular directions of the rays that are cast from each radiation source, as well as the discretization of the scattering source function, also affect the calculation accuracy. Apart from these factors, common to all 3D dust RT codes although in different forms 6 , in DART-Ray the numerical accuracy is also affected by the estimate of the extent of the source influence volumes. This is because DART-Ray calculates the RFED contributions from each source only within this volume surrounding the source itself, thus neglecting the contributions outside it. Since this is a core characteristic of DART-Ray, we are interested in quantifying the accuracy error due to the cut off of the rays when they reach the boundary of the estimated source influence volume. We note that this accuracy error is systematic, since it will always produce RFEDs which are underestimated compared to the correct value. The ray cut off occurs when the RFED contribution carried by a ray satisfy the criterion 1 . So, once a lower limit to the RFED distribution has been estimated, the input-defined parameter is the key factor affecting the numerical accuracy of the calculation. In N14, we stated that should be low enough to preserve energy balance, in the sense that, at the end of the calculation, the total radiation luminosity that has been neglected because of the ray cut-off should be only a small fraction of the total luminosity of the model. In this case, the effect of cutting the rays is minimal because almost all the radiation luminosity has been followed in its propagation throughout the model. However, since this energy balance can only be checked at the end of the RT run, potentially several attempts had to be made to find the appropriate value for . Instead, in the following, we show how the value of the parameter can be set before an RT run such to guarantee the desired level of accuracy. We have been able to find a relation between and the accuracy of the derived RFED distribution by making a minor change in the definition of in formula 1 , compared to NA14. This is now defined as: where is the average specific intensity of the ray within the ray-cell intersection path, is the area of the emitting cell originating the ray, is the solid angle subtended by the intersected cell, is the linear size of the intersected cell, its volume and the light speed. This formula differs only slightly
Paper: 2017A&A...599A..14J (fulltext)
that the former can be used as a reliable proxy for the latter. The random theoretical 1-NNS distribution derived for an infinite media (see Appendix B for the full development) is given by: where is the mean intensity of the random process (the average surface density of stars) and is the 1-NNS. We want to compare this theoretical distribution to the 1-NNS histogram obtained from 10 000 random Monte Carlo samplings within the finite and irregularly-shaped window (see Fig. 3 ). Their respective moments are similar (see Table 2 ), with theoretical moments computed from the following expressions: where , , , , and are the mean, median, variance, standard deviation, and skewness factor (see Appendix B for their derivation). The moments from random Monte Carlo samplings were obtained from: where is the ordered sequence of the 1-NNS from the smallest to the highest value, the standard errors of the moments and the standard error of the median are taken respectively from Ahn Fessler (2003) and Kendall Stuart (1977) . Fig. 3 Probability distribution function (PDF) of the 1-NNS distributions observed in Taurus (solid blue histogram: within the limited window , that is, the three main filaments; dashed blue histogram: the entire Taurus region) and predicted for a random distribution (black histogram: for a Monte Carlo sampling within the window ; red curve: unlimited theoretical random 1-NNS distribution, see Eq. ( 3 )). Both random populations are drawn using the same mean surface density . Increasing the mean density by a factor of two leads to a shift towards the left whose amplitude is represented by the green arrow. The Monte Carlo sampling in a finite window and the unlimited theoretical random 1-NNS do not significantly differ. The 1-NNS distributions computed either in the whole Taurus or within do not significantly differ either. However, the Taurus and random 1-NNS distributions are statistically highly inconsistent, with a -value of 10 for the KS test. The spatial distribution in Taurus is clearly far from being random. We thus conclude that finite size and edge window effects do not significantly alter the distribution of 1-NNS from that obtained in an infinite medium at the level of our observational uncertainties. To prove this statement quantitatively, we use a bootstrapping technique, comparing 10 000 realizations of a) a sample of 1-NNS obtained from standard rejection sampling technique using analytical random 1-NNS distribution (Eq. ( 3 )) to b) the 1-NNS associated to random Monte Carlo samplings of 252 stars enclosed within . The two-sample Kolmogorov-Smirnov (KS) and Anderson-Darling (AD) statistical tests indicate that the two 1-NNS distributions cannot be statistically distinguished with a mean -value and standard deviation respectively of for the KS test and for the AD test. We have therefore shown that the random theoretical 1-NNS probability density function (Eq. ( 3 )) may be used as a reliable proxy to describe a 1-NNS
Paper: 2018ApJ...855...95H (fulltext)
in the center of the sheet, and decays toward the rear of the current layer. This transpires from the shape and separation of the red equipotential contours in Figure 1 . Beyond the center, there is more energy in the stress-release motion than in the residual shear component. This means that a Poynting flux, , builds up behind the leading edge of the current front that subsequently converges, thus feeding the electromagnetic energy into the particles by maintaining . Associated with the first increasing and then decreasing stress-release motion is an inertial current, j in , pointing away from the sheet's center line. This component leads to concentrations of the current along the leading and trailing edges of the progressing sheet. Such refinements have not been incorporated in the cartoon of Figure 1 . Underlying this figure is the assumption that the current front propagates in a stationary fashion. In this case, the perpendicular potential drop equals and can be approximated byTwo aspects differ from the mechanical analogy of the fracture process: the magnetic field lines become newly connected, and part of the converted energy remains inside the fracture zone as thermal energy. However, a fraction, say about 10% of the electrons, is running through much of the parallel potential drop without losing its energy to the wave field and escapes along B , carrying perhaps up to 50% of the converted energy out of the sheet. These are the runaway electrons, candidates for producing the hard X-rays in solar flares. Figure 1. Oblique current sheet propagating with normal velocity, v n . Red lines represent equipotential contours for a quasi-stationary situation. The transverse electric field is related to the ongoing reduction of the shear field. The associated inertial current, j in , leads to current concentrations along the leading and trailing edges (not shown). The dashed magnetic field lines indicate the presence of a shear component, B ⊥ , that decays upon transit through the current sheet. An interesting problem is that of the reverse current. The question whether it is cospatial with the flux of runaway electrons has occupied many authors (Holman 1985 ). Emslie Hénoux ( 1995 ), for instance, argue against cospatial return currents. Figure 2 shows that the obliqueness of the current sheet facilitates maintaining charge neutrality. Charge neutrality requires that the flux of high-energy and low-density runaway electrons is balanced by counter-flowing low-energy background electrons. This begins outside the current sheet and thus outside the strong electric field, which would have a repulsive action on the return current, and continues all along the respective magnetic field lines. Because of the transverse propagation of the emitting current sheet, this lasts for a very limited time, τ CS , to be defined in Section 4.3 . It must be noted that the individual fluxes of runaway and reverse current electrons are by orders of magnitude higher
Paper: 2017MNRAS.465.1241W (fulltext)
left panel but for galaxies split into redshift-independent mass bins. Bottom left panel: evolution of the logarithmic derivative of the half-mass radius r 0.5 with respect to the mass ratio m m / M s ( m is the stellar mass gained between two consecutive outputs) for non-mergers or m the mass ratio of the merger, for all galaxies with M s 510 9 M and all time outputs between z 5.2 and 1.2. Filled blue symbols indicate resolved mergers, red and light green ones represent non-merger diffuse mass acquisition, with unresolved mergers (in red) or not (in green). Whilst the evolution is linear in each case, the dependence of radius growth on the mass ratio is found to be much steeper for mergers. Bottom right panel: same plot as the top left panel, except that we have split the merger sample according to different pre-merger morphologies: discs (light blue) and spheroids (red). The steepness of the radius versus m relation appears mainly caused by minor merger disruption of the discs. The top panels show the evolution of the average half-mass radius r 0.5 as a function of redshift when we split our galaxy sample in bins of final mass (i.e. galaxy masses at redshift z 1.2; top left panel) and in bins of constant stellar mass (i.e. independent of redshift; bottom right panel). These results are consistent with the overall evolution of the sizemass relationship from observations such as Huertas-Company etal. ( 2013 ) (though our simulated galaxies are a factor of 2 larger): galaxies of a given stellar mass display much larger radii at z 1.2 than their counterparts of similar stellar mass at z 5.2; most of the growth taking place between z 3 and 1.2. More specifically, at z 1.2, galaxies with M s 10 10.5 M display an average half-mass radius two to three times bigger than their counterparts at z 3 (see bottom right panel), while one can see on the bottom left panel that galaxies reaching M s 10 10.5 M at z 1.2 have also seen their half-mass radius grow by a factor of 2 to 3 since z 3 and by a factor of 4 since z 5.2. To better investigate the role of different stellar acquisition processes in growing the size of galaxies, we compute the logarithmic derivative of the half-mass radius r 0.5 with respect to M s as a function of the merger mass ratio m for post-merger structures (bottom left panel, blue curve) and the mass increase ratio m m / M s log 10 M s , where m is the stellar mass accreted between two consecutive outputs, for galaxies only subjected to diffuse stellar accretion (green curve for pure in situ formation and red curve when including possibly unresolved mergers). This is computed for all galaxies and all time outputs between z 5.2 and 1.2 with M s 510 9 M . Note that M s or M 0.5 are equivalent for the purpose of this comparison so we use them interchangeably. The result is shown in the top left panel of Fig. 8 . Note the similarity between results for the pure star formation sample (in green) and the full sample of galaxies
Paper: 2020ApJ...901...32J (fulltext)
The gray scale is linear from zero to erg cm −2 s −1 sr −1 Å −1 , reversed (black is the maximum intensity) to reveal fainter emission between bright regions. The right panels show the average spectra (black) superposed with the average quiet Sun from the IRIS data studied here in red. Although the R-MHD calculation is for a network region, the region is dominated in area by non-network emission; thus, we compare with the quiet IRIS data. Note that the computed line k 2 peak separations are substantially smaller than observed. Significantly, in comparison with plage observations, these k 3 and K 3 components are far deeper in the computations. This suggests that the source function is on average larger where this feature is formed than is captured in the calculations. In Figure 9 we show spatial autocorrelation data computed from the 3D and 1D intensities of the k line, together with spatially averaged intensity profiles. Solid lines show observations, while dashed lines show computations. The autocorrelation characteristic lengths show three kinds of behavior depending on the wavelengths relative to line center. In the line wings, the images have the smallest spatial scales, with FWHM values of about 750 and 610 km. In 3D, these scales steadily increase toward the line cores, peaking near the observed peaks k 2 V and k 2 R . (In 1D these lengths decrease toward the line center before showing a small increase at k 2 V wavelengths.) Within the line core, the widths computed in 3D drop dramatically to below 700 km. Figure 9. Average profiles (top panel) are shown with autocorrelation full widths for the IRIS network and quiet regions (red and gray, respectively) in the bottom panel. The same data for the 3D and 1D calculations of Sukhorukov Leenaarts ( 2017 ), are also shown as dashed lines. The comparison of 3D and 1D images in Figure 8 is instructive in ways complementary to the interesting points made by Bjørgen et al. ( 2018 ). Figure 9 shows clearly that 3D transfer effects are important within ±1 Å of line center: the FWHM length scales are larger on average by a factor of two for the k line (15%–20% for the K line) in 3D than in 1D. The 3D features all appear “fuzzier” to the eye than their 1D counterparts, on scales of a few hundred kilometers in Figure 8 . We will speculate further on what is missing in these calculations in Section 5 . The second and fourth rows of Figure 8 show how the calculated model might be observed through filter instruments, with FWHM values of 0.1, 1, and 3 Å. Wider FWHM instruments have been used in solar physics for many years to record the Ca ii lines, and other, narrower filter widths have been developed. 11 11 See, e.g., https://www.su.se/isf/ . These panels suggest that 3D smearing effects are biggest at wavelengths within the emission cores. The differences are probably lower limits, given that the source functions for strongly scattering lines are smeared by the use of the short characteristics, which
Paper: 2021A&A...646A..30A (fulltext)
WD resulting from the merger of two equal-mass WDs by assuming the extreme situation of complete mixing between the two WDs and a CO core for the merged remnant. To this end, we adopted the internal distribution of carbon, oxygen, and He of the 0.58 M ⊙ WD model computed in Miller Bertolami (2016) from the evolutionary history of its progenitor star. Assuming complete mixing between both WDs, the mass fraction of He, carbon and oxygen throughout the interior of our UMCO WD model results X He = 0.017, X C = 0.363 and X O = 0.62. We mention that in this study we are interested in the advanced evolutionary stages of the WD and not the evolutionary stages shortly after the merger event, which cannot be adequately followed by our stellar evolution code. Finally, in this work we did not consider the possibility that WD mergers lead to ultra-massive ONe-core WDs, as predicted in Schwab (2021) . The reason is that we do not expect such ONe WDs to present marked differences in their pulsational properties as compared with the ONe-core models resulting from the single evolution that is explored in the present paper. 4. Evolutionary properties In this section, we compare the evolutionary properties of our UMCO WD models resulting from the two single-evolution scenarios and from the binary merger we described previously, with those predicted by the ONe-core WD models resulting from off-centre carbon burning during the single evolution of the progenitor star ( Siess 2010 ). To isolate the impact of the internal composition predicted by the possible scenarios, we adopted the same stellar mass of 1.159 M ⊙ for all of our WD sequences 4 . In addition, we assumed the same H content for all of our sequences except the merger case. In particular, it was taken as nearly the maximum allowed by evolutionary considerations for such stellar mass, log( M H / M ⋆ )≈ − 6. This limit is imposed by the occurrence of unstable nuclear burning on the cooling track for the WD mass considered. Finally, the ONe-core WD-evolutionary and -pulsational models were taken from Camisassa et al. (2019) and De Gerónimo et al. (2019) . Such models were computed with the same codes we used to compute the evolution and pulsations of our UMCO WD models (see Sect. 2 for details). The evolutions of all of our WD sequences were followed from very high luminosities, through the domain of the pulsating ZZ Ceti stars, down to very low surface luminosities. In Fig. 5 , we display the internal chemical profile of our UMCO WD models (see Sect. 3 for details), together with the chemical profile of the ONe-core WD model studied in Camisassa et al. (2019) , top panel. Specifically, the second and third panels illustrate, respectively, the chemical profiles resulting from reducing the mass-loss rates of an initially 7.8 M ⊙ progenitor star, and those from considering core rotation ( f = 0.1) in the AGB phase of an initially 7.6 M ⊙ progenitor star. The bottom panel shows the chemical profile we
Paper: 2020MNRAS.491.3884P (fulltext)
counts and fluxes from the eregionanalyse task. They have less than 35 counts in the full band; for three of them, we even only get upper limits. The EPIC net source counts and fluxes in the full (0.2–10 keV) energy range (0.2–0.5, 0.5–2.0, 2.0–5.0, and 5.0–10.0 keV energy ranges obtained for these quasars) are summarized in Table 3 . The full-band XMM 100 arcsec EPIC mosaics of these six quasars are shown in Fig. 1 (rows 2 and 3). Table 3. X-ray net counts and fluxes for the new z > 6.4 X-ray quasars. Note . The upper limits quoted in this table correspond to the 3σ upper limits and are used only if the net counts or fluxes are smaller than their 3σ error. But only the quasars with a given value in the ‘Det.’ column have an X-ray detection. For the two X-ray detected quasars in this work, the source counts and fluxes presented in Tables 2 and 3 are from the edetect_chain task. 4 DISCUSSION For the full sample of nine quasars, we derived a set of optical and X-ray properties (see Table 4 ): The absolute magnitude at the rest-frame wavelength of λ = 1450 Å ( M 1450 ) was computed by extrapolating the J -band magnitude to the monochromatic magnitude at ( m 1450 ) assuming a power-law fit of the continuum in the UV–optical with α ν = −0.3 (Selsing et al. 2016 ). The monochromatic flux at the rest-frame wavelength of () was derived from m 1450 assuming also a power-law slope of α ν = −0.3. The X-ray luminosity in the rest-frame 2–10 keV band was computed from the count rate obtained by the eregionanalyse task on the 2–10 keV image (as done in Section 2 for the other energy ranges) and then K -correction was applied assuming a photon index Γ = 1.9. For the two detected quasars VDES J0224−4711 and PSO J159.2257−02.5438, the flux was obtained directly from their X-ray spectrum. Upper limits are used if the luminosity is smaller than its 3σ error (which is the case for all the non-detected sources) and are at the 3σ level. The hardness ratio is defined as HR = ( H − S )/( H + S ), where S and H are the number of counts in the soft (0.5–2.0 keV) and hard (2.0–5.0 keV) bands, respectively. The optical-to-X-ray slope (α ox ) is defined as where is the monochromatic flux at the rest-frame wavelength of as defined above, and the rest-frame monochromatic flux at 2 keV () was inferred from the flux in the 0.2–10 keV range assuming a photon index Γ = 1.9. Upper limits for α ox are used if the flux at 2 keV is smaller than its 3σ error. Table 4. Summary of X-ray and optical properties of the DES and new z > 6.4 X-ray quasars. Note . a Reminder: VDES J0020−3653 and PSO J036+03 have a measurement of α ox with errors because their 2 keV flux (from the 0.2–10 keV flux) is at a 3σ significance, but they are not significantly (>4σ) detected as VDES J0224−4711 and PSO J159−02. We used the compilation of X-ray observations of quasars at z > 5.5 by Nanni et al. ( 2017 ) plus the new analysis by Nanni et al. ( 2018 ) for SDSS J1030+0524 ( z = 6.28) and the observation
Paper: 2018ApJ...859...50F (fulltext)
wings. If we compare epochs I and II, we can see that the magnified blue wing fades while the red wing enhances. This supports that the separation between the approaching and receding parts of the microlensed region of the BLR is of about a few light-days. In several cases, mainly affecting the shelf-like feature at ∼ λ 1610 and several blends of Fe ii and Fe iii , the impact of microlensing and microlensing variability (see Tables 2 – 5 ) is mag, comparable to the typical microlensing magnification amplitudes observed in the X-ray continuum. It is important to notice that the spectral features mainly affected by intrinsic variability are also the C iv wings, some blends of Fe ii and Fe iii , and the shelf-like feature at ∼ λ 1610. In contrast with microlensing, we have not observed marked asymmetries induced by intrinsic variability in the line profiles. This result supports (within the statistical significance of our relatively limited sample) the hypothesis that the small region sensitive to microlensing is not systematically affected by extinction, beaming, or any other mechanism that may selectively enhance one part of it. Consequently, the asymmetric enhancements observed, in either the blue or red parts of microlensed line profiles, likely originate from an anisotropic distribution of microlensing magnification in the source plane. 3. Discussion 3.1. Microlensing and Kinematics The main result of the previous sections is that the cores of C iv , C iii ], and Mg ii and the wings of the last two lines are only weakly affected by microlensing. However, the wings of C iv can be strongly affected. In other high-ionization emission-line features, such as the Fe iii λλ 2039–2113 blend and the shelf-like feature at ∼ λ 1610, the whole feature may be globally affected by high-magnification microlensing. To discuss these results with more detailed kinematic information than the separation between core and wings, in Figure 6 we have overlapped the averaged line profiles corresponding to C iv , C iii ], Mg ii , and Fe iii λλ 2039–2113. The wings of Mg ii correspond to relatively low velocities as compared with other lines. This is consistent with the weak impact of microlensing on this line. Regarding C iii ] and C iv , the red parts of these lines match very well, except at the lowest intensity level, where the shelf-like feature at ∼ λ 1610 is present in C iv (the blue side of C iii ] is blended with Al iii , Si iii , and Fe ii , and no reasonable comparison can be made). In spite of this remarkable kinematic coincidence, the C iv line can be strongly affected by microlensing, whereas C iii ] seems to be rather insensitive to this effect (see, e.g., the cases of SDSS J1004+4112 and SDSS J1339+1310). In addition, they have a different degree of ionization (C iv is of high ionization and C iii ] of low ionization). These results suggest that both lines are mostly generated in the same region, but that there is also a contribution, exclusive to the