adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019ApJ...883..176S (fulltext)
More specifically, we can summarize our findings as follows: 1. Because the drag force experienced by small dust grains is very strong, a specific implicit numerical scheme was implemented in the code RoSSBi to accurately follow the dynamics of the disk. This method is very accurate for any value of the Stokes number of the pressureless dust fluid, and we use it to study the evolution a vortex model with dust characterized by Stokes numbers in the range . 2. Dust is systematically accumulated inside the vortex for every value of the Stokes number. The exponential growth rate of the dust density at the vortex center follows the analytical model we developed in our previous study. The timescale depends on the grain size, but the density enhancement reached at the end, when the gas is perturbed by the drag force, is similar regardless of the Stokes number, and produces dust-to-gas ratios of order unity. We highlight the existence of a generic capture process, characterized by a timescale that scales with the Stokes number, which allows us to compare disks with different dust sizes at an equivalent state of evolution, but corresponding to intrinsically different physical timescales. 3. The vortex instability is triggered at the end of the capture, regardless of the grain size. The growth rates of the excited modes also depend weakly on the Stokes number. This effect is highly correlated with the similarity of the dust capture, and the concept of equivalent states of evolution, producing almost the same perturbation of the gas vorticity profile. 4. A new phase of dusty vortex evolution is discovered for Stokes numbers on the order of 10 −3 and below, where a succession of vortex instabilities followed by dust capture events occurs. This period lasts several hundred of disk orbits and produces strong mixing and a fast reorganization of the dust during the relaxation of the vortex. The dust-to-gas ratio increases progressively to high values, up to 8 at the vortex center. The very dense core that forms and survives even after eventual vortex dissipation could be a preferential place for planetesimal formation. 5. When the timescale of the drag effect is too long compared to the resistance and relaxation of the vortex, the formation of a dust ring becomes unlikely. In our case, the runs with St = 10 −3 were still in the unstable phase by the end of the simulations. Even when it forms, for St 10 −2 , the dust ring produced by well-coupled grains has fewer eddies than with larger Stokes numbers. Even if the dust-to-gas ratios are comparable, they suffer diffusion, and their role in planetesimal formation is less promising than the unstable phase. There are a number of caveats to our model that need to be pointed out. First, the fact that the simulations are still only two-dimensional, which could have a relevance both for the development of two-fluid turbulence during and after the vortex instability phase (see the discussion in Surville et al. 2016
Paper: 2019MNRAS.488.3716C (acknowledgments)
ACKNOWLEDGEMENTS We thank the anonymous referee for the detailed comments that helped to improve this manuscript. We thank Patrick Diamond, Ellen Zweibel, Michael Norman, and Todd Thompson for insightful suggestions and advice, Eve Ostriker for pointing out a typo, and Bili Dong for his help with yt . We would like to thank the Simons Foundation and the participants of the Galactic Superwinds symposia for stimulating discussions. TKC and DK were supported by NSF grant AST-1715101 and the Cottrell Scholar Award from the Research Corporation for Science Advancement. Support for PFH was provided by an Alfred P. Sloan Research Fellowship, NSF Collaborative Research Grant #1715847, and CAREER grant #1455342, and NASA grants NNX15AT06G, JPL 1589742, 17-ATP17-0214. EQ was supported in part by a Simons Investigator Award from the Simons Foundation and by NSF grant AST-1715070. The Flatiron Institute is supported by the Simons Foundation. CAFG was supported by NSF through grants AST-1517491, AST-1715216, and CAREER award AST-1652522, by NASA through grants NNX15AB22G and 17-ATP17-0067, and by a Cottrell Scholar Award from the Research Corporation for Science Advancement. The simulation presented here used computational resources granted by the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant no. OCI-1053575, specifically allocation TG-AST120025. Numerical calculations were also run on the Caltech compute cluster ‘Wheeler’, allocations from XSEDE TG-AST130039 and PRAC NSF.1713353 supported by the NSF, and NASA HEC SMD-16-7592. This work uses data hosted by the Flatiron Institute’s FIRE data hub. This work also made use of yt (Turk et al. 2011 ), matplotlib (Hunter 2007 ), numpy (van der Walt, Colbert Varoquaux 2011 ), scipy (Jones et al. 2001 ), and NASA’s Astrophysics Data System.
Paper: 2018MNRAS.478.5671W (fulltext)
aberrations and we anticipate good astrometry results with the mean PSFs of each cluster. However, PSFs from stars with a magnitude of 11 have small and almost randomly distributed clusters. Although the mean PSFs of two different clusters are almost the same in Fig. 12(f) , this might be induced by the average of many different PSFs. To fully reflect the classification performance, we perform PSF-fitting astrometry in these data, using the Fourier-based registration method (Stone et al. 2001 ) with the mean PSFs obtained from our method. This method assumes that the PSFs in the same cluster are morphologically similar and only have a different subpixel shift. We can obtain this subpixel shift by maximizing the correlation coefficients between the stamp images of stars and the mean PSFs in an oversampled Fourier domain. The subpixel shift and the position of the brightest pixels in the images of the celestial objects can then be used to calculate the star's position in the image. For comparison, we also use the Gaussian function as a PSF template and we calculate the positions of stars with the least-squares fitting method (Stone 1989 ). The Gaussian function has a FWHM of 2 pixels. The positions we calculated with the above two methods are compared with the Tycho-2 catalogue (Høg et al. 2000 ) as follows. We obtain the star positions (α i , ξ i ) from the Tycho-2 catalogue as the theoretical positions of these stars and we construct the association between that position and the position of these stars in the image using as proposed in Sun, Zhao Zhang ( 2013 ). Here, (α 0 , δ 0 ) is the corresponding equatorial position of the frame centre. With this association, the observed position (α c , ξ c ) of these stars can be obtained from the pixel position ( x c , y c ), and the residual position errors of each star (α c − α i , δ c − δ i ) are obtained. The astrometry error of these two methods from the Tycho-2 catalogue is shown in Fig. 13 . The systematic errors of Tycho-2 are nearly 0.01 arcsec so this does not have an effect on the calculation. Compared with the Gaussian fitting method, we can see that stars with a magnitude of 10 have better astrometric accuracy with the mean PSF and stars with a magnitude of 11 have almost the same astrometric accuracy. Figure 13. Astrometric residuals of stars with magnitudes of 10 and 11 of telescope B. The number of stars with the magnitude of 10 is 517 and the number with the magnitude of 11 is 737. Top: the residuals of right ascension. Bottom: the residuals of declination. Crosses indicate Gaussian PSF-fitting centroid and circles indicate mean PSF fitting. It is worth noting that PSF classification is a clustering problem and for stars with the magnitude of 11, we do not know why the PSFs are not clustered distinctly. There are two possibilities: (i) these PSFs are nearly the same essentially, so applying the average of these PSFs for subsequent processing is a sound method (Sun Jia 2017 ); (ii) the
Paper: 2018MNRAS.481.5651A (acknowledgments)
ACKNOWLEDGEMENTS VW’s research is funded by an ERC Starting Grant (3DICE, grant agreement 336474). The authors acknowledge the CNRS program ’Physique et Chimie du Milieu Interstellaire’ (PCMI) co-funded by the Centre National d’Etudes Spatiales (CNES). LM also acknowledges the support from the NASA postdoctoral program. A portion of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration.
Paper: 2019ApJ...882...85O (fulltext)
the EUVI-A stack plot does not show any clear upward-propagating structure. The result that it was unable to be picked up in the field of view captured by EUVI-A, combined with the observation of the slow-rise phase in the COR1 field of view, suggests that the eruption was initiated from higher altitudes. The EUVI stack plot does, however, show a large brightening around the time of the rapid acceleration phase beginning, likely to be post-eruption loops associated with the stealth CME. We then fitted exponential and quadratic curves to the CME position in the COR1 field of view. The curves can give indications of what mechanisms are driving the eruption (Schrijver et al. 2008 ; D’Huys et al. 2014 ). Numerical simulations matched with observations have demonstrated that an exponential rise profile occurs when an instability is dominating the eruption, such as the torus or kink instability, while a quadratic rise profile occurs during a breakout model scenario. For this event the exponential curve produced the best fit, suggesting an instability dominating the eruption. Both the torus and kink instability require a flux rope prior to eruption, and given that the concave-up structure could be tracked for a period of hours during the slow-rise phase, it is likely that in addition to the structure being initiated at a high altitude, the flux rope was also formed at a high altitude in the corona. Figure 2. Left: EUVI/COR1 image indicating the slice used to create the stack plot (solid line). Right: EUVI-B 195 Å (lower) and COR1-B (upper) stack plots. The height–time profile of the stealth CME observed on 2009 October 27 can be determined from the COR1-B data, which show the underside (concave-up structure) of the stealth CME. Exponential (red line) and quadratic (blue line) fits have been applied to the COR1-B data. The stealth CME is not observed in the COR1-A images until ∼15:30 UT on 2009 October 27, several hours after the eruption onset time as determined by Kilpua et al. ( 2014 ). The CME is also very faint and barely visible in the images, making it difficult to pinpoint the exact time that it enters the field of view. It also emerges from a bright streamer that acts to mask the CME structure. However, the stealth CME is seen more clearly, and earlier, in COR1-B, which could suggest that the eruption is closer to the limb of STEREO-B than STEREO-A . The estimated source region from triangulation techniques is of similar distance to the nearest limbs in both spacecraft, while the decayed AR is directly on the limb in STEREO-A and on disk in STEREO-B , and AR 11029 is directly on the limb of STEREO-B and on disk in STEREO-A . Using COR1 and COR2 from the twin STEREO spacecraft, the wire frame of the GCS model (representing the flux rope) was fitted to the concave-up cavity structure in the coronagraph observations (Figure 3 ). The footpoints of the erupting structure were found to be N03E05 and N03W18 from the GCS model, illustrated by
Paper: 2016MNRAS.457..351Y (fulltext)
CO core mass M CO , and the surface mass fractions of He ( X S (He)), C ( X S (C)), and O ( X S (O)) of the stellar models. We calculate the evolution during PPI stage using the stellar evolution code and a ppm hydrodynamic code (e.g. Colella Woodward 1984 ; Umeda Nomoto 2005 ) as follows. This is because it is difficult to solve unbound surface structure during the PPI mass ejection using the stellar evolution code. We describe the calculation method using the second pulsation of the 140 M ⊙ star model as an example. Fig. 1 shows the evolutionary track of the 140 M ⊙ star model on the plane of log ρ C (the central density) and log T C (the central temperature). Two points (A) and (B) in the second pulsation are assigned. First, we calculate the stellar evolution passing through points (A) and (B) without mass-loss by the PPI using the stellar evolution code. Figure 1. The evolution of the 140 M ⊙ model on the plane of log ρ C − log T C after He core burning. The shaded region shows unstable region against electron–positron pair instability. Details for points (A) and (B) are explained in text. Next, using the ppm code, we calculate the mass ejection of the star for about 10 4 s, corresponding to the period until the mass ejection is almost completed. The initial structure is set to be the structure at the time when the star has the maximum total energy during the pulsation (point (A) in the second pulsation). During the expansion, outermost region of the star accelerates and exceeds escape velocity. Thus, the mass exceeding the escape velocity is considered as the ejected mass. Then, we restart the stellar evolution calculation from the time when the central temperature is almost minimal (point (B) in the second pulsation). We artificially reduce stellar mass with a constant mass-loss rate evaluated in the previous hydrodynamic calculation. The mass-loss continues until the lost mass is equal to the ejected mass obtained from the hydrodynamic calculation. After several pulsations, the star forms an Fe core and collapses. We stop the calculation of the stellar evolution when the central temperature reaches 10 10 K. 2.2 Stellar evolution during PPI stage First, we show the evolution of the 140 M ⊙ model after the C-burning. Fig. 2 shows the mass fraction distribution after the C-burning. The central temperature is log T C = 9.14. The main component at the centre is O, Ne, and Mg. The mass fraction of Si also increases. The central C-burning occurs radiatively. The O/C/Ne layer is in the range of ∼6–48 M ⊙ in the mass coordinate. The surface component is mainly O and C, and 19 per cent 4 He by mass fraction remains as shown in Table 1 . Figure 2. Mass fraction distribution of the 140 M ⊙ model after the C-burning at log T C = 9.14. Black, sky-blue, blue, purple, dark-green, green, and orange lines indicate the mass fractions of 4 He, 12 C, 16 O, 20 Ne, 24 Mg, intermediate-mass elements (Si-Sc), and Fe-peak elements (Ti-), respectively. The time
Paper: 2019ApJ...884...49T (acknowledgments)
We thank Arecibo for the generous amount of observing time for the completion of this project and the observatory staff for the successful completion of our observations. We also thank Richard Crutcher for his help in the analysis of our data. This work took part under the program ISM3D of the PSI2 project funded by the IDEX Paris-Saclay, ANR-11-IDEX-0003-02. This work was supported in part by the National Science Foundation under NSF grant AST 0908841. The Arecibo Observatory is operated by SRI International under a cooperative agreement with the National Science Foundation (AST-1100968), and in alliance with Ana G. Méndez-Universidad Metropolitana, and the Universities Space Research Association. Facility: Arecibo.
Paper: 2017MNRAS.469.3541P (acknowledgments)
Acknowledgments We thank the referee for their comments that helped us to clarify and improve this paper. We have used DSS scans in this paper. Based on photographic data of the National Geographic Society–Palomar Observatory Sky Survey (NGS-POSS) obtained using the Oschin Telescope on Palomar Mountain. The NGS-POSS was funded by a grant from the National Geographic Society to the California Institute of Technology. The plates were processed into the present compressed digital form with their permission. The Digitized Sky Survey was produced at the Space Telescope Science Institute under US Government grant NAG W-2166. This research has made use of the SIMBAD data base, operated at CDS, Strasbourg, France.
Paper: 2016A&A...587A..70S (fulltext)
of the A-AB distributions, a -value of for the A-B distributions, and a -value of for the AB-B distributions (from the AD-tests we obtain -values of , and for each bin, respectively). Finally, to analyse the distribution of slopes depending on the luminosity of the galaxies, we divided the sample again into three groups: luminous (L, mag), intermediate (I, mag), and faint (F, mag) galaxies. We obtained these results: The statistical tests yield a -value of % for the KS-test in the case with the largest differences, that is, when comparing luminous and intermediate galaxies. The remaining KS-tests return a -value of for the comparison between luminous and faint galaxies and a -value of when analysing the intermediate and faint distributions (a -value of , and respectively, for the AD-tests). Similar results for all these separations are found when using any scale-length normalisation for the radial distance instead of the disc effective radius, either the radius or the physical scale of the galaxy (i.e. the radial distance in kpc). 4.3. Common abundance gradient The fact that the distribution of the oxygen abundance gradients for all the galaxies in the sample is well fitted by a Gaussian function suggests the existence of a characteristic value for the slope, independent of other properties of the galaxies, as the morphological type or the luminosity. This is true when normalising the radial galactocentric distances to the disc effective radius and limiting the fitted interval between 0.5 and 2.0 . Below this range (), a nearly flat distribution or even a drop towards the centre is found for some galaxies of the sample. At larger galactocentric distances (), a flattening can be observed in the abundance gradient of most of the galaxies. It is easier to illustrate this result if we represent the radial distribution of the oxygen abundance for all the galaxies in the same figure. This is shown in the left panel of Fig. 8 . The black contours represent the density distribution of the star-forming spaxels. Although the radial gradient can be discerned through the contour plot, the wide range of abundances blurs the result. To clarify the origin of this widening, we colour-code each represented abundance according to the integrated stellar mass of the host galaxy in log scale. For clarity, only the oxygen abundance values with a contribution of at least 1% of the total number of spaxels are plotted. Adopting this scheme, it is evident that the abundances present a common radial gradient, but with an offset depending on the mass, as expected from the relation (and as discussed in Sect. 4.1 ). To remove this dependence on the mass from the map, we rescaled the oxygen abundances of each galaxy following the analytical form of the relation ( Tremonti et al. 2004 ) derived by Sánchez et al. (2013) , applying an offset according to the integrated stellar mass of the galaxy and normalising to the average value for the whole sample of 8.5. The
Paper: 2016A&A...585A...4C (fulltext)
beappropriate for an optically thin spectral line with a given formation temperature). At a temperature of 6.3 kK we already see low lying loop structures connecting magnetic field of opposite polarities. There is a multitude of these low lying, short loops but much of the plasma at that temperature is also distributed in structures that are less loop-like. At 10 kK most of the lowest lying loops have disappeared and we have fewer, more pronounced loops that reach higher. At higher temperatures we basically see the same loops, all the way up to 316 kK () when a new set of hotter, higher lying loops start to appear. These loops dominate up to about 1 MK. The maximum temperature in this simulation is 2.2 MK and this hottest plasma is located in loops that do not reach as high as the loops with temperatures up to 1 MK. The lower lying loops with temperatures below 300 kK evolve on shorter timescales than the hotter loops and give rise to the “Unresolved Fine Structure” (UFS) loops discussed in Hansteen et al. (2014) . Fig. 13 Probability density function (PDF) of the electron density as function of height at s ( upper panel ) and at s ( lower panel ). Note the logarithmic electron density scale. 3.5. Ionization balance The simulation includes the effects of non-equilibrium ionization of hydrogen, see Leenaarts et al. (2007) . Figure 13 shows the electron density as a function of height for two times, just 10 s after the non-equilibrium ionization of hydrogen was switched on at s and for the first published snapshot, at s. The non-equilibrium hydrogen ionization leads to much higher electron density in the cool pockets at 0.5–2 Mm and also higher electron densities up to 3.5 Mm height. There is not much change with time of this probability density function after s. 3.6. Velocity field Spectral lines are normally observed to be broader than what the thermal broadening of the opacity profile would give. An extra free parameter, called microturbulence, is often introduced in 1D semi-empirical models to account for this broadening. The “micro” in the name comes from the fact that this parameter is introduced as an extra broadening of the opacity profile, acting in exactly the same way as thermal broadening. This would be a physically correct description in the limit of zero length-scale for the velocity field. It is also often necessary to introduce a second free parameter to account for the observed lineshape. This is called macroturbulence and is equivalent to a Gaussian convolution of the emergent intensity profile (rather than a convolution of the opacity profile as is the case for microturbulence). Realistic 3D radiation hydrodynamic simulations of the solar photosphere give line profiles that are close to the observed profiles without the addition of extra free parameters – the non-thermal broadening comes from Doppler shifts arising from the convective flows and oscillations (e.g., Asplund et al. 2000 ). Also spectral lines formed in the