adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019ApJ...877...45M (acknowledgments)
We would like to thank the referee for comments that have helped improve the paper. B.M., A.K., E.J.A., and F.B. acknowledge support from the Collaborative Research Centre 956, subproject A1, funded by the Deutsche Forschungsgemeinschaft (DFG). We would like to thank Carsten Kramer, Santiago Navarro, David John, Albrecht Sievers, and the entire IRAM Granada staff for their support during the instrument installation and observations. C.M.C. thanks the University of Texas at Austin College of Natural Sciences and NSF grants AST-1714528 and AST-1814034 for support. D.R. acknowledges support from the National Science Foundation under grant No. AST-1614213. Based on observations carried out under project Nos. 247-11, 227-12, 242-13, 117-14, and 232-15 with the IRAM 30 m telescope. IRAM is supported by INSU/CNRS (France), MPG (Germany), and IGN (Spain). This work was also supported through NSF ATI grants 1020981 and 1106284. Facility: IRAM: 30 m.
Paper: 2018MNRAS.476.4527T (fulltext)
times of the suboperations in chi2Image (left-hand panel) and chi2Profile (right-hand panel). Each vertical bar represents the smallest time across 20 calls of chi2* for a 4096 2 image. The CPU version is run with the optimal configuration of threads for this image size. CPU to GPU (Host to Device) copy operations are coloured in tones of blue. On the GPU, chi2Profile dramatically reduces the time spent in copying with respect to chi2Image and, even accounting for the extra-time needed to create the image, it manages to be 1.5 times faster than chi2Image . The important point of Fig. 7 is that all compute-intensive operations are faster on the GPU compared to the CPU, in particular the Fourier transform and the creation of the image. But the data transfers partly reduce this advantage, either because they are not needed at all on the CPU (for example the copy of read-only data like observations) or because the PCI express bus has a much reduced bandwidth and higher latency compared to accesses to the main CPU memory. For a large image, copying to the device actually takes longer than the FFT, that is why chi2Image is nearly 20 ms, or 50 per cent, slower than chi2Profile on the GPU, and that does not even account for the time needed to create the image on the CPU, which in this example takes another 20 ms. On the CPU, chi2Profile is only about 15 per cent faster than chi2Image . 7 CONCLUSIONS In this paper we have presented GALARIO, a GPU accelerated library for analysing radio interferometer observations. Distributed under the open source GNU LGPLv3, GALARIO is actively developed at http://github.com/mtazzari/galario and can be easily installed on machines with different configurations. Unlike single dishes, which directly measure the brightness of a source over a continuous region of the sky, radio interferometers measure its Fourier transform, sampling it at discrete locations and producing a collection of complex visibilities. The computational effort required to compare a model prediction to an observational data set of complex visibilities has increased dramatically in the last years due to the improved angular resolution and data rate of modern radio interferometers, which require larger matrix sizes and involve hundreds of thousands of visibility points. The process of computing synthetic visibilities from a model brightness involves several time-consuming matrix operations such as Fourier transforms, quadrant swaps, and interpolations. These operations have to be performed once for every likelihood evaluation, and the likelihood is called thousands or even a million times in the normal workflow required to fit a model to the data. In this context, GALARIO leverages the computing power of modern GPUs to accelerate the computation of the synthetic visibilities, thus reducing the overall execution time of the likelihood. For ease of use, GALARIO offers dedicated functions that produce directly the weighted χ 2 of the model for the
Paper: 2017MNRAS.469S...2K (fulltext)
are used in the heat transfer model (the simulation domain is shown in Fig. 4 ). At the surface, which is exposed to free space, radiation interaction with the external (solar) irradiation as well as the mutual radiative interaction of the various surface elements is taken into account. For the case that water ice resides on the whole or on parts of the free surface, sublimation causes a cooling effect, which has a feedback on the temperature evolution both on the surface and in the subsurface layers. The surface boundary condition is expressed as where A is the surface albedo (which is assumed negligible on the diffuse surface: A = 0), and F is the local irradiation intensity in [Wm −2 ]. The second term on the right-hand side describes the cooling effect caused by sublimation of water ice (if it is present at a particular position) and the third term is the heat flow towards the interior. The sublimation term is the product of the latent heat of sublimation ( L s = 2.778 × 10 6 J kg −1 ) and the mass-loss term caused by ice sublimation, where α is the accommodation coefficient, assumed to be α = 1 at icy spots where free sublimation prevails. For water ice, the vapour pressure constants have the values a = 3.56 × 10 12 Pa and b = 6141.7 K (Fanale and Salvail 1984 ). For the interior bottom and side boundaries of the computational domain (Fig. 4 ), adiabatic boundary conditions (vanishing heat flux) have been used. Figure 4. Illustration of the Abydos LAMsmall geometry (computation domain). The three surface points ( Ref , Top and Wall ) are indicated together with the vertical lines starting at these positions and directed towards the interior of the comet. The ‘active spot’ where water ice is exposed to the surface in some of our model computations is indicated in blue. In addition to the boundary conditions described above an initial temperature distribution over the whole domain of interest has to be defined, which can serve as a starting point for the time-dependent calculation. To obtain a reasonable estimate for such an initial temperature in the case of Comet 67P, we have consulted old papers published by Squyres, McKay Reynolds ( 1985 ) and by McKay, Squyres Reynolds ( 1986 ). These authors have calculated typical interior temperatures of cometary nuclei, in particular for short period Jupiter family comets. For such objects, they obtained with various spin axis orientations, comet core temperatures between 115 and 129 K (see table 1 of McKay et al. 1986 ). Therefore, in the absence of more detailed information and for reasons of simplicity we have assumed for the calculations presented in this paper, a constant initial temperature of 120 K throughout the domain of interest. 4 NUMERICAL IMPLEMENTATION 4.1 Model geometries and grids For the numerical implementation, we have developed the following procedure that turned out to be realistic w.r.t. the limitations in computer memory and computation time. It uses a combination of
Paper: 2020A&A...642A..39R (fulltext)
to calculate both the planetary and stellar spectra from 10 to 10 6 Å. The planetary, stellar, and orbital parameters used for the computation irradiated model of WASP-19b are summarised in Table 1 . 3 Results and discussion A thermal spectrum of WASP-19b from Anderson et al. (2010) , Anderson et al. (2013) , Gibson et al. (2010) , Burton et al. (2012) , Bean et al. (2013) , and Lendl et al. (2013) , is shown in Fig 1 . The planet-to-star flux ratio at 4.5 μ m is higher than the one at the 3.6 μ m Spitzer /IRAC channel. Moreover, at higher wavelengths, namely at 5.8 and 8.0 μ m, the planet-to-star flux ratio is even higher than 4.5 μ m. This clearly indicates the presence of excess emission in some photometric bands over others. Table 1 Planetary, stellar, and orbital parameters used in the model are from Butler et al. (2006) , Southworth et al. (2007) , Hebb et al. (2010) , and Hellier et al. (2011) . Fig. 1 Comparison of observed thermal spectra of WASP-19b with the modelled spectra calculated using PHOENIX (solid line) at different metallicities with an average dayside heat redistribution ( f = 0.5). The corresponding temperature-pressure profile is shown in the inset image (the grey shaded region corresponds to the structure inversion). The observed thermal spectrum of WASP-19b presented in this paper is adopted from Anderson et al. (2010 , 2013 ) (filled square and filled circles), Gibson et al. (2010) (filled upward triangle), Bean et al. (2013) (open circles), Burton et al. (2012) (open diamond), and Lendl et al. (2013) (filled downward triangles). The emission features at 4.5 μ m are due to CO in the presence of thermal inversion. The atmosphere of WASP-19b has been modelled with the parameters given in Table 1 using PHOENIX . It shows the presence of thermal inversion (see inset diagram, Fig. 1 ) and the equilibrium temperature is found to be ~ 2700 K. The comparison between observational data and our theoretical modelling (see Fig. 1 ) indicates that the excess planet-to-star flux ratio at 4.5 μ m is due to the presence of CO, which in turn is caused by thermal inversion that exists in the atmosphere. The corresponding pressure range, as probed by the secondary eclipse, is 10 −2 to 10 −3 bar. Previous studies of WASP-19b did not show any evidence of thermal inversion in its atmosphere ( Anderson et al. 2013 ; Bean et al. 2013 ). However, these studies were mainly based on retrieval techniques where abundances and thermal structure were fitted to the data. We investigate the effect of metallicity on the atmosphere of WASP-19b by changing all the heavy-elemental abundances by a factor of two. As shown by the inset image of Fig. 1 , the thermal inversion becomes stronger as the metallicity increases. This is mainly due to the temperature dependence of the thermal dissociation of various molecules, which changes the photospheric abundances ratio of TiO and H 2 O at different metallicities ( Parmentier et al. 2018 ). At higher
Paper: 2018MNRAS.476...12B (acknowledgments)
ACKNOWLEDGEMENTS GAMA is a joint European-Australasian project based around a spectroscopic campaign using the AngloAustralian Telescope. The GAMA input catalogue is based on the data taken from the Sloan Digital Sky Survey and the UKIRT Infrared Deep Sky Survey. Complementary imaging of the GAMA regions is being obtained by a number of independent survey programs including GALEX MIS, VST KiDS, VISTA VIKING, WISE, Herschel -ATLAS, GMRT, and ASKAP providing UV to radio coverage. The VISTA VIKING data used in this paper are based on observations made with ESO Telescopes at the La Silla Paranal Observatory under programme ID 179.A-2004. GAMA is funded by the STFC (UK), the ARC (Australia), the AAO, and the participating institutions. The GAMA website is http://www.gama-survey.org/ . This work made the extensive use of topcat Taylor ( 2005 ) and stilts Taylor ( 2006 ) software packages, which are supported by an STFC grant to the University of Bristol.
Paper: 2020ApJ...891...75P (fulltext)
such amplification is two-fold: partly due to a contribution from vortical flow and partly associated with the enhanced Rayleigh–Taylor instability. The spatial location of the maximum magnetic field is quite near the immediate downstream, indicating that amplified magnetic field could occur in the immediate vicinity of an SNR shock. It is not surprising to see the discrepancy between our results and the results given by Guo et al. ( 2012 ), which could be attributed to the time-dependent effective adiabatic index adopted here. Reproducing such a feature is pivotal, as X-ray thin rims seen in some SNRs, and their coincidence with the inferred shock locations have suggested an amplified magnetic field occurring at or within a short distance to forward shock. The maximum values of density and magnetic field are also obtained under this circumstance. Besides, we also trace the relative positions of contact discontinuity and the derived profiles are consistent with the observational results for two typical Ia SNRs: SN 1006 and Tycho. It is noteworthy that the lowest value of our derived profile in both cases is about 1.02 which seems at odds with suggested value ∼1.0 from X-ray observations (Warren et al. 2005 ; Cassam-Chenaï et al. 2007 , 2008 ; Miceli et al. 2009 ). Meanwhile, we also find no obvious ejecta protrusions which have been identified by several authors both in Tycho and SN 1006 irrespective of their elusive origin (Velázquez et al. 1998 ; Rakowski et al. 2011 ). The foregoing inconsistency indicates us that there may exist alternative scenarios which can further enhance Rayleigh–Taylor instability or even modify the initial ejecta profile, thus reducing the relative positions of contact discontinuity to match observations. As a recent attempt, Ferrand et al. ( 2019 ) have investigated the imprint of an asymmetrical SN Ia explosion on the resulting SNR morphology with a state-of-the-art scheme. The clumpy patterns are clearly seen in their synthetic thermal emission maps and the entire SNR morphology seems very realistic-looking (resembling the X-ray observational results of Tycho). Some Rayleigh–Taylor fingers are even protruding beyond the main forward shock, which is in accord with the findings in Orlando et al. ( 2012 ) where the ejecta clumping serves as an underlying assumption. Moreover, by postprocessing our data, a compared inspection with the case of no acceleration is given. The shock positions, radially averaged magnetic fields, time evolutions of the maximum magnetic field strength, and its relative locations in different circumstances have been depicted. The closer analysis reveals that when consider the turbulent background solely, the forward shock extends but the ripples seem to be less obvious. The maximum radially averaged magnetic field is smaller than the case with efficient acceleration by a factor of about 1.9. Similar conclusion can be drawn for the evolution of the maximum magnetic field strength. As for the
Paper: 2019ApJ...887..119L (acknowledgments)
We thank the referee for comments that helped to improve the presentation of this paper. This research was supported by the National Natural Science Foundation of China under grant No. 11903002 and the Guangxi Natural Science Foundation under grant No. 2017GXNSFAA198348. Funding for the Sloan Digital Sky Survey IV was provided by the Alfred P. Sloan Foundation, the U.S. Department of Energy Office of Science, and the Participating Institutions. SDSS-IV acknowledges support and resources from the Center for High-Performance Computing at the University of Utah. The SDSS website is http://www.sdss.org/ . SDSS-IV is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS Collaboration including the Brazilian Participation Group, the Carnegie Institution for Science, Carnegie Mellon University, the Chilean Participation Group, the French Participation Group, Harvard-Smithsonian Center for Astrophysics, Instituto de Astrofísica de Canarias, The Johns Hopkins University, Kavli Institute for the Physics and Mathematics of the Universe (IPMU)/University of Tokyo, Lawrence Berkeley National Laboratory, Leibniz Institut für Astrophysik Potsdam (AIP), Max-Planck-Institut für Astronomie (MPIA Heidelberg), Max-Planck-Institut für Astrophysik (MPA Garching), Max Planck-Institut für Extraterrestrische Physik (MPE), National Astronomical Observatories of China, New Mexico State University, New York University, University of Notre Dame, Observatário Nacional/MCTI, The Ohio State University, Pennsylvania State University, Shanghai Astronomical Observatory, United Kingdom Participation Group, Universidad Nacional Autónoma de México, University of Arizona, University of Colorado Boulder, University of Oxford, University of Portsmouth, University of Utah, University of Virginia, University of Washington, University of Wisconsin, Vanderbilt University, and Yale University.
Paper: 2019A&A...626L...4M (acknowledgments)
We thank our anonymous referee for helpful comments. This study was financed by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001. Part of this research is suported by INCT do e-Universo, Brazil (CNPQ grants 465376/2014-2). Based in part on observations made at the Laboratório Nacional de Astrofísica (LNA), Itajubá-MG, Brazil. BM thanks the CAPES/Cofecub-394/2016-05 grant. G.B.R. acknowledges the support of the CAPES and FAPERJ/PAPDRJ (E26/203.173/2016) grants. ARGJ thanks FAPESP proc. 2018/11239-8. MA thanks CNPq (Grants 427700/2018-3, 310683/2017-3 and 473002/2013-2) and FAPERJ (Grant E-26/111.488/2013). VL’s research was supported by an appointment to the NASA Postdoctoral Program at the NASA Jet Propulsion Laboratory, California Institute of Technology, administered by Universities Space Research Association under contract with NASA. RVM acknowledges the grants CNPq-306885/2013, CAPES/Cofecub-2506/2015, FAPERJ/PAPDRJ-45/2013 and FAPERJ/CNE/05-2015. JIBC acknowledges CNPq grant 308150/2016-3. FBR acknowledges CNPq support, proc. 309578/2017-5. RS and OCW acknowledge Fapesp proc. 2016/24561-0, CNPq proc. 312813/2013-9 and 305737/2015-5.
Paper: 2019A&A...624A..21G (fulltext)
modelled continuum. Star formation history was modelled by a short burst of star-forming galaxies with age less than 10 Myr, which represents the young stellar population in addition to continuous star formation responsible for older stars with ages t 1 and t 2 ( t 1 t 2 ) randomly varied in the range between 10 Myr and 15 Gyr. The derived age t 2 of the oldest stars is 10 Gyr in most of galaxies. The relation between [Mg/Ne] and stellar mass is shown in Fig. 4 . The linear regression to all Mg II galaxies is shown by a solid blue line and average values in bins of stellar masses by solid green lines. A slight slope is seen, meaning that more massive galaxies have higher magnesium depletion. In particular, [Mg/Ne] ≃ –0.15 for low-mass galaxies with M ⋆ ≃ 3 × 10 7 M ⊙ and ≃–0.35 for high-mass galaxies with M ⋆ ≃ 3 × 10 10 M ⊙ . A similar behaviour is seen for the magnesium depletion, which increases with metallicity (Figs. 1 c and d). Fig. 4. [Mg/Ne] – mass of stellar population log( M ⋆ / M ⊙ ) relation. Symbols are as in Fig. 1 . A linear regression to all Mg II galaxies is shown by the solid blue line, and the average values in bins of stellar masses are represented by solid green lines. 4.2. Dependence of [Mg/Ne] on O<sub>32</sub> O 32 = [O III ] λ 5007/[O II ] λ 3727 is an important parameter to search for galaxies with Lyman continuum (LyC) escaping radiation. The parameter O 32 is usually used for the selection of galaxy candidates able to ionise the intergalactic medium, which is interesting in connection with the problem of the reionisation of the Universe at redshifts z ∼ 5–10. The dependence of [Mg/Ne] on O 32 is shown in Fig. 5 . There is very large scatter of [Mg/Ne] for Mg II galaxies with O 32 ∼ 0–3 (Fig. 5 a). Nevertheless, in galaxies with O 32 below 3–4 [Mg/Ne] is constant, but at higher O 32 [Mg/Ne] increases with increasing O 32 . Linear regression to all the galaxies (purple line) and average values in bins of O 32 (blue lines) demonstrate an increase in [Mg/Ne] with increasing O 32 , which depends on the ionisation parameter. Moreover, the galaxies with O 32 > 5 are above the average value of [Mg/Ne] (long solid red and dashed black lines). We note that almost all galaxies with O 32 > 5 have low masses (log( M ⋆ / M ⊙ ) 9.0), low metallicities (12 + log O/H 8) and high EW(H β ) > 150 Å (dots inside green circles). In Fig. 5 b we only show galaxies with abundances derived by the direct T e method and accuracies better than 40% (more than 2.5 σ ) for the weak but important Mg II λ 2797Å and [O III ] λ 4363Å emission lines. Galaxies with flux ratios I [O III ] λ 4959/ I (H β ) less than 0.7 were also excluded to ensure that we use only high-excitation galaxies whose [O III ] λ 4363Å emission lines are reliably detected. The trend of increasing [Mg/Ne] with increasing O 32 becomes even more evident (purple line in Fig. 5 b). Fig. 5. Panel a : [Mg/Ne] – O 32 relations for all Mg II emitters depicted as in Fig. 1 . The dots
Paper: 2019A&A...627A.112K (fulltext)
4 Power distributions for phase fractions in different H I phases at v LSR = 0 km s −1 . Top left : WNM; top right : LNM; bottom left : CNM. Black lines show all-sky data, red lines are for | b | > 20°. Spectral indices are derived at 10 l 100 as indicated by the vertical lines. Bottom right : uncertainties from the Gaussian decomposition (black) and ringing caused by apodization (red). Fig. 5 Velocity distribution of average H I phase fractions for CNM, LNM, and WNM using unpublished data from Kalberla Haud (2018) . Next we consider the power spectra derived from the Gaussian components of different H I phases. Figures 8 and 9 show still local minima for the CNM spectral indices, however the spectral indices for the other phases are now mostly anti-correlated; the WNM has a particularly smooth distribution. Spectral indices derived from power spectra of the total observed H I also show smooth changes. For high latitudes ( Fig. 9 , red) we find a steepening in anti-correlation to the WNM, but with local minima that are affected by the CNM and LNM. In the all-sky case ( Fig. 8 , black) we observe a smooth trend, but influences from the CNM and LNM are weak. Velocity dependences for the spectral indices have been observed before ( Kalberla Kerp 2016 ; Kalberla et al. 2017 ). Recently, Choudhuri Roy (2019) have presented data that show a steepening of the spectral indices at velocities that are considered to be representative for the CNM. Spectral indices for 13 CO (2–1) and 12 CO (3–2) channel maps in the Perseus cloud were observed by Sun et al. (2006) with a steepening of the indices that can be described asan average structure of the index spectrum similar to the line profile. Numerical studies of the condensation of WNM into CNM structures caused by turbulence and thermal instabilities were conducted by Saury et al. (2014) . They found that turbulence plays a key role in the structure of the cold medium; it can induce the formation of CNM when the WNM is pressurized and put it in a thermally unstable state. They found evidence for subsonic turbulence with a shallow power index γ ~ −2.4. Recent high resolution simulations by Wareing et al. (2019) have shown that the thermal instability dynamically can form sheets and filaments on typical scales of 0.1–0.3 pc, explaining that as the structure grows in the simulation, the density power spectrum rapidly rises and steepens. Wareing et al. (2019 ; Fig. 10) derive γ ~ −3, but the steepening is most prominent on scales below 1 pc. Fig. 6 All-sky velocity dependences of spectral indices for H I phase fractions. The thin lines represent the scatter for one-sigma uncertainties. Fig. 7 High latitude velocity dependences of spectral indices for H I phase fractions. The thin lines represent the scatter for one-sigma uncertainties. The preferred width of the filaments in the range 0.1–0.3 pc is consistent with unresolved cold small-scale structures observed by Clark et al. (2014) and Kalberla et al.