adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020A&A...642A.183V (fulltext)
gainer has an initial mass of 2.7 M ⊙ . With CF = 0.95 one has a shell with an initial mass of 0.135 M ⊙ . The solidly rotating core keeps its mass of 2.565 M ⊙ during the entire evolution. At the end of RLOF A, the mass of the gainer has grown up to 6.936 M ⊙ . Since the core maintains its initial mass, the mass of the shell raises up to 4.371 M ⊙ , containing 63% of the gainer’s mass (CF = 0.37). At the end of RLOF B, the mass of the gainer is again somewhat larger and the value of CF shrinks to 0.31. Fig. 4. Mass fraction of the core (CF) decreases with time during the evolution of the system shown in Figs. 1 – 3 . This is not so different from the evolution of a shell with, for example, an initial value of CF = 0.5 that ultimately evolves into a massive shell with CF = 0.2 (80% of the mass of the gainer being in the shell), giving rise to magnetic fields and magnetic braking that are very similar to those obtained with an initial value of CF = 0.95. This value was used in all our calculations. 3.4. Calculated cases We found 44 binaries for which the equatorial velocity of the gainer was published. These systems are shown in Tables 1 and 2 . Table 1. Calculated equatorial velocities of gainers that best fit observations. Notes. Masses are in M ⊙ , orbital periods in days and v eq in km s −1 . The masses mentioned by the observations of Glazunova Yushcenko (2008) are from Budding et al. (2004) . Table 2. Calculated equatorial velocities of gainers fitting observations less good than in Table 1 . Notes. Masses in M ⊙ and orbital periods in days. Masses for V356 Sgr are from Dominis et al. (2005) . Masses in Glazunova Yushcenko (2008) are from Budding et al. (2004) and Brancewicz Dworak (1980) . For every binary in these tables, we calculated the evolution of a number of possible progenitors. In the case of conservative evolution, the initial orbital period P 0 of every progenitor was determined from the presently observed period P 1 with relation ( 13 ), which obeys to the rule of conservation of angular momentum: The subscript d is for the donor and g for the gainer. Although we calculated the orbital period with relations ( 10 )–( 12 ) using our code, relation ( 13 ) yields correct initial periods in the case of conservative evolution. This demonstrates that neglecting the spin angular momenta of the individual stars, as done in relation ( 13 ), is of minor importance. In the case of liberal evolution initial periods are determined with relation ( 14 ). This relation is valid when all matter that leaves the system takes only the orbital angular momentum of the involved stars. Here, M is the total mass of the system, so that: The quantity Δ M SW , the amount of mass that leaves the system as stellar wind, is always small compared to the stellar masses. The quantity Δ M OUT is the amount of RLOF mass that is not accreted by the gainer. This amount can be large and is difficult to predict. In our sample of 44 binaries, only two of them (V356
Paper: 2018A&A...609A..92C (fulltext)
there is no over-exposure on the QS. Figure A.7 shows the CC constructed for the extreme exposure problems considered. The green, blue, and red curves display the imposed relation, the measured QS CLV, and the standardized linear relation, respectively. For all the data, our method allows calculating the NSB accurately enough so that the obtained CLV reproduces the imposed relation for almost the whole range of QS values. In particular, the maximum unsigned relative difference between the imposed CC and the one recovered by the method is less than 0.005. The accuracy drops slightly for the last point at the limb and only for the cases of severe under-exposure, rising to 0.017. Subset 4: Different disc sizes with linear CC without inhomogeneities in the image The photographic plates from different observatories have different sizes. Even among the images of the same archive there are variations due to changes of the spectroheliograph and of the photographic plates. The digitization affects the number of pixels covered by the disc as well. The disc size is also affected by the seasonal variation of the Sun–Earth distance. Therefore, this subset was created to test the efficiency of the proposed method when applied to datasets that have a different disc size. We selected 10 Rome/PSPT images covering the period 2000–2014 and re-sampled them, so that each was represented in the form of solar images with 10 different radii between 100 and 550 pixels. The range of radii was defined to include the sampling present in most available datasets, however the upper limit was dictated by the number of pixels covered by the disc on Rome/PSPT observations. Subset 4 consists of 100 images. Figure A.8 shows the relative differences between the images derived with our method and the original Rome/PSPT data. Panels a) and b) refer to values of NSB, while panels c) and d) refer to values of contrast images, each (coloured) box for a different image of subset 4. In each panel, rows (columns) show values derived from different observations (imposed radii). The relative difference between the retrieved NSB (SB) and imposed background decreases with increasing radius of the disc; the maximum unsigned difference measured is 0.03 (0.007) for the images with the smallest radius, average difference is , and maximum of rms difference is 0.004 (0.001). The relative difference between the contrast image derived with our method and the original data reaches up to 0.056; the average differences are ; the rms differences are . Fig. A.8 Colour-coded relative differences between the images retrieved by applying our method to images of subset 4 and the underlying original, undegraded Rome/PSPT images. Each box (with a given colour) corresponds to a different synthetic image; row (column) shows results derived from a given Rome/PSPT observation (imposed radius). Note the different scale of the colour code, as represented by the corresponding colour bars. a) Mean relative and b) rms
Paper: 2020ApJ...890..128F (acknowledgments)
We wish to thank the referee, Dr. Thorsten Lisker, for the very thorough reading of the paper and the many insightful and useful suggestions. This work is supported in part by the Canadian Advanced Network for Astronomical Research (CANFAR), which has been made possible by funding from CANARIE under the Network-Enabled Platforms program. This research used the facilities of the Canadian Astronomy Data Centre operated by the National Research Council of Canada with the support of the Canadian Space Agency. The authors further acknowledge use of the NASA/IPAC Extragalactic Database (NED), which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration and the HyperLeda database ( http://leda.univ-lyon1.fr ). This publication has made use of data products from the Sloan Digital Sky Survey (SDSS). Funding for SDSS and SDSS-II has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, the US Department of Energy, the National Aeronautics and Space Administration, the Japanese Monbukagakusho, the Max Planck Society, and the Higher Education Funding Council for England. S.C. and J.T. acknowledge support from the Natural Science and Engineering Research Council of Canada through a Research Discovery Grant. E.W.P. acknowledges support from the National Natural Science Foundation of China under grant Nos. 11173003 and 11573002 and from the Strategic Priority Research Program, “The Emergence of Cosmological Structures,” of the Chinese Academy of Sciences, grant No. XDB09000105. This work is supported in part by the French Agence Nationale de la Recherche grant VIRAGE (ANR10-BLANC-0506-01). C.L. acknowledges the NSFC grant Nos. 11203017 and 11125313. P.G. and E.T. acknowledge support from NSF grants AST-1412504 and AST-1010039. Facility: Canada–France–Hawaii Telescope (CFHT).
Paper: 2017ApJ...847L..12S (fulltext)
as shown in the right panel of Figure 2 . A power-law index of −0.6 clearly agrees with the mass function slope for low-mass objects. This suggests that the differential mass functions are well fit by a single power law because there is a significantly larger number of planetesimals at small masses, thus weighting the fit toward the small-mass end. Therefore, while in reality the mass distribution will have a cutoff at some high-mass value (which depends on τ ), the differential distribution can be well fit by a single power law that is representative of masses away from the cutoff. Finally, we have also examined the evolution of p and the maximum and minimum planetesimal masses (in all three simulations) for times after which a significant number of planetesimals have formed; the results are shown in Figure 3 . Note that due to limitations with the clump finder algorithm (as described more fully in Simon et al. 2016 ), we smoothed the curves (with a boxcar average) to remove noise associated with this algorithm. As the figure shows, p and the minimum planetesimal mass are relatively constant in time, 7 7 We do observe a slight decrease in the minimum mass in some instances, a behavior that could arise from a combination of processes, including tidal stripping of smaller bodies by large planetesimals, fragmentation of smaller bodies, and/or the preferential production of smaller planetesimals as the reservoir of particles from which to produce planetesimals decreases in size (Johansen et al. 2015 ). Elucidating the nature of this behavior requires a more sophisticated clump-finding algorithm and will thus be reserved for future work. whereas the maximum planetesimal mass grows by a factor of order unity in each simulation, likely due to accretion of smaller particles and/or mergers with smaller planetesimals. Despite this growth, the mass function does not evolve appreciably once planetesimals have formed. Figure 3. Time evolution of the power-law index (solid black line), maximum planetesimal mass (dashed red line), and minimum planetesimal mass (dotted blue line) for the (left), (middle), and (right) simulations for a period of time after planetesimals have formed. Both the power-law index, p , and the minimum planetesimal mass are relatively constant in time, whereas the maximum planetesimal mass grows slowly, presumably due to continued accretion of small particles and/or mergers with smaller planetesimals. Our ability to directly measure any potential dependence of the mass function on particle properties is limited by the relatively small number of collapsed clumps, but across the range of τ considered, we can bound deviations at the level of . We can therefore exclude, with moderately high confidence, the possibility that the streaming instability might result in a steep mass function () with most of the mass in the smallest planetesimals. 4. Particle Clustering Prior to Collapse The similarity in the mass functions from the different
Paper: 2018ApJ...856...31T (acknowledgments)
We thank the anonymous referee for providing comments that significantly improved the manuscript. The authors would like to acknowledge the contribution of an Australian Government Research Training Program Scholarship in supporting this research. This work was supported by resources provided by the Pawsey Supercomputing Centre with funding from the Australian Government and the Government of Western Australia. This scientific work makes use of the Murchison Radio-astronomy Observatory, operated by CSIRO. We acknowledge the Wajarri Yamatji people as the traditional owners of the Observatory site. Support for the operation of the MWA is provided by the Australian Government (NCRIS), under a contract to Curtin University administered by Astronomy Australia Limited. We gratefully acknowledge the support of NASA and contributors of SkyView surveys. Funding for Breakthrough Listen research is provided by the Breakthrough Prize Foundation. 3 3 http://breakthroughprize.org Facility: MWA .
Paper: 2019MNRAS.484...81P (fulltext)
kpc which is almost the same as the scale length of the molecular disc which is ∼4.05 kpc. However, for our sample galaxies, the scale length of the molecular scale height is found to be approximately a factor of 2 greater than the scale length of the exponential surface density profile (see Fig. 6 ). This result strongly indicates that the coupling with the other disc components and with the dark matter halo significantly influence the molecular scale height. Figure 7. The behaviour of the molecular scale height in the absence of any external influence in NGC 3198. The solid red and the black dashed lines represent the molecular gas surface density profile (), and it’s fit to an exponential profile, respectively (marked by the left axis). The solid magenta and the dashed-dotted blue lines represent the estimated molecular scale height (HWHM) and its fit to a rising exponential function, respectively (marked by the right axis). The scale lengths of the exponential fits to the ( l CO ) and the HWHM profile ( l scl ) are found to be ∼4.05 and ∼4.06 kpc (as quoted in the top left), are almost the same. Next, we use the solutions of the hydrostatic equilibrium equation and the observed rotation curves to build a dynamical model of the molecular discs for our sample galaxies. We then incline these dynamical models to the observed inclinations and project it to the sky-plane to produce a column density map. These maps then convolved with the telescope beam (13 arcsec × 13 arcsec) to produce simulated maps that are equivalent to the observed ones. In Figs 8 and 9 we compare these simulated maps with the observed maps. In the left-hand columns of the figures, we plot the modelled column density maps in contour on top of the observed maps in colour scale. As we do not solve the hydrostatic equation for the central regions of our sample galaxies, the central region should be excluded for any comparison. Further, the convolution with the telescope beam at the edge of the central region would corrupt an area equivalent to the beam size. We hence exclude a region that we do not solve from the centre plus the beam size. The vertical dashed lines in each panel of the left-hand column represent this excluded region. As can be seen from the figure, the contours (modelled) match to the observed map reasonably well. However, for two galaxies NGC 925 and NGC 2976, the model seems to deviate from the observation. This is due to azimuthal asymmetry in their column density distribution that is one of our implicit assumptions. We would like to emphasize here that, the molecular surface density profiles of our sample galaxies are calculated through spectral stacking method (Schruba et al. 2011 ). This produces surface density profiles that are much more sensitive than what will be observed in the moment zero maps. Because of this, the surface density profiles of our sample galaxies extend to a much larger radius as given in Schruba et al. ( 2011 ) than what can be
Paper: 2018ApJ...855...22G (acknowledgments)
D.A.G. gratefully acknowledges Eric Linder for a careful reading of the manuscript and for thoughtful feedback. Greg Aldering and Alex Kim also deserve thanks for feedback that improved the paper. D.A.G., T.E.C., and P.E.N. are members of the Large Synoptic Survey Telescope Dark Energy Science Collaboration (LSST-DESC), and they acknowledge Phil Marshall and the Strong Lensing working group for bringing them together so that this work could be launched. D.A.G. thanks Kyle Barbary for leading the development of sncosmo (Barbary et al. 2016 ), which was essential to the analysis. The authors acknowledge support from the DOE under grant DE-AC02-05CH11231, Analytical Modeling for Extreme-Scale Computing Environments. D.N.K is supported in part by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics, under contract number DE-AC02-05CH11231 and DE-SC0017616, and by a SciDAC award DE-SC0018297. This research was supported by the Gordon and Betty Moore Foundation through Grant GBMF5076. The authors are grateful to the Royal Society for an International Exchange Grant (IE170307), which has supported our collaboration on this work. This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 as well as the savio cluster at UC Berkeley. Figures 2 , 9 , and 11 were rendered with code based on ChainConsumer (Hinton 2016 ). This research has made use of NASA’s Astrophysics Data System. This research made use of Astropy, a community-developed core Python package for Astronomy (Astropy Collaboration et al. 2013 ).
Paper: 2019MNRAS.489.4543F (acknowledgments)
ACKNOWLEDGEMENTS We thank the referee for insightful comments, which were very valuable for improving the paper. GF is supported by the Foreign Postdoctoral Fellowship Program of the Israel Academy of Sciences and Humanities. GF also acknowledges support from an Arskin postdoctoral fellowship at the Hebrew University of Jerusalem. GF thanks Seppo Mikkola for helpful discussions on the use of the code archain. Simulations were run on the Astric cluster at the Hebrew University of Jerusalem.
Paper: 2020A&A...634A..40B (fulltext)
is submitted to the electric field of a single proton. The calculation of this electric field requires knowing the distance between the electron and the proton, which can be taken as the mean distance between particles, which is the −1/3 power of their density, which is roughly the same for electrons and protons. This electric field derives from a potential, as does the gravitation field. Thus, there is also an escape velocity, as there is for gravitation. The escape velocity for freeing the electron from the corresponding single proton is then where q is the elementary charge, m e is the electron mass, and n the density, which is nearly the same for electrons and protons. In the solar interior conditions given by Allen (1973) , we then obtain that this escape velocity is nevertheless six times lower than the quadratic average thermal velocity for electrons, which then also escape from the protons, at least initially. As the escape velocity is obtained by equating the particle kinetic and potential energies, the total escape velocity from gravity and proton for an electron is and we obtain that for electrons it is 5.5 times lower than the quadratic average thermal velocity at 0.5 R ⊙ . The electrons then initially escape from gravity and protons. 6.4. Escape velocity from several protons As the electron cloud is free at the beginning, in a first approximation it begins to expand. The result is a decrease in the electron density, while the proton density remains unchanged. As a consequence, the number of electrons and protons is not yet the same in a given volume. As a result, an escaping electron is now submitted to the attraction of more than one mean proton, depending on the density ratio. If we denote as ρ this density ratio where n e and n p are the electron and proton density, respectively, the total escape velocity of the electron submitted to ρ > 1 protons and to gravity becomes because the mean distance between the electron and the other particles is . If we denote as x the ratio between the electron escape and thermal velocities we obtain from the preceding equations which permits us to compute the proton-to-electron density ratio ρ from the desired velocity ratio x . The electron escaping will certainly be suppressed when x = 3, which means that the escape velocity will be at least three standard deviations higher than the thermal velocity for each velocity component. In this case, ρ is found to be 6 × 10 3 at 0.5 R ⊙ in the Allen (1973) conditions. This corresponds to a mean distance between electrons 18 times larger than that between protons. These quantities are found to be quite constant along the solar radius. 6.5. Spreading time One important question is where exactly do the initially free electrons escape, and the answer is to the distance where the electron cloud density is divided by the ratio ρ defined above. We propose that the density scale height is the typical dimension of the cloud. We denote as h this density scale
Paper: 2017MNRAS.465.2559S (fulltext)
BYF40 HCN ( l , b , V ) data cube we chose as a test area for our code development, and has the advantage of being scriptable in a modern computing environment, as well as having a built-in tool for fitting the model across all spectra in a cube. Assuming LTE, it solves for the common parameters of all hyperfine components. In order to improve the fit quality, the original fits cube input for the package was binned by two spectral channels and convolved from an original resolution of 4060arcsec. Once a minimum signal-to-noise ratio (S/N) threshold is set for the hyperfine fitting, PySpecKit yields separate 2D maps of the excitation temperature T ex , opacity , mean velocity V LSR , and velocity dispersion V , as well as error maps for each parameter map. A sample fit is shown in Fig. 3 , while the parameter maps appear in AppendixA. 3.2 Column density calculation After obtaining the parameter maps and uncertainties as outputs from PySpecKit , the column density N was evaluated using a mix of C shell and miriad (Sault, Teuben Wright 1995 ) software. and T ex were measured in each voxel and then converted into N HCN using the equation Here, Q refers to the partition function, E up is the energy of the upper level above the ground state of the J upper J lower transition, h is the energy of the transition, and k is the Boltzmann constant. The derived N HCN map is shown in Fig. 2 , and has a flatter distribution compared to the overlayed I HCN contours. Figure 2. Maps of N HCN (a) and its uncertainty (b) across BYF40, plotted with contours at intervals of 10percent of the peak I HCN . Figure 3. Sample spectra from the original, model, and residual data cubes. Each was measured at the coordinate l = 284.002(3), b = 0.846(3). Under traditional methods, the X factor for 12 CO is often used to estimate column densities with a simple linear conversion. The accuracy of such conversions has frequently been debated, and recent work by our group (Barnes etal. 2015 , 2016 ) has shown that, in the case of CO at least, both the lines T ex and need to be properly accounted for, and must be self-consistently measured. In this paper, we calculate the uncertainty in N HCN by propagating all of the formal fit errors, and applying the constraints T ex 2.73K and 0 on the PySpecKit solutions. By using the error images and the output images together, relative errors were also determined for several intermediate quantities in the calculation, and for N HCN . The V LSR values fall in the range of 810kms 1 , V = 1.22.2kms 1 , T ex = 49K, and = 0.52.5. 3.3 Clump masses In Table 1 we give the observed and physical parameters of the BYF40 clumps used to derive these results. The values appear similar to table 4 from Paper I , due to the high correlation present between HCN and HCO in the clump. Additionally, Fig. 4 (b) shows that there is a high correlation between the integrated intensities of HCN and HCO . This can be seen in the Fig. 1 (a) Mopra map, where the blue of HCO