adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018MNRAS.475.4066V (acknowledgments)
Acknowledgements This work has benefited greatly from discussions with the following individuals: Andy Burkert, Benedikt Diemer, Oliver Hahn, Andrew Hearin, Fangzhou Jiang, Johannes Lange, and Fred Rasio. We also acknowledge the referee, Chris Power, for an insightful, constructive referee report. FvdB is grateful to the Munich Excellence Cluster for its hospitality, and is supported by the Klaus Tschira Foundation and by the US National Science Foundation through grant AST 1516962. Part of this work was performed at the Aspen Center for Physics, which is supported by the National Science Foundation under grant PHY-1066293, and at the Kavli Institute for Theoretical Physics, which is supported by the National Science Foundation under grant PHY-1125915. GO is supported by funding from the European Research Council under the European Union's Horizon 2020 research and innovation programme (grant agreement no. 679145, project ‘COSMO-SIMS’). Some of the numerical computations were carried out on HA-PACS at the Center for Computational Sciences at University of Tsukuba.
Paper: 2020A&A...635A..26B (fulltext)
is not taken into account. The observed seismic diagnostics are indicated in Fig. 1 . I also show the best model for each case in Tables 3 and 4 (red thin lines). These optimal theoretical values are barely distinguishable from one model to the other. This indicates that they may be equally valid in order to reproduce the data, and that statistical model comparison methods (see e.g. Robert 2007 , Chap. 7) alone will not suffice to distinguish them. Further discussions on model comparison are given in Sects. 3.2 and 3.1 . Visual inspection of Fig. 1 also indicates that the accuracy of the present results is similar to what was obtained by Metcalfe et al. (2015) . Figure 2 shows the MAP estimates of all non-seismic observations. They are all reproduced with good accuracy, within 1 σ of the observed values. Fig. 1. Seismic diagnostic for 16 Cyg A ( left panel ) and B ( right panel ). The squares, triangles, and circles denote the observed r 13 , r 02 , and r 01 observed ratios, respectively. The red lines show the best models for all runs in Tables 3 and 4 . Fig. 2. Non-seismic observations for 16 Cyg A ( left panel ) and B ( right panel ). The horizontal lines represent the observed values (full lines) and the corresponding uncertainties (dashed lines). The dots denote the estimated values (MAPs of the marginal densities) for the corresponding observation, for each run. The runs are labelled on the abscissa according to the numbering given in Tables 3 and 4 . The two-dimensional joint PDFs for this set-up are shown in Fig. 3 alongside the corresponding correlation coefficients. The mass correlates most significantly with X 0 . The age correlates extremely well with the mixing-length parameter and slightly less with X 0 . It may be counter-intuitive that the mass does not correlate strongly with the age. A likely explanation is that, due to the level of constraint imposed by the seismic data, these two parameters cannot vary enough to allow such a correlation to be observed. Interpreting these correlations is not straightforward because there is in general no one-to-one correspondence between a parameter and an observation. Thus elements of interpretation can be gathered by looking at the correlation between the stellar parameters and other quantities. The mass and X 0 (Pearson correlation coefficients ≳0.8) both correlate strongly with the luminosity and the radius on the zero-age main sequence (ZAMS). This is related to the mass-luminosity relation on the ZAMS, in which the mean-molecular weight enters (see e.g. Clayton 1968 ). To maintain a roughly constant luminosity, an increase in mass must be compensated by a decrease in the mean-molecular weight. This latter corresponds to an increase in the initial hydrogen-mass fraction and a decrease of the initial helium-mass fraction because the two quantities are perfectly anti-correlated. The interaction between the mass and the hydrogen-mass fraction thus sets the initial conditions of the
Paper: 2019ApJ...875...12R (acknowledgments)
We thank Vasileios Paschalidis for useful discussions and comments on this work. C.R. is supported by the NSF Graduate Research Fellowship Program Grant DGE-1143953. F.O. and D.P. acknowledge support from NASA grant NNX16AC56G.
Paper: 2020MNRAS.497.5061I (fulltext)
the same plots as Fig. 5(a) but for the cases with more massive clouds with M cl = 10 6 M ⊙ , where the FUV feedback effects are more remarkable than other cases. In this case, T out is much higher than the threshold temperature 100 K for any range of Σ cl . This is due to the dependence of G out ∝ S FUV Σ cl / M cl again. With a fixed value of Σ cl , G out is larger with higher M cl because G out ∝ ε S FUV / M * = εΞ FUV and ε is enhanced following equation ( 22 ). The SFE required to disrupt the natal cloud is much smaller than the case only with EUV feedback. Fig. 5(b) also suggests that even with a large threshold temperature ≲700 K, the FUV feedback should still reduce the minimum SFE ε min . 3.3 Chemical compositions of molecular cloud remnants Our calculations suggest that the EUV and FUV radiative feedback from forming clusters jointly contribute to reduce the SFE of molecular clouds. In this section, we cast light on the gas that has not been used for star formation (i.e. the ‘remnants’ of the clouds). The cloud remnants still retain a large part of the cloud materials because the obtained SFEs are much smaller than unity for many cases. We focus on the chemical compositions of the cloud remnants, which are also followed in our calculations. We calculate the masses of H i , CO-dark and CO-bright H 2 gases as follows: First, we consider the cases with the fixed cloud mass M cl = 10 5 M ⊙ . Fig. 6(a) presents the mass fraction of the gas with the different chemical properties as functions of Σ cl . The neutral and molecular hydrogen are the dominant components of the cloud remnants, and they occupy 70 and 30 per cent of the total mass, respectively. In particular, we distinguish H 2 molecules associated with CO molecules and those without CO. The H 2 gas without CO molecules is the so-called ‘CO-dark’ molecular gas. Let us look at the cases where the minimum SFE is limited by the EUV and FUV feedback (Criterion 2, solid lines). Fig. 6(a) shows that most of the H 2 molecules contained in the remnants are actually CO-dark. Such a trend only has a weak dependence on Σ cl ; the mass of the CO-dark H 2 gas is generally much less than 10 per cent of that of the H 2 gas associated with CO molecules. This is caused by the different shielding processes of H 2 and CO molecules. As shown in Fig. 6(c) , the column density of the shell is roughly , corresponding to A V ≃ 1–3.5. The dust attenuation of the FUV radiation is not very efficient for such cases. In fact, Fig. 6(b) shows that the FUV flux at the shock front non-dimensional quantity ( G out ) is several to several tens, which is high enough to photodissociate CO molecules. However, H 2 molecules are protected against the FUV radiation by the self-shielding effect even with the small column densities. As self-shielding is not available for CO molecules, which only have small abundance, CO molecules are selectively destroyed. Figure 6. Chemical compositions of the gas that has not been
Paper: 2016MNRAS.458.2360J (acknowledgments)
We thank the referee for critical comments which helped to improve the paper. This research has made use of the MAXI data provided by RIKEN, JAXA and the MAXI team. We acknowledge financial support by the Polish NCN grant DEC-2012/07/N/ST9/04242.
Paper: 2020ApJ...891..137Z (fulltext)
of Galactic anticenter, the kinematic method tends to gain larger distances, which may be caused by the deviation due to noncircular motion (Tian Leahy 2012 ) or the larger uncertainty of the rotation curve in the outer disk in comparison with inner disk due to invalidity of the tangent velocity method. The kinematic method suffers the well-known ambiguity problem as well. Ten estimates derived from the associated objects show the difference with our results on a similar level to the kinematic method. Distance measurements based on RC stars are mainly applicable to the SNRs in the first and fourth quadrants of the Milky Way because RC stars are much less abundant toward Galactic anticenter. So only 3 of 22 SNRs are studied with the RC stars. Two of them (G82.2+5.3 and G109.1–1.0) yielded much larger distances than ours as well as other results, and the estimate to G89.0+4.7 is comparable to ours. Distances derived from the Σ − D relation (seven cases) are much closer to our estimates compared to the results with methods of kinematics and associated objects. The five Sedov estimates all yield distances larger than ours. The three studies based on proper motion or shock velocity provide some informative restrictions on the SNRs in Level B whose distances are not well determined in this study. The detailed analysis of the differences can be found in the Appendix . 4.1.3. Distribution in the Galactic Plane With the measured distances, Figure 9 shows the spatial distribution of 22 SNRs in Level A and B projected on the Galactic plane. Due to the limits of the APOGEE and LAMOST spectroscopic surveys, most of the SNRs under investigation are in the second and third quadrants of the Milky Way. It can be seen that the SNRs are mainly located in the Local arm. Six SNRs are suggested to reside in the Perseus arm. Three cases, i.e., G89.0+4.7, G93.7−0.2, and G94.0+1.0, are found between the two arms. Figure 9. A face-on view of the distribution of the SNRs in Level A (15) and B (7). The red dots and squares with error bars show the measured distances and uncertainties. The black squares are the cases with distance upper limits. The sizes are proportional to their diameters. The background image, created by Robert Hurt in consultation with Robert Benjamin (Churchwell et al. 2009 ), is centered at the Sun, with the Galactic Center in the upward direction. 4.2. The Extinction Curve The extinction curves vary from one sightline to another, revealing the wavelength-dependent extinction law in different environments. As the absolute extinction is usually difficult to measure and uncertain, we use the color excess ratios (CERs), i.e., , to represent the extinction curves for the sightlines to the 32 SNRs, as well as the “ l 165” and the Rosette Nebula. For SNRs whose reddening profiles were well model-fitted, the color excess is then δA in Equation ( 3 )—the amplitude of the extinction jump at a given color. For the other SNRs, we first calculate the color
Paper: 2019ApJ...873...78B (fulltext)
stellar mass growth that for individual massive galaxies. At a fixed space density threshold of density of , the most massive blue galaxies are ∼0.3 dex lower in stellar mass than the most massive red galaxies (i.e., half the mass). While we expect the most massive red galaxies to remain on the red sequence and increase in mass via mergers, it is likely that the most massive blue galaxies have star formation quenched and then move onto the red sequence. The rate at which massive red galaxies increase in stellar mass through mergers with smaller galaxies appears to slow somewhat from ∼0.4 dex per unit redshift for z = 1.1 to z = 0.7 to ∼0.2 dex per unit redshift for z = 0.7 to z = 0.3. We know that massive early-type galaxies must have grown in stellar mass because observations clearly show them experiencing mergers sufficient in frequency and mass ratio to give rise to a significant increase in stellar mass. In fact, merger studies have produced a range of estimates of stellar mass growth. For example, van Dokkum ( 2005 ) found a stellar mass increase due to mergers in massive red galaxies of per Gyr, which implies a stellar mass increase over the 4.7 Gyr from z = 1.1 to z = 0.3 of ×∼1.4. Similarly, López-Sanjuan et al. ( 2012 ) found stellar mass growth in massive early-type galaxies due to mergers of ×∼1.3 from z = 1 to the present. On the other hand, Masjedi et al. ( 2008 ) found that luminous red galaxies were growing due to merger activity at a much slower rate: at least per Gyr on average at redshift ∼0.25, implying at least 8% growth from z = 1.3 to z = 0.3. The lack of evolution (in comoving coordinates) of the spatial correlation function of massive red galaxies also clearly indicates that the most massive red galaxies must be undergoing mergers. White et al. ( 2007 ) used observations of the clustering of luminous red galaxies to show that about one-third of the most luminous satellite galaxies appear to have undergone merging or disruption with massive halos between z = 0.9 and z = 0.5, while Brown et al. ( 2008 ) found that massive red galaxies grew by ×1.3 from z = 1.0 to z = 0. As with SMD evolution, our stellar mass growth measurements for massive red galaxies based on SMF evolution (×1.7) are comparable with the those implied by comparison of the K -band LF with passive evolution (×1.4). 10.4. Stellar Mass Evolution—Errors In addition to the sources of error inherent in measuring K -band luminosity evolution (Section 9.4 ), we have one very significant additional source of error in measuring stellar mass evolution—namely, uncertainty in stellar mass-to-light ratios M / L K —and we include this in Table 5 for red galaxies. M / L K uncertainties arise from the evolving M / L K –rest-frame ( B − V ) relationships in Equations ( 3 ) and ( 5 ). We take the intrinsic random variation in between galaxies to be 0.1 dex (Section 5 ). Systematic uncertainties arise from the fact that Equations ( 3 ) and ( 5 ) are derived from SPS
Paper: 2016A&A...596A..60B (acknowledgments)
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. The Nançay Radio Telescope is operated as part of the Paris Observatory, in association with the Centre National de la Recherche Scientifique (CNRS) and partially supported by the Région Centre in France. This research has made use of the HyperLeda database ( http://leda.univ-lyon1.fr ), 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 Sloan Digital Sky Survey which is managed by the Astrophysical Research Consortium for the Participating Institutions.
Paper: 2021MNRAS.505.1607B (fulltext)
(Fabricius et al. 2021 ). Radial velocity measurements in Gaia EDR3 are nearly the same as in Gaia DR2. Further details on Gaia EDR3 are available at https://www.cosmos.esa.int/web/gaia/earlydr3 . The membership selection in NGC 752 from the Gaia EDR3 data set is carried out in a region of radius 5° around its centre. This sample is termed All sources . We use ML-MOC (Agarwal et al. 2021 ) to identify cluster members using the proper motion and parallax information. While details of the membership selection are described in Agarwal et al. ( 2021 ), we briefly state it here. ML-MOC is based on the k-nearest neighbour algorithm (kNN; Cover Hart 1967 ) and the Gaussian mixture model (GMM; Mclachlan Peel 2000 ). It identifies cluster members in the PM– parameter space, independent of the spatial density of the cluster, thereby allowing for the identification of faint extended spatial structures such as tidal tails. As a first step to the membership selection, kNN is utilized to remove the obvious field stars such that the remaining member candidates have more cluster members than field stars. This is done by applying kNN to those stars within the central region of radius 1° to calculate a broad range of PM and ω values for the cluster members. For NGC 752, we find the range of PM in RA, μ α* = 8.372–11.372 mas yr −1 , PM in Dec., μ δ* = −13.948–−10.348 mas yr −1 , and ω = 1.552–5.199. The stars in All sources which fall in the aforementioned parameter ranges are termed Sample sources . Their spatial, PM, and ω distributions are shown in Fig. 1 . For the second step, a three-dimensional GMM is used in the PM–ω parameter space of the Sample sources to distinguish between the cluster and field members, also assigning a membership probability ( p memb ). Those sources having p memb ≥ 0.6 are considered as high probability members, also including all the radial velocity members. A small number of stars having 0.2 ≤ p memb ≤ 0.6 are also considered as members if their values lie within the range of values specified by the members that have p memb ≥ 0.8. This results in the identification of 282 members in NGC 752 whose spatial, PM, and ω distributions are also shown in Fig. 1 . Note the extended tails are clearly identified in the spatial distribution. The identified members with their Gaia EDR3 IDs, astrometry, photometry, radial velocity (RV), and p memb have been tabulated in Table 1 . Figure 1. The spatial, proper-motion, and parallax distributions of the Sample sources and the member stars identified by ML-MOC. Table 1. Gaia EDR3 members identified in NGC 752 using ML-MOC. The full table will be available in the online version of the journal. In Fig. 2(a) , the marked p memb values show that the extended tails are present for the high probability members showing their identification as being robust. The mean distance to the cluster is found to be 443.8 pc (see Agarwal et al. 2021 ) consistent with literature values (Agüeros et al. 2018 ).
Paper: 2019ApJ...884...46M (acknowledgments)
The authors would like to thank the SDO and RHESSI teams for their open data policy. SDO and RHESSI are NASA’s missions under the Living With a Star (LWS) and SMall EXplorer (SMEX) programs, respectively. The authors would also like to thank the NICT team for providing the HiRAS dynamic spectrum. The authors acknowledge the constructive comments and suggestions of the anonymous referee, which improved the presentation and scientific content of the article.
Paper: 2017MNRAS.467.4965K (acknowledgments)
Acknowledgments We are grateful to Georg Horedt for his comments on the first version of this article. AO was supported by the Kosciuszko Foundation.
Paper: 2020A&A...636L...4M (fulltext)
companions in the disk. Huang et al. (2018) and Dullemond et al. (2018) show that the rings frequently seen in continuum emission from (sub)millimeter grains are likely caused by dust trapping, perhaps due to embedded planets carving gaps in disks as opposed to being related to snow lines. In the case of SR 21 the possibility of a low-mass companion being responsible for carving out this cavity is further supported by the small grains seen in scattered light. These are expected to be closely coupled to the gas, tracing spiral arms confined to the millimeter cavity, as is frequently seen in hydrodynamical models. Azimuthal asymmetries in the outer ring. It is hard to tell from scattered light alone whether the asymmetry of the outer ring is caused by azimuthally localized overdensities in the gas and dust or if they are the product of shadowing by the inner spiral arms. This asymmetry looks different in all three ALMA bands available: Band 9 shows an asymmetry in the south, while Bands 7 and 3 show a ring-like structure with a bright asymmetry to the north. Pinilla et al. (2015b) have already suggested that the southern bright spot seen in Band 9 may be the result of a hotter region in the southern side of the disk. This is supported by Band 9 being optically thick (peak optical depth of τ ∼2.0, assuming a physical temperature of 20 K). Therefore, continuum emission in this band is not only sensitive to density variations, but also to temperature variations, which can produce structure in the emission. In Band 3, where the disk is optically thin (peak optical depth of τ = 0.17 assuming a physical temperature of 20 K), the asymmetry is reversed, with the southern bright spot disappearing and the northern one becoming prominent. This asymmetry, at this low optical depth, suggests that the northern bright spot seen in SPHERE and ALMA Bands 7 and 3 probably corresponds to a dust overdensity at this location of the ring. Band 7, with an intermediate estimated peak optical depth of τ = 0.5, shows both the overdensity and the hot region in the south. Such an overdensity could, for example, be produced in the apastron location of a slightly eccentric disk, where the lower velocity at apastron leads to higher gas densities that can also slow down drifting grains and replicate the overdensity in large grains ( Ataiee et al. 2013 ). The spiral arms pointing to the likely position of the planet. The “kinked spiral” feature we see to the north in the saturated cut of the H -band data (Fig. 1 ) is strongly reminiscent of the spirals arising in hydrodynamical models of planet-disk interaction (see, e.g., Nelson et al. 2000 ; Sándor et al. 2007 , or Pinilla et al. 2015b ). The position of the kink in the spiral, indicating the exact location of the planet in these hydrodynamical models, is found inside of the ALMA ring and in the gap between this ring and the two brighter inner spirals seen with SPHERE (Fig. 1 , right panel). This is consistent with a
Paper: 2020MNRAS.491.5740P (acknowledgments)
ACKNOWLEDGEMENTS We used the NASA/IPAC Extragalactic Database (NED), which is operated by the Jet Propulsion Laboratory, Caltech, under contract with NASA. We acknowledge useful comments by an anonymous referee.
Paper: 2018MNRAS.476.2457C (acknowledgments)
ACKNOWLEDGEMENTS We would like to thank the anonymous referee for the suggestions that helped to improve this paper. GVC acknowledges technical support from Dr Rodrigo Romero and Estudio Conturso Asoc. This work was supported in part by the Consejo Nacional de Investigaciones Científicas y Técnicas de la República Argentina (CONICET), the Consejo Nacional de Investigaciones Científicas, Técnicas y de Creación Artística de la Universidad Nacional de San Juan (CICITCA) and the Secretara de Estado de Ciencia, Tecnología e Innovación del Gobierno de San Juan (SECITI). Funding for SDSS-III has been provided by the Alfred P. Sloan Foundation, the participating institutions, the National Science Foundation, and the US Department of Energy Office of Science. The SDSS-III web site is http://www.sdss3.org/ . SDSS-III is managed by the Astrophysical Research Consortium for the participating institutions of the SDSS-III Collaboration, including the University of Arizona, the Brazilian Participation Group, Brookhaven National Laboratory, Carnegie Mellon University, University of Florida, the French Participation Group, the German Participation Group, Harvard University, the Instituto de Astrofisica de Canarias, the Michigan State/Notre Dame/JINA Participation Group, Johns Hopkins University, Lawrence Berkeley National Laboratory, Max Planck Institute for Astrophysics, Max Planck Institute for Extraterrestrial Physics, New Mexico State University, New York University, Ohio State University, Pennsylvania State University, University of Portsmouth, Princeton University, the Spanish Participation Group, University of Tokyo, University of Utah, Vanderbilt University, University of Virginia, University of Washington and Yale University.