adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017A&A...608A..19K (fulltext)
above, that we assume the albedo distribution is not different for binaries and non-binaries. This assumption can be justified by the AD test result -value = 0.13, applied to samples from “TNOs are cool” program, which contains albedos for 108 single and 30 multiple objects. This fact indicates, that there is no occurred statistically significant difference between TNOs and TNBs albedo samples. The empirical albedo distributions are shown in Fig. 1 . Table 2 Result of the two-tailed AD test applied to diameter dataset using the whole MPC list. Notes. (a) Classification of Gladman et al. (2008) . CC denotes cold-classicals, HC denotes hot-classicals, Det. and SDO denote detached and scattered disc objects, Res. denotes resonant objects. (b) The -value is computed using the techniques developed by Scholz Stephens (1987) . and sd denote -values, calculated for the mean and mean standard deviation of the AD statistic, respectively. (c) The average number of objects. The number of objects with and/or varies, depending on Monte Carlo samples. The resulting -value for the average AD test statistic for diameter samples is summarised in Table 2 . The obtained result (-value , i.e. statistical significance >) indicates that the null hypothesis about identical diameter distributions is rejected. However, this result may be biased. To check for biases in statistical conclusions, we have repeated the AD test for different sub-samples. In a first step, the comparison is made for dynamically equivalent samples, namely using the classification of Gladman et al. ( 2008 ; see Table 2 ). The test statistic shows no significant evidence of the difference between TNBs and TNOs among detached objects and SDO (-value = 0.08). Thus, it appears that the occurred difference in diameter distributions can be caused by cold classicals, resonant objects, and possibly by cenataurs among which no binaries were observed. On the other hand, this conclusion can be not very robust from statistical point of view, since the small sizes of certain sub-samples for binary objects (e.g. less than 30 binary objects for HC, resonants, detached and SDOs) . It should be noticed, however, that the compared samples have significantly different size ranges. On the one side, the sample includes a very low binary fraction of small objects among plutinos, SDOs and centaurs. On the other side, the largest objects in the sample – dwarf planets – almost all have satellites. Consequently, the size distribution of TNBs may be biased towards the largest objects, whereas the sample of single TNOs may be biased by small objects. Thus, in a second step, we compare the size distribution over a restricted size range, namely, over the range from 140 km (the diameter of the smallest TNB 2001 RZ143 with known observed by Herschel ) to 1142 km (the largest TNO 2007 OR10 without satellites in the Herschel sample). In this case, the test indeed shows no significance about difference between two samples. This
Paper: 2016ApJ...824...13L (acknowledgments)
We thank Woong-tae Kim and Yonghwi Kim for helping us understand the Athena code. Hospitality at APCTP during the 7th Korean Astrophysics Workshop is also kindly acknowledged. The research presented here was funded by a grant from the Max-Planck Society under the cooperation agreement with the Chinese Academy of Sciences and was additionally supported by the 973 Program of China under grant no. 2014CB845700, by the National Natural Science Foundation of China under grant nos. 11333003, 11322326, and by the Strategic Priority Research Program “The Emergence of Cosmological Structures” (no. XDB09000000) of the Chinese Academy of Sciences. J.S. acknowledges support from an Newton Advanced Fellowship awarded by the Royal Society and the Newton Fund. This work made use of the facilities of the Center for High Performance Computing at Shanghai Astronomical Observatory.
Paper: 2019ApJ...870...73Y (acknowledgments)
This work made use of data from the NuSTAR mission, a project led by the California Institute of Technology, managed by the Jet Propulsion Laboratory, and funded by the National Aeronautics and Space Administration. We thank the NuSTAR Operations, Software and Calibration teams for support with the execution and analysis of these observations. This research has made use of the NuSTAR Data Analysis Software (NuSTARDAS) jointly developed by the ASI Science Data Center (ASDC, Italy) and the California Institute of Technology (USA). G.Y. acknowledges support from NASA under NuSTAR Guest Observer cycle-2, proposal number 15-NUSTAR215-0024. L.C.H. was supported by the National Key RD Program of China (2016YFA0400702) and the National Science Foundation of China (11473002, 11721303). Y.T. is supported by JSPS Grants-in-Aid for Scientific Research 15H02070 and 16K05296. F.G.X. and F.Y. are supported in part by National Key RD Program of China (grants 2016YFA0400804 and 2016YFA0400704), the Natural Science Foundation of China (grants 11873074, 11573051, 11633006, 11650110427, 11661161012, 11303008, and 11473002), and the Key Research Program of Frontier Sciences of CAS (grants QYZDJSSW-SYS008 and QYZDB-SSW-SYS033). F.G.X. is also supported by the Youth Innovation Promotion Association of CAS (ID. 2016243) and the Natural Science Foundation of Shanghai (No. 17ZR1435800). We thank the referee for a concise and thorough reading of the manuscript that led to an improved version of the article.
Paper: 2020MNRAS.493.1736R (acknowledgments)
ACKNOWLEDGEMENTS We would like to thank the anonymous referee for thoughtful suggestions that improved the clarity and flow of our manuscript. We also thank Kelvin Au for many interesting discussions and for proofreading the manuscript. AR, AM, and BP wish to acknowledge the contribution of the Brandon University Research Committee and the Canada Summer Jobs Program. XE is supported by National Science Foundation of China (NSFC) Grant No. 11473032 and No. 11873006.
Paper: 2018MNRAS.478.5473L (acknowledgments)
ACKNOWLEDGEMENTS PAAL thanks the support of CNPq, grant 308969/2014-6; and CAPES, Programa Estágio Sênior no Exterior , process number 88881.120856/2016-01. ALBR thanks for the support of CNPq, grant 311932/2017-7. FD acknowledges long-term support from CNES. TFL acknowledges financial support from CNPq (grant 303278/2015-3). MT thanks the support from the PSL Research University. This research has made use of the SAO/NASA Astrophysics Data System, the NASA/IPAC Extragalactic Database (NED) and the ESA Sky tool (sky.esa.int/). Funding for the SDSS and SDSS-II was provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, the U.S. Department of Energy, the National Aeronautics and Space Administration, the Japanese Monbukagakusho, the Max Planck Society, and the Higher Education Funding Council for England. A list of participating institutions can be obtained from the SDSS Web Site http://www.sdss.org/ . The 2dFGRS ( http://www.2dfgrs.net/ ) has been made possible by the dedicated efforts of the staff of the Anglo-Australian Observatory, both in creating the 2dF instrument and in supporting it on the telescope. We are also very thankful for the availability of the Final Release of 6dFGS ( http://www-wfau.roe.ac.uk/6dFGS/ ).
Paper: 2020MNRAS.494.5923P (fulltext)
produced synthetic NIR light curves based on GRMHD models that incorporate a model for accelerating electrons from a thermal distribution into a power-law tail. A control model, which contains only thermal electrons, produces no significant NIR emission. A simple non-thermal model that places a constant fraction of the electrons into a non-thermal (κ distribution) component is able to produce the correct spectral slope and flaring flux density but not the variability. The next simplest model is based on the idea that electrons are accelerated due to resistive dissipation in reconnecting current sheets and, therefore, sets the fraction of electrons in the non-thermal distribution proportional to J 2 , where J is the 3-current density measured in the plasma frame. This model matches the spectral slope and flaring emission but also matches the flux density distribution fairly well. A final model fares similarly in matching observations but is both better motivated physically and more computationally expensive. It advects the non-thermal electrons with the fluid, accelerates electrons into the non-thermal component at a rate proportional to J 2 , and allows them to cool using a constant cooling time model. Because these last two models agree with each other relatively well, future work will focus on the simpler one. No model shows substantial NIR centroid wander or any correlation between the NIR flux density and image centroid. These models are the first to accurately produce the observed Sgr A* mm mean flux, mm image size NIR spectral slope, flare duration, and flux distribution at the same time. Future work will adapt these models to 3D and will include Compton scattering in order to examine X-ray flares. APPENDIX A: DISSIPATION IN RELATIVISTIC MHD Here, we prove that heating rate per unit volume due to resistive dissipation (the dissipation function) is J 2 /σ if we adopt the simplest covariant model for Ohm’s law. Maxwell’s equations are Project this into the space normal to the plasma four-velocity u μ using the projection tensor and define the projected current If we then write a simple model for Ohm’s law as where σ is the conductivity, it is easy to show that this reduces to the familiar form of Ohm’s law for a scalar conductivity. To evaluate the dissipation function, observe that since then For an ideal fluid stress-energy tensor, this implies where u is internal energy and p is pressure. This is just the internal energy equation as derived from the first law assuming the dissipation function ρ T d s /dτ = ρ Tu μ s ; μ = 0. Here, the scalars s ≡ entropy and T ≡ temperature. Now consider a magnetized fluid and break the stress-energy tensor into an ideal fluid and electromagnetic piece, . Then From MTW, Misner et al. ( 1973 ), exercise 22.10 (which uses a symmetry argument and Maxwell’s equations) Combining, Then Ohm’s law implies This is positive definite because J μ is spacelike by construction and, therefore, the second law is
Paper: 2018MNRAS.480.3117L (acknowledgments)
ACKNOWLEDGEMENTS This work has been supported by the National Natural Science Fund of China under grant nos. 11603005 and 11775038. We are grateful to Y. Sang for useful discussions.
Paper: 2015ApJ...808..138L (acknowledgments)
We thank the anonymous referee for valuable comments and suggestions that helped us to improve the paper. We thank Elvijs Matrozis for his helpful discussions. This work is supported by the Alexander von Humboldt Foundation. R.J.S. is the recipient of a Sofja Kovalevskaja Award from the Alexander von Humboldt Foundation. B.W. acknowledges support from the 973 programme of China (No. 2014CB845700), the NSFC (Nos. 11322327, 11103072, 11033008, and 11390374), the Foundation of Yunnan province (No. 2013FB083).
Paper: 2017MNRAS.472.2808D (fulltext)
/yr) L and D n 4000. 8 The goal is to check how these properties vary according to the employed classification: GMM, BPT and WHAN. To that end, we portray their statistical properties as boxplots in Figs 9 11 , as well as their summary statistics in Table 3 , which shows the values for the median, first (Q1) and third (Q3) quartiles, and the interquartile range (IQR Q3Q1). Figure 9. Metallicity distributions portrayed as boxplots for GMM, BPT and WHAN diagram classes, from top to bottom. For the GMM we can see the GCs displayed in the following order: GC4, GC1, GC3, GC2 following increasing log[N ii ]/H, i.e. the x -axis of the BPT diagram. The order of the remaining groups is given by the KL distance to the GMM components. The width of boxes is proportional to the square root of the number of galaxies within each bin and the whiskers extend to the most extreme data point, which is within the 50per cent interquartile range (IQR). To better illustrate the overall distribution of the sample, for the boxplots of the BPT diagram we have omitted part of the outlier zone, zooming into the interquartile distance. Figure 10. Average stellar age distributions portrayed as boxplots for GMM, BPT and WHAN diagram classes, from top to bottom. The GCs and remaining groups are ordered as in Fig. 9 . Figure 11. D n 4000 distributions portrayed as boxplots for GMM, BPT and WHAN diagram classes, from top to bottom. The GCs and remaining groups are ordered as in Fig. 9 . Table 3. Summary statistics for the Z /Z L , log( t /yr) L and D n 4000 galaxy properties. Shown are the median, IQR and the first and third quartile for the properties of the GMM, BPT and WHAN groups. On the boxplots, the groups are vertically aligned based on their proximity in terms of the KL distance, and the fiducial order roughly follows increasing values of log[N ii ]/Hin the BPT diagram (i.e. from left to right: SF, composite, AGN). As we can see from the boxplots, by aligning these groups in this way, a positive monotonic relationship between the classes and the median values of Z /Z L , log( t /yr) L and D n 4000 distributions is revealed. The trend is a consequence of the AGN host galaxies having different characteristics from their inactive counterparts, preferentially populating the so-called green valley and red sequence of the colourmass diagram (e.g. Schawinski etal. 2010 ). Thus, as we move towards the right-hand side of the BPT diagram, one is mostly looking at early-type galaxies, that are characterized by older and more metallic stellar populations, and higher values of D n 4000 (e.g. Poggianti Barbaro 1997 ; Schawinski etal. 2010 ; De Souza etal. 2016 ). Notably, the GMM solutions automatically find groups that share meaningful physical properties, beyond the features used in the clustering algorithm. An inspection of Table 3 confirms that the statistical properties are quite similar between the GMM groups and their respective counterparts on the BPT and WHAN diagrams.
Paper: 2016A&A...595A.118V (acknowledgments)
We wish to thank the anonymous referee for carefully reading our manuscript and making a number of very useful comments. We wish to thank Eric Gérard, and Laurence Alsac and other Nançay Radio Telescope staff members for their technical advice and assistance. TJ and RM are grateful for the bursary provided by the South African SKA Project Office; S.B., T.J., R.C.K.K., M.R. and PS were supported by the South African National Research Foundation. The Nançay Radio Astronomy Station 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: 2015ApJ...805...77M (acknowledgments)
We acknowledge with thanks the variable star observations from the AAVSO International Database contributed by observers worldwide and used in this research. The authors would like to thank Alycia Weinberger for providing the published IRS spectrum of BD+20 307, and Rui Wang for the help in illustration preparation. This work is based on observations made with the Spitzer Space Telescope , which is operated by the Jet Propulsion Laboratory, California Institute of Technology under a contract with NASA. This work was partially supported by contracts 1255094 and 1256424 from Caltech/JPL to the University of Arizona, and by NASA Grant NNX10AD38G. Facilities: AAVSO , Spitzer (IRAC)
Paper: 2019A&A...621A.139T (acknowledgments)
This research was funded by the European Union’s Seventh Frame-work program under grant agreement 337595 (ERC Starting Grant, “CoSMass”). EV acknowledges funding from the DFG grant BE1837/13-1. EV also acknowledges support of the Collaborative Research Center 956, subproject A1 and C4, funded by the Deutsche Forschungsgemeinschaft (DFG). JD acknowledges financial assistance from the South African SKA Project (SKA SA; www.ska.ac.za ).
Paper: 2018MNRAS.480.4434W (fulltext)
In agreement with this trend, shows a slow first core outflow; Fig. 9 shows the radial velocity in a slice through the first core at ρ max ≈ 10 −7 g cm −3 . Although a similar ‘X’-shaped pattern is visible for all the non-ideal MHD models, the pattern in has only a narrow band of gas that is slowly outflowing ( r ≲ 0.3 km s −1 ), while the surrounding material is falling at a slightly faster rate; the gas is accreting faster along both the equatorial and polar directions than along the diagonals. Given the high angular momentum in the disc of , outflows are not required to carry angular momentum away. Figure 9. Radial velocity in a slice through the first core for each of the calculations. The white contour is r = 0. There are no first core outflows in the purely hydrodynamics model, and there are narrow, slow outflows embedded in the centre of the diagonal infall of . By contrast, as the ionization rate is decreased for the models with , the speed of the first core outflows increases and broadens. In all cases, the lower ionization rate reduces the magnetic field strength that accounts for the broadening of the outflows. Thus, at any given ζ cr , the difference in outflow speed is necessarily a result of the Hall effect. 1 In the models with , the Hall effect spins down the gas above and below the first core, which reduces the toroidal component of the magnetic field. As previously shown (e.g. Bate et al. 2014 , WBP2018 ), lower ratios of toroidal-to-poloidal magnetic field strengths result in faster outflows. Shortly after the formation of the first core, the trend of decreasing ratios of toroidal-to-poloidal magnetic field strengths corresponds to increasing outflow speeds. Vaytet et al. ( 2018 ) modelled the collapse through the first and stellar core phases using ζ cr = 10 −17 s −1 , however, their model included only ambipolar diffusion and Ohmic resistivity. They found no first core outflows, akin to our but contrary to . Their model had an initial m = 2 perturbation and an initial faster rotation. Thus, their initial conditions promoted disc formation and hence they found results similar to our , in which the Hall effect is responsible for promoting disc formation. 4.5 Counter-rotating envelopes During the first core phase, a counter-rotating envelope forms in , and at its most massive contains ∼10 −3 M ⊙ and extends to r ∼ 30 au. The counter-rotating envelope dissipates with time and disappears completely by ρ max ≈ 2 × 10 −9 g cm −3 , just before the disc forms. This envelope is smaller and less vertically extended than those found by Tsukamoto et al. ( 2015b , 2017 ). Their initially stronger magnetic field strength and faster rotation likely required the larger envelope at larger radii to conserve angular momentum. Thus, counter-rotating envelopes are likely a transient feature, with their properties dependent on the Hall effect, ionization rates (e.g. Wurster, Bate Price 2018b ), and initial conditions. 5 SUMMARY AND CONCLUSION
Paper: 2016A&A...585A..41W (fulltext)
product. We recommend not using the unofficial LAB HEALPix map any longer. 6.2. Spectral data The full spectral data will be provided for download in FITS binary table format on a HEALPix grid (nside-1024, Galactic coordinates are used for indexing). Because the EBHIS Milky Way spectral data set has a size of about 50 GByte, we split it into smaller chunks, namely into the 192 HEALPix pixels of the nside-4 tessellation. Because EBHIS is only available for declinations above , not all of the nside-4 pixels contain data, and these are therefore omitted. Additionally, smaller spectral data cubes of size are available for download in FITS image format. 7. Summary We presented the first data release of the Effelsberg-Bonn H i Survey (EBHIS), elaborated on the recent changes to the data processing software and techniques compared to Winkel et al. (2010) , and studied the quality of the resulting data products in great detail. EBHIS is a 21-cm H i line survey of the sky north of , comprising full spatial sampling and high sensitivity. At full spectral resolution of , EBHIS has an average noise level of . Its angular resolution is . About 1000 h of observing time with the Effelsberg 100-m telescope were spent until the completion of the first coverage that forms the basis for the first data release. Owing to the large bandwidth of 100 MHz, not only is the Milky Way emission covered in EBHIS, but also H i in the Local Volume out to a redshift of . The presented data products comprise the Milky Way emission. The analysis of the extragalactic part of the survey will be published elsewhere. EBHIS was designed to replace the northern-hemisphere portion of the important Leiden/Argentine/Bonn Survey (LAB; Kalberla et al. 2005 ), which has about a factor of 4 lower angular resolution and severely suffers from the insufficient spatial sampling (beam-by-beam only; compare Kerp et al. 2011 , e.g., their Fig. 7). Great effort went into developing new data reduction techniques, including but not limited to software radio-frequency-interference mitigation, a frequency-dependent flux-density calibration scheme, and robust baseline estimation in the 2D time-frequency domain. Furthermore, the very successful stray-radiation prediction algorithms, which were also employed for the LAB and Galactic All-Sky Survey (GASS; McClure-Griffiths et al. 2009 ; Kalberla et al. 2010 ; Kalberla Haud 2015 ), have been adapted to the Effelsberg telescope. We carefully assessed the data quality of the EBHIS spectra and inferred column densities. Our investigation revealed a mismatch in intensity calibration of GASS with respect to EBHIS and LAB, which led to a revised calibration scale in the third data release of GASS ( Kalberla Haud 2015 ). EBHIS and LAB show a near-perfect one-to-one relation in column densities and brightness temperatures. From a comparison of EBHIS and LAB, and from the overlaps of the EBHIS fields themselves, we estimated the uncertainties of the EBHIS column