adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018MNRAS.478.2896M (acknowledgments)
ACKNOWLEDGEMENTS The authors are supported by the project grant 2014.0017 ‘IMPACT’ from the Knut and Alice Wallenberg Foundation. AJ was supported by the European Research Council under ERC Consolidator Grant agreement 724687-PLANETESYS, the Swedish Research Council (grant 2014-5775), and the Knut and Alice Wallenberg Foundation (grants 2012.0150, 2014.0017, and 2014.0048). The simulations were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at Lunarc. This research has made use of the Exoplanet Orbit Database and the Exoplanet Data Explorer at exoplanets.org . We thank the referee, John Chambers, for a careful and insightful referee report.
Paper: 2016MNRAS.463.2690D (acknowledgments)
This paper is based on observations collected at the European Southern Observatory Very Large Telescope, Cerro Paranal, Chile – Programs 166.A-0106, 079.B-0469, 185.A-0745, 092.A-0170. We are indebted to an anonymous referee for helpful comments that clarified several points in the paper. VD is grateful to Gwen Rudie for sharing and adapting her results on the KBSS for Fig. 9 . MV is supported by the ERC Starting Grant ‘cosmoIGM’ and PD51 INDARK grant. TSK acknowledges funding support from the ERC Starting Grant ‘cosmoIGM’, through grant GA-257670. MH was supported by the ERC Advanced Grant 320596 ‘The Emergence of Structure during the epoch of Reionisation’. PB is supported by the INAF PRIN-2014 grant ‘Windy black holes combing galaxy evolution’. Parts of this research were conducted by the Australian Research Council Centre of Excellence for All-sky Astrophysics ( CAASTRO ), through project number CE110001020 .
Paper: 2019A&A...631A.121D (acknowledgments)
Acknowledgements We thank G. Dashyan, M. Lemoine, C. Pfrommer, R. Teyssier, and A. Wagner for enlightening discussions. We warmly thank S. Rouberol for smoothly running the Horizon cluster on which several simulations were run. This work was supported by the ANR grant LYRICS (ANR-16-CE31-0011) and the CNRS programs “Programme National de Cosmologie et Galaxies” (PNCG) and “Physique et Chimie du Milieu Interstellaire” (PCMI).
Paper: 2018ApJ...855....9L (fulltext)
might be candidates of prestellar cores or simply overdensities of molecular gas flows. 4.2. Gas Temperatures in Dense Cores We estimated gas temperatures in dense cores using the VLA (1, 1) and (2, 2) lines. The temperatures were then used to calculate core masses below. Line widths, column densities, and optical depths were derived at the same time. We extracted the mean (1, 1) and (2, 2) spectra within the FWHM of the 2D Gaussian fit of each core and fit them with a customized code pyAmor . 9 9 https://github.com/xinglunju/pyAmor We assumed local thermodynamic equilibrium (LTE) conditions and the same filling factor for all hyperfine components. The free parameters in the fit are the core velocity , the rotational temperature , the column density N (), the velocity dispersion , and the filling factor. The core velocity was first selected by eye and then varied within ±0.62 (one channel) of the selected value. Model spectra were constructed with these parameters. The nonlinear least-squares minimization of the difference between the model spectra and the observed spectrum was implemented with the lmfit package (Newville et al. 2014 ), and then the parameters of the best-fit model spectrum were taken to be the best-fit results. The optical depth at the line center of the main hyperfine component of (1, 1), , was derived afterward using the best-fit , N (), and . An example of the fitting is shown in Figure 6 . Figure 6. Fitting results of gas temperatures in I18308-c1 using the VLA (1, 1) and (2, 2) lines. The red curve shows the best-fit model, with the best-fit parameters displayed in the panel. Toward several dense cores, multiple velocity components in lines were found. Then we fit two independent models, each of which was constructed with the parameters listed in the previous paragraph. Two sets of best-fit parameters were derived, from which we chose the one that has similar to dense gas tracers in cores (e.g., H 2 CO, CH 3 OH) to calculate dense core properties. 4.3. Masses, Densities, NH<sub>3</sub> Abundances, Virial States The masses of the dense cores were derived using the following equation:where is the gas-to-dust mass ratio, S ν is the dust emission flux, d is the distance, is the Planck function at temperature , and is the dust opacity. We assumed and cm −2 g −1 (MRN model with thin ice mantles, after 10 5 yr of coagulation at 10 6 ; Ossenkopf Henning 1994 ). The kinematic distances were derived using the rotation curve model in Reid et al. ( 2009 ; see Lu et al. 2014 , for details). The near distance was taken in case of kinematic distance ambiguity. Under the assumption of LTE, the dust temperatures were taken to be the gas temperatures derived from , , in Section 4.2 . We did not use dust temperatures derived from Herschel data because their angular resolution is not sufficient to resolve the dense cores, nor did we use gas temperatures based on the SMA H 2 CO or CH 3 CN lines because they usually only probe the inner,
Paper: 2020MNRAS.496L.142D (fulltext)
cent confidence limits, respectively, analogous to 1σ and 2σ random experimental error bars. 2.1 The misinterpretation of the mass-error correlation The next step taken by K20 is to take the upper error bars on each MC trial’s measured M hi (i.e. the upper error bars on the data points in their fig. 3, or our Fig. 1 ), and plot them as a function of ( M hi − M hi,in ) (shown in their fig. 5). K20 name this latter quantity ‘overestimate of M hi ’, but which is equivalent to ‘ M hi /M − 18’, i.e. the difference between the output and input values of M hi . K20 observed that these two quantities are correlated, and interpreted this correlation as each randomly scattered data point being aware of how far it is from the centroid of the input distribution, and that this information is communicated back to the observer through the data point’s error bar. Continuing with this line of reasoning, K20 then claim that it is possible to transpose the results of each individual MC trial back to the input value of M hi,in with a precision of ±0.8 M. Finally, K20 then treats the analysis of the real-world sample of 24 progenitor mass estimates as though it were a single MC trial, and concludes that the best fit of M hi M should be adjusted down to M hi = 15.8 ± 0.8 M. This reduction of M hi , as well as its upper error bar, implies that the difference between M hi and the mass associated with the H–D limit is now in excess of 10σ, leading K20 to state that ‘the RSG problem remains’. In Figs 2 and 3 , we use the results of our MC experiment to make similar figures to fig. 5 in K20 , again seeing a correlation. However, in Fig. 2 , we make the same plot but for three different values of M hi,in . Were the individual data points aware of of the true input value of M hi,in , the three sets of data would be offset in the y -direction, since a different correction factor would be required for each different value of M hi,in . However, the exact same trend is seen irrespective of M hi,in . Hence, we can say that the trend seen in fig. 5 in K20 has no dependence on M hi,in . Figure 2. Our version of fig. 5 in K20 , showing the quantity ( M hi − 18 M) for each of our MC trials, as a function of the positive error bar on M hi for that trial. In addition to showing the results for the same input value as K20 ( M hi,in = 18 M), we also show the results for M hi,in = 20 and 22 M. The exact same trend is seen irrespective of M hi,in , demonstrating that the correlation seen in the plot does not depend on the input value of M hi . Figure 3. Same as our Fig. 2 , but for fixed M hi,in = 18 M, and with three different sizes of error bars on the input progenitor masses (σ M = 10, 20, and 30 per cent). Different trends are seen for each value of σ M , demonstrating that the correlation seen in fig. 5 of K20 is in fact caused by simple error propagation. Next, in Fig. 3 , we again recreate fig. 5 of K20 , but for MC experiments with fixed M hi,in = 18 M and three different values
Paper: 2017MNRAS.469S.357K (acknowledgments)
Acknowledgements OSIRIS was built by a consortium of the Max-Planck-Institut für Sonnensystemforschung, in Göttingen, Germany, CISAS-University of Padova, Italy, the Laboratoire d’Astrophysique de Marseille, France, the Instituto de Astrofisica de Andalucia, CSIC, Granada, Spain, the Research and Scientific Support Department of the European Space Agency, Noordwijk, The Netherlands, the Instituto Nacional de Tecnica Aeroespacial, Madrid, Spain, the Universidad Politechnica de Madrid, Spain, the Department of Physics and Astronomy of Uppsala University, Sweden, and the Institut für Datentechnik und Kommunikationsnetze der Technischen Universität Braunschweig, Germany. The support of the national funding agencies of Germany (DLR), France (CNES), Italy (ASI), Spain (MEC), Sweden (SNSB) and the ESA Technical Directorate is gratefully acknowledged. This research project also received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 686709. In memoriam of Mike F. A’Hearn. Much of Rosetta ’s research has been motivated by the results of the Deep Impact and EPOXI missions that were under Mike's leadership. We lost an outstanding colleague and dear friend.
Paper: 2021ApJ...910..112K (fulltext)
is the linear radius of the half-power beam width of the KVN telescope at the distances of two VeLLOs. On the other hand, there are other targets whose infall velocities are smaller than the freefall velocity at the distances comparable to those of these two sources. This indicates that the collapsing radius of the core may not be uniquely determined but variable depending on the physical status of the core. Any further quantitative discussion in comparison of our observations with results predicted by the various models dealing with gas infall motions is beyond the scope of this paper, and we leave this issue for future study. 4.4. Mass Infall Rates and Mass Accretion Rates toward the VeLLOs, and Their Implication In this section, we consider the mass infall rates from the envelopes of the VeLLOs and the mass accretion rates toward the VeLLOs as useful physical quantities to help us understand the physical processes involved in the formation of the VeLLOs from their parent envelopes. The mass infall rates were derived using the infall speeds obtained from our model fitting, with , where ρ 0 is the mean density of the envelope given by and R in is the half of FWHM (16′′) of our observing KVN telescope. It is assumed that the envelope has a uniform density ρ and the infall is spherically symmetric with a speed of V infall . Estimated mass infall rates range from 10 −7 to 10 −5 M ⊙ yr −1 , with a median value of (3.4 ± 1.5) × 10 −6 M ⊙ yr −1 (Table 3 ). The values of κ can be a significant contributor to the uncertainties in the mass and then the mass infall rate, which may vary within a factor of 2 owing to the uncertainty in the value of κ (Ossenkopf Henning 1994 ). We note that line profiles showing an infall asymmetry can be affected by an outflow activity in their shapes if it is strong enough, and thus the infall speed and the mass infall rate that we derived can be affected. We found that 13 out of 15 sources for which infall speeds are derived from their infall asymmetric profiles have possible outflow wings from the CO observations by Kim et al. ( 2019 ), but they are found to be all weak enough not to significantly affect the derived values of infall speed and the mass infall rate. The mass accretion rate can be obtained by using CO observations around the outflow regions. Kim et al. ( 2019 ) made a single-pointing and/or a mapping survey of 68 VeLLOs in CO lines, finding the evidence for outflows over 16 dense cores having the VeLLOs. We found that there are five outflow sources identified from the CO mapping survey for which the mass infall rates were estimated in this study. For these sources, we used the mass accretion rates given by Kim et al. ( 2019 ). We also found that there are eight more outflow sources identified from the single-pointing CO observations for which the mass infall rates were estimated in this study. For these sources we calculated the mass accretion rates using the equation given by Kim et al. ( 2019 ) with
Paper: 2016ApJ...820...55X (acknowledgments)
We thank Shoya Kamiaka and Kento Masuda for useful discussions. We are also grateful to an anonymous referee for several important suggestions that improved our earlier manuscript. This research is supported by the Grant-in Aid for Scientific Research by Japan Society of Promotion of Science No. 24340035.
Paper: 2016MNRAS.459.3861C (fulltext)
of the star formation rate (SFR) density of the universe with time and stellar mass (e.g. Brinchmann et al. 2004 ; Bauer et al. 2005 ; Feulner et al. 2005 ; Noeske et al. 2007 ; Zheng et al. 2007 ; Chen et al. 2009 ). The Hα, [O ii ] , ultraviolet–optical spectral energy distribution fitting and infrared photometry are commonly used to estimate the SFR at different redshifts. Two major conclusions of these studies are as follows: (1) the comoving SFR density of the universe has declined by an order of magnitude since z ∼ 2 (Hopkins et al. 2006 ); (2) the SFR per unit stellar mass (SSFR) depends on both stellar mass and redshift, with massive galaxies forming most of their stars earlier than less massive systems (Heavens et al. 2004 ; Thomas et al. 2005 ). To fully understand galaxy formation and evolution, it is important to understand both how the population as a whole evolves and how SFR within individual galaxies evolves. A first step is looking at how SFR is distributed spatially in galaxies as a function of their physical parameters, such as stellar mass and environment. However, this is not an easy task, especially for high- z galaxies given their small angular size and faintness. The radial distribution of star formation in local galaxies has been analysed in individual galaxies (e.g. NGC 1566; Comte Duquennoy 1982 ) and samples of tens to hundreds of galaxies using narrow-band imaging centred on Hα which traces the ionizing photons produced by massive stars. Ryder Dopita ( 1994 ) studied the relative scalelengths of Hα, V -, and I -band emission in 34 S0-Sm galaxies, finding that the line emission had a larger scalelength than the continuum. James, Bretherton Knapen ( 2009 ) compared the Hα and R -band light profiles of 313 S0a-Im field galaxies, finding that the major central Hα deficit (relative to the continuum) happens in barred galaxies, particularly in early-type and hence high-mass barred galaxies (see also Nair Abraham 2010 ; Hakobyan et al. 2014 ). Statistical studies of larger volumes have so far been lacking due to the decrease in spatial resolution with distance, and the necessity of using multiple narrow-band filters to accommodate the redshifting of the Hα line. Sloan Digital Sky Survey (SDSS) provides the largest galaxy sample of photometry and spectra so far. It is a great data base to study all kinds of properties of local galaxies. Ge et al. ( 2012 ) select double-peaked narrow emission-line galaxy sample from SDSS DR7 and study their properties. However, due to the 3 arcsec diameter fibre, it is hard to derive any spatial information about the galaxies directly from the spectra. In this paper, we make the first attempt of getting spatial information from the structures of Hα emission lines in SDSS spectra. As we know, the Hα profile depends on the strength of emission at a given velocity, which is determined by the rotating curve and the Hα surface brightness distribution in a rotating disc. Thus, through comparing
Paper: 2020ApJS..250...28H (fulltext)
a significant challenge. The initial magnetic and thermodynamic configuration (i.e., densities, pressures, and temperatures imposed on the system) is critical to the initial force balance of the system, particularly in the layers at and directly above the model photosphere. Unless this initial state is constructed in a physical way, where the pressure gradients act to balance the forces from magnetic pressures and stresses acting to push apart concentrations of field, nonphysical flows and dynamics will eventually dominate any driving forces imposed at the photosphere. This is a particular problem with the zero- β approach because the only possible restorative forces in these regions are due to Reynolds and viscous stresses. On the Sun, the observed evolution of the magnetic field in the photosphere and low atmosphere results from a complex interaction of fields and flows in a gravitationally stratified, turbulent environment. But there is insufficient observational information available to adequately specify the dynamic and thermodynamic initial state of the system. In standard ab initio models of magnetic flux emergence or magnetoconvection, one is not faced with this difficulty. Typically, field-free thermodynamic states are developed in a physical way through a dynamic and energetic relaxation process, and stratification in density, pressure, and temperature naturally results from the presence of gravity and the application of physical boundary conditions. Once this relaxation procedure is complete, only then is magnetic field introduced into the system. In the case of data driving, we are presented with the opposite scenario. We are given an initial magnetic field and an electric field to be applied to the model’s photospheric boundary, with little to no information regarding the hydrodynamic state of the plasma in the low atmosphere where such information is necessary to drive the dynamics of the system in a physical way. So how best to proceed? We find that there are two ways to address this initialization problem: the first is to simply scale the fields and place the lower boundary of the simulation in the upper transition region or corona, thereby placing the driving layer in a field-filled, magnetically dominated regime. The second is to keep the driving layer in the photosphere, generate an initial thermodynamic stratification, and artificially limit nonphysical runaway flows when necessary to mitigate dynamics unrelated to the driving forces of interest. We choose not to pursue the first approach, because we find that photospheric electric fields bear little resemblance to coronal electric fields once magnetic structures have expanded into the low-density, low- β corona. This amounts to ignoring the data in the photosphere altogether in favor of a different, more idealized problem. The second approach involves generating initial thermodynamic states and flow fields by a dynamic relaxation process that holds the initial magnetic
Paper: 2020ApJ...890L..29A (fulltext)
more warm absorption or reflection modeling. We then fit all the spectra with an absorbed power-law model. The required additional absorption at a redshift of the source in Chandra spectra modeling is moderate with a fitted absorption of . The value of the inferred absorption in Suzaku is consistent with that within uncertainties (Table 2 ). The Chandra spectra are harder with a photon index of , compared to the value of in Suzaku . The two Swift spectra are even harder than that of Chandra , while the value of the photon index cannot be well constrained due to low S/N and degeneracy with absorption. Thus in the fitting of Swift spectra we tied all the parameters to those of Chandra except the normalization. The fitted parameters are shown in Table 2 , and the spectra with the fitted power-law model are shown in Figure 3 . In Figure 3 we also show the joint contours of the photon index Γ and column density N H . It is clear that the changes of photon index between Suzaku and Chandra are significant at 99% confidence. Figure 3. Unfolded X-ray spectra of SDSS J155258+273728 with absorbed power-law model fits for Suzaku and Chandra observations. Inset: contours give the joint 68%, 90%, 99% confidence for the two interesting variables, photon index Γ, and the intrinsic absorption N H . Table 2 Parameters of X-Ray Spectra Fits a Notes. a Unabsorbed flux in 2–10 keV. b The values are fixed in the fitting. There is more than a factor of 2 brightening in Chandra observation with an unabsorbed 2–10 keV flux of erg s −1 cm −2 , compared to those in Suzaku and Swift -2014 with values of erg s −1 cm −2 and erg s −1 cm −2 , respectively. The inferred flux of Swift -2018 is erg s −1 cm −2 , indicating that the X-ray emission of SDSS J155258+273728 fades back to the level of Swift -2014. 3.1. IR–Optical–X-Ray Photometry and Variability In Figure 4 we show the X-ray and broad H α line variations of SDSS J155258+273728 on a decade. The optical and mid-IR photometry from CRTS and the Wide-field Infrared Survey Explorer survey are also shown. It is clear that accompanied by the significant enhancement of broad H α emission, SDSS J155258+273728 presents increased emission in hard X-ray, optical V -band, and mid-IR. The variation timescale of this broadband brightening is ∼4 yr, in which the X-ray varied more than a factor of 2, and CRTS V -band and mid-IR W 1, W 2 bands varied with amplitudes of 0.18 ± 0.12, 0.32 ± 0.02, and 0.43 ± 0.03, respectively. Figure 4. Light curves showing the multiwavelength changes observed in SDSS J155258+273728 in the optical V -band data from CRTS (top panel), mid-IR W 1 and W 2 bands from WISE (middle panel), and hard X-ray flux along with broad H α emission (bottom panel). In the top and bottom panels, we also present the median values of each season epoch. The dotted–dashed lines mark the start/end of the significant variation timescale. 4. Discussion 4.1. Reddening Measurement from Optical Spectra The detected moderate