adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020ApJ...888...88L (fulltext)
in the diffuse ISM do not significantly alter as galaxies grow, while the dust in H ii regions and/or the surrounding dense clouds is more sensitive to the galaxy evolution. Since can be connected to dust mass surface density and also gas mass surface density (e.g., Boquien et al. 2013 ; Brinchmann et al. 2013 ; Kreckel et al. 2013 , 2016 ), one would speculate that the observed –SFR relation might stem from the more basic Kennicutt–Schmidt star formation law (Schmidt 1959 ; Kennicutt 1998a ) and leads to the – M * and –12+log(O/H) relations via the SFMS and MZR, respectively. We believe that the Kennicutt–Schmidt law can partly account for the –SFR relation; however, it cannot fully explain the observed trends along the two scaling relations due to the following reasons. First, Table 1 shows that both M * and gas-phase metallicity more strongly correlate with compared to SFR. If the –SFR relation is more fundamental and connects with M * and gas-phase metallicity via the SFMS and MZR, respectively, we would expect a stronger correlation with SFR compared to M * and gas-phase metallicity. Second, comparing with , displays stronger correlations with M * and SFR and a comparable correlation with gas-phase metallicity, implying that although exhibits very weak correlations with these physical properties, its role is still nonnegligible. Third, our partial correlation analysis discussed below suggests that when SFR is controlled, M * still moderately correlates with , and these three parameters seem to play comparable roles in the correlation. Koyama et al. ( 2019 ) speculated that the decrease of with increasing SFR at the low-SFR side originates from the relation between M * and because of the SFMS. Similarly, the correlation between and gas-phase metallicity revealed by Figure 11 also might result from the M * – relation due to the tight MZR. To test these conjectures, a partial correlation analysis is performed utilizing the R ppcor package 12 12 https://cran.r-project.org/web/packages/ppcor/ (Kim 2015 ). We calculate the Pearson correlation coefficients r and Spearman rank correlation coefficients ρ to see how correlates with M * , SFR, and 12+log(O/H)[N2], respectively, when the other one or two physical properties are controlled. The results are presented in Table 2 , in which the first four columns show the cases of controlling only one parameter and the last three columns are the cases of controlling two parameters simultaneously. When only one parameter is controlled, M * gives the strongest correlation with , regardless of the controlled parameters and calculation methods. Comparing with the coefficients listed in Table 1 , it is evident that all three parameters can partly explain the corresponding correlations, while M * seems to have a dominant effect. In the cases of controlling two physical properties at the same time, the effect of M * becomes much smaller compared to SFR and metallicity when considering linear correlation.
Paper: 2017A&A...604A.103P (acknowledgments)
The authors would like to thank Prof. A. Gould for consultation. The OGLE project has received funding from the National Science Centre, Poland, grant MAESTRO 2014/14/A/ST9/00121 to A.U. OGLE Team acknowledges Profs. M. Kubiak and G. Pietrzyński, former members of the team, for their contribution to the collection of the OGLE photometric data over the past years. J.P.B. and J.B.M. gratefully acknowledge support from ESO’s DGDF 2014. C.C. was supported by an appointment to the NASA Postdoctoral Program at NASA Ames Research Center, administered by Universities Space Research Association under contract with NASA.
Paper: 2016A&A...591A..61C (acknowledgments)
We acknowledge insights and assistance with data provided by Drs. Philip Best, Annalisa Bonafede, George Contopoulos, Federica Govoni, Christian Kaiser, and Preeti Kharb. We especially thank Annalisa Bonafede and Federica Govoni for presenting us with the calibrated data for A2142A and 5C4.152. We also acknowledge the assistance of Antonios Nathanail in the preparation of Fig. 2 . This work was supported by the General Secretariat for Research and Technology of Greece, the Irish Research Council (IRC) and the European Social Fund in the framework of Action Excellence. We thank the referee whose comments have led to an expansion of the paper that helped improve the clarity and significance of our results.
Paper: 2016ApJ...832..154B (acknowledgments)
S.B., D.R.G.S., and R.B. are thankful for funding through the DFG priority program “The Physics of the Interstellar Medium” (projects BO 4113/1-2, SCHL 1964/1-2, and BA 3706/3-2). T.G. acknowledges the Centre for Star and Planet Formation funded by the Danish National Research Foundation. D.R.G.S. is thankful for funding through Fondecyt regular (project code 1161247) and through the “Concurso Proyectos Internacionales de Investigación, Convocatoria 2015” (project code PII20150171). D.R.G.S. would also like to thank Basel PFB-06 CATA for funding. S.B. also acknowledges the kind hospitality of the Kavli Institute for Theoretical Physics (KITP), where this work has been completed, and Andrea Ferrara, Troels Haugbølle, and Raffaella Schneider, for fruitful discussions on this topic. We are indebted to Hiroyuki Hirashita for the derivation of the equations reported in the Appendix. This research was supported in part by the National Science Foundation under Grant No. NSF PHY11-25915. The plots of this paper have been obtained by using the YT tool (Turk et al. 2011 ). The simulations have been performed on the German HLRN cluster under the project nip00035.
Paper: 2019ApJ...874...59S (fulltext)
previous snapshot. The second criterion ensures that we exclude galaxies that experienced a merger within the past 150 Myr. 4. Synthetic Observations of the Simulations: Images and Spectra To generate synthetic observations, each simulation snapshot is post-processed with the dust radiative transfer software SUNRISE (Jonsson 2006 ; Jonsson Primack 2010 ; Jonsson et al. 2010 ). Synthetic images and spectra are created from the SUNRISE output. The synthetic Hubble Space Telescope ( HST ) images used in this paper are available as a high-level science product from MAST 12 12 https://archive.stsci.edu/prepds/vela : https://doi.org/10.17909/t9-ge0b-jm58 . 4.1. Dust Radiative Transfer with <monospace>SUNRISE</monospace> SUNRISE contains two primary steps. It first generates a spectral energy distribution (SED) for each source of radiation in the simulation. It then propagates the associated polychromatic rays through the 3D gas grid of the simulation, taking into account dust absorption and scattering, toward a user-defined camera(s). For each snapshot, the ART output is first loaded into the YT analysis software (Turk et al. 2011 ) and converted into the appropriate SUNRISE format. This is carried out using an updated version of the pipeline used in Moody et al. ( 2014 ). An SED is assigned to each star particle based on its age, mass, and metallicity using STARBURST99 (Leitherer et al. 1999 ) assuming a Kroupa ( 2001 ) initial mass function. Emission lines are generated from young star particles, less than 10 Myr old, using a starburst model from the photoionization and shock modeling code MAPPINGS III (Dopita et al. 2005 ; Groves et al. 2008 ). This model includes a central massive young stellar cluster whose SED is set with STARBURST99 , a surrounding H ii region, and a photodissociation region with a covering fraction of 0.2 (Groves et al. 2008 ). To account for 10 4 K thermal broadening, we convolve the original MAPPINGS III H α emission line with a 10 km s −1 1D Gaussian kernel. The dust density is assumed proportional to the gas metal mass using a dust-to-metals mass ratio of 0.4 (Dwek 1998 ). Consequently, the dust geometry is resolved to a typical gas cell size of 17–35 pc. We adopt the dust grain size distribution from Weingartner Draine ( 2001 ) and Draine Li ( 2007 ) and set the slope of the dust extinction law, R v , to the Milky Way value of 3.1 (e.g., Gordon et al. 2003 ). SUNRISE cameras are placed at 19 positions around the galaxy. The camera parameters are user-selected and not defaulted to SUNRISE. Of the cameras, six are random but remain fixed in comoving space over time, eight are random but change from timestep to timestep, and the remaining five are fixed with respect to the net angular momentum of the galaxy and, as such, change from timestep to timestep. Those fixed to the galaxy include one that is face-on, i.e., along the gas angular momentum axis; one that is reverse face-on; two that are edge-on; and one that is
Paper: 2016ApJ...828...87W (fulltext)
multiple power sources. Furthermore, the presence of H α emission lines in the late-time spectrum and the rebrightening in the late-time LC show an unambiguous signature for interactions between the SN ejecta and a hydrogen-rich CSM. Hence, any model with a single power source or double power sources is challenged in explaining the LC of iPTF13ehe. A triple-power-source model should be seriously considered. In this paper, we propose a triple-energy-source model for iPTF13ehe and try to understand its explosion mechanism and LC. This paper is organized as follows. In Sections 2 and 3 , we fit the early-time and late-time LC using double and triple-energy-source models, respectively. In Sections 4 and 5 , we discuss and conclude our results. 2. MODELING THE EARLY-TIME LC We start by studying the early LC and determining the power sources of iPTF13ehe. As pointed out by Yan et al. ( 2015 ), of 56 Ni were synthesized while of 56 Ni are required to account for the LC peak. Therefore, there should be other energy sources to aid the high peak luminosity of iPTF13ehe. The early-time spectra shows no H emission lines, indicating that the ejecta-CSM interaction can be neglected in the early-time luminosity evolution. Hence, the energy source aiding the SN might be a magnetar spin-down input. The possibility that iPTF13ehe is a CCSN and can be aided by a magnetar was also pointed out by Yan et al. ( 2015 ), though they did not provide further analysis. Considering the contribution from 56 Ni, which cannot be neglected, the most reasonable model should contain contributions from both 56 Ni and a magnetar. Wang et al. ( 2015b ) have constructed such a unified model for SNe Ic and explained three luminous SNe Ic. We employ this model here to fit the LC of iPTF13ehe. Supposing that the ratio of the r -band flux to bolometric flux remains nearly constant around the peak and in the late-time, we scale the r -band luminosities to obtain the bolometric luminosities. The scaled bolometric luminosities are rough, but they can be regarded as an approximation of real values. These data are plotted in Figure 1 . In this section, we use some models to fit the early-time LC. The photospheric velocity and the optical opacity κ are fixed to ∼13,000 km s −1 (Yan et al. 2015 ) and 0.2 cm −2 g −1 , respectively. Figure 1. Modeling iPTF13ehe’s early-time LC using 56 Ni, magnetar, and hybrid ( 56 Ni + magnetar) models. Data are obtained from Yan et al. ( 2015 ). The early-time and late-time bolometric luminosities scaled from r -band ones are shown as the orange filled circles and orange unfilled circles, respectively. The dashed–dotted line is the LC powered by 19 of 56 Ni; the dashed line is the LC powered by a magnetar; the solid line is the LC powered by 56 Ni and a magnetar. The short dashed line is the LC powered by 2.5 of 56 Ni with full trapping of gamma-rays. The other model parameter values are shown in the text. 2.1. <sup>56</sup>Ni Model The first of the models
Paper: 2018ApJ...865..145R (fulltext)
analyses of the strongly depleted element Ca have demonstrated that dust commonly reaches out far beyond the stellar bodies of galaxies deep into circumgalactic and intergalactic space (e.g., Richter et al. 2011 ; Zhu Ménard 2013 ). In this broader context, additional systematic studies of metal depletion in the MW halo and in other circumgalactic environments would be very useful to better understand the large-scale circulation of dust in and around galaxies. 5.3. Origin of LA II To further evaluate whether LA II originally stems from the SMC or LMC let us first collect the main results from our absorption-line study and summarize the main conclusions from these findings. 1. LA II exhibits an α abundance and N/ α ratio that closely match those of the current-day SMC, possibly implying that the gas was recently removed from the SMC and pushed deep into the MW halo. If so, the age of LA II can be at most a few hundred Myr. If we consider only the chemical abundance pattern as age indicator, LA II could be older if the gas originates in the LMC. In that case, the observed abundance pattern in LA II would reflect that of the LMC 1–2 Gyr ago (Pagel Tautvaisiene 1998 ; Harris Zaritsky 2004 ). That the LMC had exactly the same α /N abundance pattern as the SMC today at the time when the gas was removed from the MCs is not unrealistic given the star formation history of the LMC (Pagel Tautvaisiene 1998 ), but it would be a coincidence. 2. LA II exhibits a very high thermal gas pressure of P / k ≈ 1680 K cm −3 in the molecular gas phase, suggesting that the gas is situated in the inner halo of the MW ≈10 kpc above the disk, where the gas is compressed by ram-pressure forces. LA II and the MCs then would be separated by at least 30 kpc in space, suggesting that it would have taken the gas more than 200 Myr to reach its current position after it was separated from its host galaxy (assuming a relative speed of 100 km s −1 ). This sets a minimum age of ∼200 Myr for the LA II. Yet a distance a small as 10 kpc for the LA cannot be reproduced by the most recent hydrodynamical simulations of the Magellanic System (Pardy et al. 2018 ) in a first infall scenario, where the minimum distance of the LA is found to be d ≈ 24 kpc. 3. The H 2 formation timescale in the cold gas in LA II is ∼640 Myr. If all of the H 2 had formed in situ, this sets a lower limit for the age of the LA II. However, given the fact that so much dust has survived the separation process, it is equally likely that also the H 2 (or at least part of it) had formed earlier in the LA II source galaxy. If so, the H 2 was transported together with the neutral gas into the MW halo, where the molecular gas fraction adjusted to the local conditions on much shorter timescales. 4. The very high dust-to-gas ratio in LA II suggests that the gas originally stems from an interstellar region with a neutral+molecular gas column as high as log N (H) = 21.2. Such high column densities are typical for the inner
Paper: 2019A&A...628A..86B (fulltext)
on redshift is smaller than the dependence on the dynamical state. There seems to be a slight although not very significant evolution of the sparsity with z . Higher-redshift ( z > 0.54) clusters have a slightly larger value of S 500/2500 by 18%, that is these profiles are less concentrated, which is not consistent with the low-redshift ( z 0.2) clusters at ∼1.8 σ . We found the same behaviour when excluding the outliers. There is a much stronger and significant variation of the sparsity with dynamical state: morphologically disturbed (high ⟨ w ⟩), non-CC (low C SB ), and disturbed (low M ) clusters have larger sparsity. For all parameters considered, the sparsity of the first and third bins is not consistent at more than 3 σ , and the difference between the sparsities of these bins is of the order of ∼50%. At the same time, the intrinsic scatter increases significantly, reaching ∼0.2 dex for the most disturbed or least concentrated objects. For example, the sparsity increases by 64% at a significance level of 3.4 σ between M −0.6 and M > 0.5, that is, between the one third most relaxed and the one third most disturbed as defined by this parameter. Only an upper limit on the intrinsic scatter can be estimated from the former ( 0.04 dex), while the intrinsic scatter for the latter reaches 0.25 ± 0.09 dex. There are strong outliers at high sparsity. Excluding the outliers yields the same qualitative results, although the variation between bins is weaker. This suggests a sparsity distribution skewed towards high values, with a skewness increasing with departure from dynamical relaxation. 7. Discussion 7.1. Dynamical state The first result of our study is that SZ-selected samples are dominated at all redshifts by disturbed and non-CC objects. Recent observational work on local clusters has converged to similar results (e.g. Lovisari et al. 2017 ; Rossetti et al. 2017 ; Andrade-Santos et al. 2017 ; Lopes et al. 2018 ). In particular, these works highlight the higher fraction of disturbed objects or the lower fraction of CC objects in SZ-selected samples as compared to X-ray-selected samples, a fact interpreted to be due to preferential detection of relaxed or more concentrated clusters in X-ray surveys. In contrast, there is little consensus in the literature concerning the evolution of the dynamical state, as determined from various morphological parameters. Studies of the evolution up to z ∼ 1 of the centroid shift and/or power ratios of X-ray-selected clusters indicate a larger fraction of disturbed clusters at high z ( Maughan et al. 2008 ; Jeltema et al. 2005 ; Weißmann et al. 2013 ). However, the latter study is also consistent with no evolution, and Nurgaliev et al. (2017) did not find any significant evolution in the redshift range 0.3 z 1 for the 400 d X-ray-selected clusters. These somewhat contradictory results may simply be due to selection effects: X-ray detectability is clearly not independent of cluster morphology. More peaked
Paper: 2019ApJ...886..117L (fulltext)
which the excitation temperature can be found naturally depends on the number of usable lines. Typically, the excitation temperature could be constrained to better than ±150 K. Fortunately, the isotopic ratio is not highly sensitive to the excitation temperature, and uncertainties in the ±150 range have only a small effect on the resulting isotopic ratios. To reduce the uncertainty in the abundances in the case of ill-defined excitation temperatures, we investigated the relation between the excitation temperature, T exc , and the effective temperature, T eff . There is considerable basis for assuming that these quantities are related via the atmospheric structure and elemental abundances. Effective temperature is a measure of the total energy integrated over all wavelengths radiated from a unit of stellar surface area. The value is fixed by luminosity and radius and is an observable quantity. T eff is given by the relationwhere F is the bolometric flux received at Earth, σ is the Stefan–Boltzmann constant, and φ is the angular diameter (Ridgway et al. 1980 ). Most values of T eff for cool giants have been set using Equation ( 1 ) with φ determined by lunar occultations and interferometry (see, e.g., Ridgway et al. 1980 ; Richichi et al. 1999 ) and F determined from near-IR photometry. Observational limits associated with the techniques restrict the sample of objects in magnitude, diameter, and position on the sky. A second approach based on infrared flux (Blackwell Shallis 1977 ; Blackwell et al. 1990 ) also produces effective temperatures. Both techniques produce similar results. We limit our discussion to the angular diameter approach. Effective temperature is known to vary with spectral type, for the M giants nearly linearly (Richichi et al. 1999 ). Worthey Lee ( 2011 ) present a comparison of T eff versus V – K relations for cool giants. The Bessell et al. ( 1998 ) T eff versus V – K relation, derived from a compilation of occultation and interferometry diameters, is the best fit to the Worthey Lee ( 2011 ) data. Sharma et al. ( 2016 ) review a number of fits to the T eff versus M giant spectral type relation against several groups of synthetic spectra. A T eff –( V – K ) relation derived from a homogeneous set of angular diameters by Richichi et al. ( 1999 ) gives the best match and extends to the coolest non-Mira M giants. Therefore, we adopted the spectral type—effective temperature relation by Richichi et al. ( 1999 ) for our study. However, use of the Bessell et al. ( 1998 ) calibration would result in negligible change. All the calibrations show scatter around a mean relation. The Richichi et al. ( 1999 ) relation shows considerable scatter to lower temperatures. Since the sample stars are bright—in fact most (Table 1 ) are in the Yale Bright Star Catalog (Hoffleit 1964 )—there are well-determined spectral types and magnitudes. We ignore the galactic reddening of these nearby objects. Table 2 lists the parameters for the program
Paper: 2019ApJ...870L..12P (fulltext)
quasar proximity zone (e.g., Eilers et al. 2017 ) allow an estimate of the intrinsic luminosity of the quasar. However, it is crucial to note that the maximum image separation for J0439+1634 is ∼02 (Fan et al. 2018 ), close to the highest-resolution limit obtainable with the Hubble Space Telescope ( HST ; see Figure 2 ). Therefore, it is a clear possibility that many of the detected z > 6 quasars are actually lensed, with image separations below the resolution threshold. Following Barkana Loeb ( 2000 ), in Figure 2 we show the distribution in angular separations of lensing images for z = 6.51, , where is the angular diameter distance between source and observer. The distribution indicates that a fraction of the lensed sources at z > 6 have image separations ≲01. Thus far, no multiple images have been detected with the HST in samples of ∼10 z > 6 quasars (e.g., Kim et al. 2009 ; McGreer et al. 2013 ). This is equivalent to stating that the fraction of μ > 2 magnified quasars at z > 6 is ≲10% in current samples, leading to an upper limit of β ≲ 3.1 (see Figure 1 ). It is worth noting that, while the diffraction limit for the HST is ∼01, its point-spread function (PSF) is very well characterized and stable. This would, in principle, allow to discern a point-source quasar from a multiply imaged one well below the diffraction limit, possibly at a level ∼0001. For example, Libralato et al. ( 2018 ) measured proper motions of stars by identifying the centroid of the PSF with a precision ∼00003. This remark could foster a re-examination of the HST images of z > 6 quasars thus far detected. Figure 2. Distribution of image separations at z = 6.51, obtained assuming a Press–Schechter distribution of galaxies, following Barkana Loeb ( 2000 ). The shaded area indicates the 1 σ uncertainty due to the distribution of velocity dispersion in galaxies σ v . The angular separation for J0439+1634 (∼02), the HST angular resolution (∼01), and the typical angular size of lens galaxies at z ∼ 1 (∼1′′) are also shown for reference. Additionally, as originally suggested by Fan et al. ( 2018 ), the current selection criteria might have missed an important fraction of the quasar population at z > 6 because a lens galaxy in the same line of sight might have contaminated the drop-out band of the spectrum, leading to a misclassification of the source. It is remarkable to note that, if we de-magnified J0439+1634 by a factor ∼5, reducing its total magnification to μ ∼ 10, its flux would have been comparable to the one from the foreground lens galaxy and, thus, the quasar would have been misclassified. This situation could be occurring systematically for z > 6 quasars with μ ≲ 10. As the typical image separation for strongly lensed z > 6 quasars is ≪1′′ and the typical angular dimension of a z ∼ 1 lens galaxy is ∼1′′ (see Figure 2 ), the probability of lensing is equivalent to the probability of having the lens galaxy and the quasar images within the same photometric aperture.