adsabs/WIESP2022-NER huggingface dataset:
Paper: 2021MNRAS.505.2009G (fulltext)
were measured with an accuracy of (Table 4 ). Moreover, we were unable to measure [O iii ]λ4959 and [O iii ]λ5007 lines for these objects. These two regions were also excluded from further analysis. 3.2 Oxygen and nitrogen abundances We are unable to use the direct T e -method to estimate the oxygen and nitrogen abundances, since auroral lines in spectra of H ii regions from our sample, such as [O iii ]λ4363 or [N ii ]λ5755, are too faint to be detected. Thus, we need to use empirical calibrations. Such methods are well developed (Kobulnicky Kewley 2004 ; Pettini Pagel 2004 ; Pilyugin Thuan 2005 ; Bresolin 2007 ; Pilyugin et al. 2010 ; Pilyugin Mattsson 2011 ; Marino et al. 2013 ; Pilyugin Grebel 2016 ). The most modern two-dimensional R and S-calibrations (Pilyugin Grebel 2016 ) are the simplest and the most popular now. The R-calibration, based on the [O ii ] and [N ii ] strong emission lines, is more resistant to the presence of diffuse ionized gas (see e.g. Sanders et al. 2017 ; Kumari et al. 2019 ; Poetrodjojo et al. 2019 ), but it is sensitive to extinction errors. The S-method, based on the [N ii ] and [S ii ] lines, does not depend strongly on extinction, but it cannot be used for the nitrogen abundance estimates (Pilyugin Grebel 2016 ). Among the other methods we distinguish the NS-calibration (Pilyugin Mattsson 2011 ), which is based on the [O iii ], [N ii ], and [S ii ] emission-line intensities (see discussion in Gusev et al. 2012 , 2014 ). All the empirical calibrations are constructed under some assumptions. One of them is that the H ii regions are in the low-density regime (Pilyugin Mattsson 2011 ; Pilyugin Grebel 2016 ), which is typical for the majority of extragalactic H ii regions (Zaritsky et al. 1994 ; Bresolin et al. 2005 ; Gutiérrez Beckman 2010 ). The dependence n e versus [S ii ]λ6717/[S ii ]λ6731 degenerates at low n e (Proxauf et al. 2014 ), thus we give upper limits of n e in Table 3 for most H ii regions. Large upper limits of n e for H ii region nos. 12, 15, and 16 in NGC 3963 are rather a result of large line flux errors. However, one H ii region of our sample, no. 6 in NGC 3963, is an apparently high-density object (see Table 3 ). The empirical calibrations are not applicable to such regions. As a result, this region was eliminated from further consideration. We calculated the oxygen and nitrogen abundances in H ii regions using three different calibrations discussed above. Data of emission lines fluxes from Table 4 were used. For several regions, where the [O iii ]λ4959 or [N ii ]λ6548 lines fluxes have not been measured, we adopt I ([O iii ]λ4959+[O iii ]λ5007) = 1.33 I ([O iii ]λ5007) and I ([N ii ]λ6548+[N ii ]λ6584) = 1.33 I ([N ii ]λ6584) according to results of Storey Zeippen ( 2000 ). The resultant oxygen abundances from the R, NS, and S-calibrations abundances, and nitrogen abundances from the R and NS-calibrations, and NS-calibration electron temperatures are given in Table 5 . Table 5. Oxygen and
Paper: 2019ApJ...885..137D (acknowledgments)
The authors thank Javier Coronado-Blázquez, Jose Luis Contreras, and the anonymous referee for helpful comments. A.D. is thankful for the support of the Ramón y Cajal program from the Spanish MINECO. R.W. was supported by a grant from VILLUM FONDEN (Project No. 16599). J.F. is supported by NASA under contract S-15633Y. M.A. and A.D. acknowledge funding support from NSF through grant AST-1715256. K.H. acknowledges support from the Icelandic Research Fund, grant No. 173728-051. D.H. and L.M. acknowledge funding support from NASA through grant 80NSSC18K1720.
Paper: 2019MNRAS.488.2175B (fulltext)
galaxies (Fig. 11 , upper right panel). Due to the relatively small range in redshift across the sample, this difference is not likely due to evolution. In the case of inner (r) and outer (R) rings, a small dependency is found (a p -value of 5.46 per cent). Both galaxies with outer and inner rings in the isolated sample are on average found at slightly larger distances than those without these features. We repeated the tests above to look for bias due to galaxy inclination (Fig. 12 ), but in all cases we found no apparent dependency, consistent with what we illustrated in Figs 6 – 8 . However, whether or not a spiral galaxy is identified as either grand design or flocculent is clearly dependent on both distance and inclination. This is illustrated in the top two panels of Fig. 16 . The sample grand design spirals seem to be more easily identified at lower inclinations and on average are more distant than the sample flocculent spirals. The dependence on inclination may not be unexpected because spiral arm contrast is lower the more inclined the disc galaxy. The dependence on distance could reflect the fact that grand design spirals tend to occur in more luminous systems than do flocculent spirals, and hence can be recognized to greater distances (Section 7). 8.2 Morphological features and isolation Using the same approach as above, we analysed whether the distributions of two environmental parameters: η k and Q Kar, p (Verley et al. 2007b ), were altered by restricting to subsamples of specific morphological features. η k is the logarithm of the local volume density of neighbouring galaxies, based on the projected separation to the k th neighbour and the distance estimate of the galaxy in question (Verley et al. 2007b ). Only neighbours that have diameters that are 0.25–4 times that of the target galaxy are considered, as larger (smaller) galaxies are assumed to be foreground (background) objects. However, this does leave open the possibility that some low-mass dwarf companions might be neglected. Typically k = 5, but in some cases the available optical field is too small to identify five neighbours and a lower value of k is used instead. Q Kar, p is a logarithmic and dimensionless measurement of the ratio of a galaxy’s internal gravitational binding force to external tidal forces, which assumes that galaxy diameter is a proxy for total mass (Verley et al. 2007b ). For CIG galaxies the mean values of η k and Q Kar, p are 1.4 and −2.7, and their standard deviations are 0.6 and 0.7, respectively. Figs 13 and 14 show the comparative histograms for stage, family, and inner and outer varieties versus η k and Q Kar, p ; the comparisons for AC are shown in the middle two panels of Fig. 16 . We find no dependency on the isolation parameters regarding the detection of inner or outer rings and bars, which could either be a sign of the minimal impact of the environment on the generation of these features, or could be indicating that the scatter in
Paper: 2018ApJ...854L..24U (fulltext)
2006 ). The observations were carried out over five nights in 2017 March, in clear weather or thin clouds, with seeing 06–10. We used a pixel scale of 025, the 210 grating in H -band and a slit width of 10, yielding a spectral resolution of R ∼ 3000. We adopted a two-point dithering pattern and an exposure time of 5 minutes per frame, for a total on-source time of 21 hr (summed from both spectrographs). To facilitate acquisition, we used a multi-object mask and chose PA = 20° to align the slit to the major axis of the galaxy (Section 2.1 ). The data were reduced using the flame pipeline (Belli et al. 2017 ), which outputs a rectified, sky-subtracted, wavelength-calibrated two-dimensional (2D) spectrum. The corresponding H α PV diagram is shown in Figure 1 (right, color scale). 2.4. One-dimensional Kinematic Profiles To create the one-dimensional (1D) velocity and dispersion profiles in CO, we proceed as described by Genzel et al. ( 2017 ): we first fit a Gaussian profile to the CO line emission in each spaxel of the data cube, smoothed over three spaxels to ensure sufficient signal-to-noise ratio (S/N) in the outer parts of the galaxy. Accounting for the galaxy’s systemic velocity, this results in the 2D velocity map. From this we determine PA kin = 23° as the axis with the steepest velocity gradient. It agrees with PA H , and with the PA of the H α slit observations (Sections 2.1 , 2.3 ). The CO 1D velocity and dispersion profiles are then constructed from 075 diameter apertures (as a compromise between the CO data resolution and the seeing-limited H α data) with the center spaced by 024 along PA kin . To create the 1D profiles in H α , we extract spectra in overlapping bins of two to four spatial pixels and fit a Gaussian profile to the H α line emission. The choice of the number of spatial pixels used for the extraction of individual data points does not substantially affect the extracted values, but allows for increased S/N in the outer disk regions. We trace H α out to 19 kpc (NE, ∼4.4 R e , H ) and 13 kpc (SW, ∼3.1 R e , H ), and CO out to 12 kpc (∼2.8 R e , H ). These physical radii at z ∼ 1.4 are equivalent to probing the rotation curve out to 23–35 kpc for a galaxy of this stellar mass at z ∼ 0 (van der Wel et al. 2014 ). Figure 2 shows the 1D velocity and dispersion profiles in CO and H α along PA kin in observed space. The uncertainties are derived from the Gaussian fits described above where noise has been taken into account. The two tracers agree, indicating that they trace the same mass distribution, most reliably in the outer disk where beam-smearing effects become less important. Figure 2. One-dimensional velocity (left) and velocity dispersion (right) profiles along the kinematic major axis in CO(3–2) (blue circles) and H α (red diamonds). Due to the spatial resolution of the observations, neighboring data points are not independent. The projected distance increases from NE to SW of the kinematic center of the galaxy. The
Paper: 2015A&A...584A.108S (acknowledgments)
We thank the anonymous referee whose comments helped improve this manuscript. We thank Endrik Krügel for helpful discussions and for providing the original version of his MC code, and Frank Heymann for discussions on the implementation of perspective-projection ray tracing. We are grateful to Sebastian Wolf for discussions, comments and suggestions which improved the content of this manuscript. PS is supported under DFG programme No. WO 857/10-1
Paper: 2015ApJ...798...66D (acknowledgments)
We are thankful to the anonymous referee(s) for an insightful report that improved the paper, and in particular for advocating more conservative assumptions about the completeness of the Kepler pipeline. R.I.D. gratefully acknowledges the National Science Foundation Graduate Research Fellowship under grant DGE-1144152 and the Miller Institute for Basic Research in Science, University of California, Berkeley. J.A.J. is grateful for the generous grant support provided by the Alfred P. Sloan and David Lucile Packard foundations. We are grateful to Smadar Naoz for many enlightening discussions and comments, including opening our eyes to other possibilities in Section 4 , for which we also thank Simon Albrecht and Fred Rasio. Many thanks to Adrian Barker, Rick Greenberg, Renu Malhotra, Francesca Valsecchi, and especially Brad Hansen for tidal insights; to Katherine Deck, Will Farr, Vicky Kalogera, Yoram Lithwick, and Matthew Payne for helpful dynamical discussions; to Will Farr and Moritz Günther for helpful statistical discussions; to Courtney Dressing and Francois Fressin for Kepler assistance; and to Joel Hartmann for helpful discussions about the ground-based sample. We are grateful to Subo Dong for constructive comments on a manuscript draft. Thanks to Joshua Carter, Boas Katz, Doug Lin, Geoff Marcy, Darin Ragozzine, Kevin Schlaufman, and Aristotle Socrates for helpful comments, and to Thomas Barclay, Christophe Burke, Jon Jenkins, and Jason Rowe for helpful discussions of Kepler 's completeness for giant planets. We benefitted from helpful conversations with Thomas Barclay about candidate vetting and Howard Isaacson and Rea Kolb about false positives. We are very grateful to Chelsea Huang for helpful discussions and for providing us with a detrended light curve for KIC 6805414. R.I.D. thanks David Charbonneau, Sean Andrews, Debra Fischer, Matt Holman, and Abraham Loeb for helpful comments on the thesis chapter version of this manuscript. Special thanks to J. Zachary Gazak for helpful modifications to the TAP code. We thank Dáithí Stone for making his library of IDL routines available. This paper includes data collected by the Kepler mission. Funding for the Kepler mission is provided by the NASA Science Mission directorate. We are grateful to Kepler team for their work in making this revolutionizing mission possible and making the rich Kepler data set available. Some of the data presented in this paper were obtained from the Multimission Archive at the Space Telescope Science Institute (MAST). STScI is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS5-26555. Support for MAST for non-HST data is provided by the NASA Office of Space Science via grant NNX09AF08G and by other grants and contracts. This research has made use of the Exoplanet Orbit Database, and the NASA Exoplanet Archive, which is operated by the California Institute of Technology, under contract with the National Aeronautics and Space Administration under the Exoplanet Exploration Program.
Paper: 2018MNRAS.477.5536Z (acknowledgments)
ACKNOWLEDGEMENTS We thank John O’Meara, Scott Burles, David Tytler, and Chris Gelino for their feedback about HIRES data used by BT ; Tristan Dwyer for running his code identifying absorption systems for us; Serj Balashev and Michael Wilczynska for useful discussions; an anonymous referee for valuable comments. EOZ was supported by an Australian Government Research Training Program (RTP) Scholarship. Work of SRS was funded by the Australian Research Council Centre of Excellence for All-sky Astrophysics (CAASTRO), through project number CE110001020. EOZ is grateful for the hospitality of the Institute of Theoretical Astrophysics at the University of Oslo during his visit in 2016 January–February. JKW thanks the Centre for Mathematical Sciences in Cambridge for an enjoyable and productive Sabbatical visit. This work is based on observations collected with the Keck Observatory Archive (KOA), which is operated by the W. M. Keck Observatory and the NASA Exoplanet Science Institute (NExScI), under contract with the National Aeronautics and Space Administration. A significant part of the analysis and figures were done using publicly available python packages: matplotlib (Hunter 2007 ), numpy (Van Der Walt, Colbert Varoquaux 2011 ), scipy (Oliphant 2007 ), astropy (Astropy Collaboration 2013 ), barak 11 by Neil Crighton, and qscan (Dumont 2017 ). This research has made use of NASA’s Astrophysics Data System. EOZ is also grateful to GitHub for the Student Developer Pack.
Paper: 2020MNRAS.492..223C (fulltext)
0.9 R ⊙ , see Section 2 ). The sound speed at the outer edge of the disc is usually in the regime dominated by the bound-free opacities and gas pressure. The mid-plane temperature could be estimated from the standard disc structure, as, e.g. in equation 5.49 of Frank et al. ( 2002 ), which yields T d ∼ 10 4 K. However, the disc in Cyg X-3 is embedded in the strong blackbody radiation of the donor, with T * ≈ 10 5 K. Thus, T d ∼ T * is likely. The sound speed is where Γ ≈ 5/3 is the adiabatic index, μ is the mean molecular weight, which is within μ ≈ 4/3 for fully ionized He and 4 for neutral He (which is most likely the main element of the WR star in Cyg X-3), and m p is the proton mass. At 10 5 K, He will be predominantly fully ionized, which yields H / R d ≈ 0.03. The field advection time-scale in the presence of magnetic outflows, equation ( 9 ), then becomes On the other hand, the standard viscous time-scale (i.e. in the absence of outflows), equation ( 10 ), is We see that the latter time-scale estimate is of the order of the average ∼50-d radio lag observed in Cyg X-3 . It may also be consistent with it for lower values of R d / R L and some plausible combinations of the parameters. Equation ( 6 ) estimates the value of the minimum strength of the magnetic field at R d required for the appearance of magnetic outflows. Using our estimates of the parameters of Cyg X-3 , it can be written as Current constraints give the average soft-state bolometric flux of erg cm −2 s −1 , as we discuss in Section 2 . Likely values of are then in the range of ≈100–200 G. Note that the dependence on the uncertain BH mass is weak. 3.3 The nature of the hard state In the low/hard state, a geometrically thick and hot inner accretion flow surrounded by an outer thin disc is present (e.g. see Yuan Narayan 2014 and the references therein). The hot accretion flow may be advection dominated (hereafter abbreviated as ADAF) though the degree of the flow advection at high Eddington ratios is uncertain. Also, the flow is likely to contain some condensed cold clumps. Still, for notational simplicity, we will use the term ADAF hereafter, but without implying a specific physics of the flow. Variations of the mass accretion rate trigger then state transitions. A hard-to-soft state transition occurs when the accretion rate increases above the critical value, . Then the ADAF cools efficiently and collapses to a thin accretion disc, which is accompanied by disappearance of jets. Steady jets are universally observed in the hard state of BH XRBs, which implies the presence of strong vertical/poloidal magnetic fields near the BH. The origin of such strong fields is unclear. If the ADAF extended all the way to the outer edge of the disc in this state, any outer field could be efficiently advected on the viscous time scale (Cao 2011 ). Equation ( 10 ) then yields ≈0.3 d for an ADAF extending to R d for typical values of H / R d = 0.5 and α = 0.1. In principle, this could also
Paper: 2017ApJ...839..102H (fulltext)
is whether or not the GCS from NGC 1275 itself is contributing to the NGC 1272 totals particularly in ACS-3. From the NGC 1275 profile (see comments below and the lower panel of Figure 8 ), it appears that the contribution from NGC 1275 at the kpc projected distance of NGC 1272 will be at the level of only a few percent. If and when more complete imaging coverage of the core Perseus region is available, it will be possible to solve for the spatial distributions of both galaxies simultaneously and more accurately (see e.g., Wehner et al. 2008 , for an example in the Hydra cluster). For the central giant NGC 1275, the available material is much more limited and the conclusions about total GC population correspondingly rougher. We use field ACS-1 along with ACS-6 to set the local background, though here we are forced to assume that is roughly constant over the region concerned. The measurements are shown in the lower panel of Figure 8 , replotted from Figure 6 . The combination of this radial profile and the inner data from Penny et al. ( 2012 ) indicates that from outward, and is relatively flat inside 60′′. The main concerns in estimating its total population are the true local background level, and how far outward the NGC 1275 halo extends before fading into the ICM; the present data do not answer either of these satisfactorily. Here we simply use an outer radius (100 kpc) for integration of the profile and arcsec −2 as above. The result is then GCs for , not including any (unknown) systematic error in the adopted background. Extrapolating over all magnitudes as described above gives N GC = 13,500 ± 2000 and a specific frequency , including ±0.1 mag uncertainties in the GCLF turnover and dispersion as above. This total GC population and S N would make NGC 1275 quite similar to M87 in Virgo (e.g., Harris 2009b ). We emphasize once again that a better assessment will require a wider-field survey in which the GC populations in the member galaxies, and the IGCs, can both be determined more completely. Nevertheless, this very limited imaging material is enough to suggest that both of the dominant Perseus galaxies appear to have very populous GC systems. The closest analogy may be the more well known Coma cluster, where both NGC 4874 and 4889 have similarly large GC populations but where the central cD (NGC 4874) has the higher specific frequency. 5. Discussion: The IGC Population The data discussed in this paper consist of only 11 pencil-beam probes of the GC population, and thus fall far short of a genuine wide-field survey of the Perseus cluster. Only a more comprehensive survey to similar depth will be able to address the true spatial distribution of the IGCs and their total number. Nevertheless, we can make a very rough attempt to gauge the total GC population. We assume, following Figures 6 and 8 , that σ (IGC) is approximately constant at arcsec −2 in the inner part of the cluster and then falls off as further out. The inflection point
Paper: 2018ApJ...867..155L (acknowledgments)
The author thanks Rubab Khan, Jamie Lomax, Philip Massey, Kathryn Neugent, Bertrand Plez, and Sarah Tuttle for using discussions regarding this work, as well as the anonymous referee for their helpful and constructive comments. This research was supported by a fellowship from the Alfred P. Sloan Foundation.
Paper: 2020MNRAS.498.2472K (fulltext)
In this paper, we have used the E-MOSAICS simulations to quantify the stellar masses and accretion redshifts of five satellite galaxies that have been accreted by the Milky Way, as well as several other of their properties. These satellites are Kraken (previously referred to as the progenitor of the ‘low-energy GCs’ by Massari et al. 2019 ), Gaia -Enceladus, the progenitor of the Helmi streams, Sequoia, and Sagittarius. They likely represent the most massive objects that the Milky Way accreted since z = 4. We predict their properties by training an artificial neural network on the E-MOSAICS simulations, which follow the co-formation and co-evolution of galaxies and their GC populations. This network relates the ages, metallicities, and orbital properties of a group of GCs that formed in a common satellite progenitor to the properties of that progenitor. The conclusions of this work are as follows: The neural network is capable of predicting the stellar masses and accretion redshifts of the satellite progenitors to high precision, with a validation score of and a scatter of and around the true values, respectively. Omitting the GC age–metallicity information leads to considerably worse constraints on both quantities. Additionally, the accretion redshifts strongly rely on the GC orbital information (Fig. 1 ). Modulo a small number of changes, we adopt the proposed GC membership of the satellite progenitors from Massari et al. ( 2019 , see our Table 1 ). The five groups of GCs associated with the different satellite progenitors show clear differences in apocentre–eccentricity space and age–metallicity space. By comparing the orbital properties of GCs to those of fossil streams from Bonaca Kruijssen ( 2020 ), we find that the streams have similar orbital properties as the GCs associated with Kraken, Gaia -Enceladus, the progenitor of the Helmi streams, and Sagittarius. The Fimbulthul stream falls in between the orbital properties spanned by the GCs associated with Kraken and Gaia -Enceladus, suggesting that it may be the relic of a GC that formed in either of these two satellites. In age–metallicity space, the Kraken GCs have the highest metallicities at a given age, closely followed by the Gaia -Enceladus GCs, suggesting that these two galaxies were the most massive at any moment in time, with Kraken having a slightly higher mass (Figs 2 and 3 ). By applying the neural network to the Galactic GCs associated with the five satellite progenitors, we find that the satellites span a relatively narrow stellar mass range at the time of accretion, of M ⋆ = (0.6–4.6) × 10 8 M ⊙ . The top end of this range is occupied by Kraken, Gaia -Enceladus, and Sagittarius, with the latter two being the most massive. This differs from the stellar masses at any given moment in time, because the accretion redshifts of these satellites differ. By accreting later, Sagittarius was able to attain a higher mass, despite initially being considerably less massive than