adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017A&A...597A..67A (fulltext)
region (see panel a of Fig. 8 ). This chemical interface induces a bump in at (panel a of Fig. 9 ). This interface also affects the shape of the Lamb frequency. In the case of white dwarfs with carbon-oxygen cores, the internal chemical structure is substantially more complex than in the case of the helium-core white dwarf models (see panels b, c, and d in Fig. 8 ). This results in a much more complex form of the run , as can be seen in panels b, c, and d of Fig. 9 . Indeed, in these cases, the Brunt-Väisälä frequency is characterized by additional maxima induced by the presence of the C/O-He and C/O chemical interfaces. Fig. 9 Propagation diagrams, the spatial run of the logarithm of the squared Brunt-Väisälä and Lamb frequencies (Unno et al. 1989 ), corresponding to the white dwarfs models shown in Fig. 8 for . The number and shape of the chemical interfaces present in the interior of DA white dwarf models strongly affect the propagation properties of nonradial pulsation modes, in particular through mode trapping and confinement ( Brassard et al. 1992 ; Bradley 1996 ; Córsico et al. 2002 ). Mode trapping or confinement results in a strong departure from uniformity of the forward period separation, (), when plotted in terms of the pulsation period , where is the radial order of the mode. Thus, the period difference between an observed mode and adjacent modes can be considered as an observational diagnostic of mode trapping. For a helium-core white dwarf, which is characterized by a single chemical interface such as that of the model of mass considered here, local minima in usually correspond to modes trapped in the hydrogen envelope; this is in contrast to local maxima in , which are associated with modes trapped in the inner core. The forward period spacing for modes in terms of the periods corresponding to the helium-core model of mass is depicted in panel a of Fig. 10 . We also show the asymptotic period spacing, computed as in Tassoul et al. (1990) , with a dotted blue line. Mode-trapping signatures are clearly noticeable, particularly for periods shorter than 1500 s. Longer periods seem to fit the asymptotic predictions, although small departures from constant period spacing are still clearly seen. In the case of white dwarfs with carbon-oxygen cores, the presence of multiple chemical transition regions causes much more complex patterns of mode-trapping/confinement, as can be seen in panels b, c, and d of Fig. 10 . In particular, strong departures from uniform period separation are evident for the models with , , and . These features persist for the entire range of periods analyzed here. Also, we note some “beating” modulating the amplitudes of the departures of . This beating is due to the combined mode-trapping and confinement effects caused by the various steps in the C/O profile in the core; see Córsico Althaus (2006) for the case of PG1159 star models. Clearly, the mode trapping and confinement features of models with C/O
Paper: 2018MNRAS.476.3974P (fulltext)
Fig. 7 ). Figure 7. The surface density profile (top panel) and the K -band luminosity function (bottom panel) of early- and late-type stars in the Galactic Centre. The points mark the stellar number counts in distance and magnitude bins, as indicated by the horizontal error bars, and the vertical error bars indicate Poisson standard errors of the bin counts. For model fitting, the unbinned values are used (see Section 4.2 ). We can reproduce and confirm the results of Buchholz et al. ( 2009 ) and other studies of the spatial distribution of stars in the Galactic Centre (e.g. Paumard et al. 2006 ; Bartko et al. 2009 ; Do et al. 2009 , 2013 ; Lu et al. 2009 ). Following Do et al. ( 2013 , appendix E), we determine the respective surface density profiles by means of Bayesian inference, using a power-law profile as a model (Σ ∝ R α ). This approach does not require binning the star counts, and allows taking into account the estimated class membership probabilities as weights. For the late-type stars, we find a rather flat density profile (α = −0.33 ± 0.05), which appears essentially core-like towards the very centre. This still presents a puzzle, because these stars are old enough to be relaxed and would be expected to form a significantly steeper Bahcall Wolf ( 1976 ) cusp in that case (−0.75 α −0.5, but see Gallego-Cano et al. 2018 ; Schödel et al. 2018 ). For the early-type stars, we find a broken power-law profile with a turnover radius at about 10 arcsec, which we interpret as the outer edge of the young stellar disc (see Støstad et al. 2015 ). Within that radius, the surface density profile has a well-defined index of α = −0.83 ± 0.10, or α = −1.00 ± 0.15 if we exclude the central S-star cluster (at R 1 arcsec). Beyond that radius, the number of early-type stars drops rapidly. We can also reproduce the results of previous studies focused on the stellar K -band luminosity function (KLF). For the late-type stars with K 14.5, i.e. if we exclude red clump stars, we find a KLF well-described by a relation N ∝ 10 β K with an index of β = 0.36 ± 0.02, which is similar to that of the Galactic bulge (e.g. Genzel et al. 2003 ; Buchholz et al. 2009 ). The KLF of the early-type stars appears to be top-heavy in the disc region (1 arcsec ≲ R ≲ 10 arcsec), as also found by Paumard et al. ( 2006 ), Buchholz et al. ( 2009 ), and Bartko et al. ( 2010 ). The question of whether the KLF of these stars is indeed flat or somewhat steeper at the faint end, as found by Do et al. ( 2013 ), can likely be answered conclusively only on the basis of spectroscopic observations with a high completeness for B-stars over the entire region, which is currently only reached by deep imaging observations (e.g. ). 5 CONCLUSIONS For a proof of concept, we have constructed a machine-trained, random forest classifier to identify early- and late-type stars in the environment of the Galactic Centre black hole, based on intermediate-band photometry using seven filters in the
Paper: 2018ApJ...860..126C (acknowledgments)
We thank Tetsu Kitayama and Daichi Suto for providing us with simulated data points that are presented in Figure 8 . We thank the anonymous referee for providing constructive suggestions that lead to the improvement of this paper. K.U. acknowledges support from the Ministry of Science and Technology of Taiwan (grants MoST 103-2112-M-001-030-MY3 and MoST 106-2628-M-001-003-MY3) and from the Academia Sinica Investigator Award. M.S. acknowledges financial support from the contracts ASI-INAF I/009/10/0, NARO15 ASI-INAF I/037/12/0, ASI 2015-046-R.0, and ASI-INAF n.2017-14-H.0. J.S. was supported by NSF/AST-1617022. M.M., M.S., S.E., J.S. acknowledge support from the Italian Ministry of Foreign Affairs and International Cooperation, Directorate General for Country Promotion (Project “Crack the lens”). This work was made possible by the availability of high-quality lensing data produced by the CLASH survey. This paper made use of the code colossus (Diemer 2017 ) and the packages from Bocquet and Carter ( 2016 ) and Hinton ( 2016 ) for plotting. This work made use of the IPython package (Pérez Granger 2007 ), SciPy (Jones et al. 2001 ), TOPCAT, an interactive graphical viewer and editor for tabular data (Taylor 2005 ), matplotlib, a Python library for publication quality graphics (Hunter 2007 ), Astropy, a community-developed core Python package for Astronomy (Astropy Collaboration et al. 2013 ), NumPy (Van Der Walt et al. 2011 ).
Paper: 2019MNRAS.483.1470R (fulltext)
(host) galaxies are typically early-type (E/S0) galaxies, poor in gas (Whitmore, McElroy Schweizer 1987 ; Finkelman, Funes Brosch 2012 ; Reshetnikov Combes 2015 ). In contrast, the polar structures are generally younger, blue (e.g. Reshetnikov, Hagen-Thorn Yakovleva 1994 ; Iodice et al. 2002 ), with large amount (several times 10 9 M ⊙ ) of HI (e.g. van Gorkom, Schechter Kristian 1987 ; van Driel et al. 2000 ). Various scenarios have been proposed to explain the formation of the two kinematically and morphologically decoupled structures in PRGs: the capture of matter from an approached galaxy (Reshetnikov Sotnikova 1997 ; Bournaud Combes 2003 ), the merging of galaxies (Bekki 1997 , 1998 ; Bournaud Combes 2003 ), the misaligned accretion of matter from cosmic filaments (e.g. Maccio, Moore Stadel 2006 ; Brook et al. 2008 ), and some others. Observations show that several formation mechanisms can apparently be realized. Galaxies with polar structures are an important class of objects that allows us to study a wide range of problems, linked with the formation and evolution of galaxies under external accretion, and to study the properties of their dark haloes. Unfortunately, PRGs are very rare objects, with a fraction of ∼10 −3 of all galaxies in the local Universe (Whitmore et al. 1990 ; Reshetnikov, Faúndez-Abans de Oliveira-Abans 2011 ). Consequently, most recent studies of PRGs are concentrated mainly on a few relatively nearby and bright galaxies: NGC 4650A (e.g. Coccato, Iodice Arnaboldi 2014 , Iodice et al. 2015 ), A0136-0801 (Spavone, Iodice Arnaboldi 2015 ), NGC 660 (Smirnova, Wiebe Moiseev 2017 ), and some others. There were several attempts to collect large samples of such galaxies. Whitmore et al. ( 1990 ) have presented a photographic atlas of PRGs and related objects containing 157 galaxies but, according to further studies, only a small part of the atlas galaxies appeared to be true PRGs. The next important step was the catalogue SPRC by Moiseev et al. ( 2011 ) based on the results from the Galaxy Zoo project 1 (Lintott et al. 2008 ). The SPRC catalogue contains 275 objects split into four groups. The first two groups comprise ‘best candidates’ (70 objects) and ‘good candidates’ (115 objects) for PRGs. The remaining objects are related galaxies (mergers, strong warps) and possible face-on polar rings. A further study showed that an appreciable fraction of the SPRC objects are indeed PRGs (fig. 1 in Moiseev et al. 2015 gives several examples). In this work we present a new sample of ‘best’ and ‘good’ (in the sense of SPRC) candidates to PRGs. Our sample includes 31 galaxies compiled from Galaxy Zoo discussion boards. Throughout this article, we adopt a standard flat ΛCDM cosmology with Ω m = 0.3, Ω Λ = 0.7, H 0 = 70 km s −1 Mpc −1 . All magnitudes in the paper are given in the AB-system. 2 THE SAMPLE AND DATA REDUCTION 2.1 The sample To select new candidates to PRGs, we analysed discussion boards 2 of the Galaxy Zoo project. By
Paper: 2016MNRAS.461..312J (fulltext)
because of enhanced mass outflow from the circumbinary disc. In the first case, we note the apparent dimming of the ruff on days 23 and 26 is suggestive that the effect may be partly artefactual. We also note that the ruff's extent of ≳50 mas is at the upper limit of angular scales to which the VLBA is sensitive, because the shortest VLBA baselines give only sparse coverage of the centre of the uv -plane. The third case is intriguing given optical observations of Blundell et al. ( 2011 ) that provided evidence that the inner circumbinary disc structure was disrupted during a flare in 2004 October. In the 2003 July observations, there are hints of detachment between the core components and the radio ruff from day 31 onwards, raising the possibility that the enlarged ruff is a manifestation of an ejected circumbinary disc. However, this would require a very fast outflow speed – at 5.5 kpc, 50 mas corresponds to a physical size of 4 × 10 13 m. To cover this in ∼10 d would require an outflow speed exceeding 40 000 km s −1 , which seems improbably high (cf. Mioduszewski et al. 2004 ). 5.4 The unified picture Despite the lack of contemporaneous optical and radio monitoring data, there are enough similarities between what we see in these milliarcsecond-scale observations and what is seen at other scales and frequencies that we can draw out some general characteristics. In Fig. 4 , we see that jet launch speeds appear to fall over a period of about 30 d before the flare begins and jet speeds increase dramatically on or around day 15. A similar decline in speed is seen in the optical jet lines in fig. 6(e) of Blundell et al. ( 2011 ), where jet speeds decline from ∼0.27 c to ∼0.24 c over the 20 d prior to a flare. Both in the 2011–2012 observations, and during the latter half of the 2003 campaign, we see bright, fast ejections, punctuated by gaps of 5 to 10 d. Again, almost identical behaviour is seen by Blundell et al. ( 2011 ) in the optical jet lines, where the intensity of the optical jet lines is lowest in the days immediately preceding the start of the flare. This stands in contrast to the behaviour of the jet in the early days of the 2003 campaign, where the jet has a more steady character. The difference is most clearly seen by comparing the first and last frame of Fig. 3 (noting that the images of all epochs are contoured to the same minimum level): in the first frame we see faint bolides set against an apparently steady background flow, while in the last we see a succession of bright, compact bolides separated by gaps of low/no emission. Taken together, we note that the variations in both the speeds of the optical emission lines and the speeds determined by the milliarcsecond scale bolides possess remarkably similar characteristics. In the absence of simultaneous, time-resolved optical and radio observations, this coincidence is intriguing evidence that the ejecta revealed by optical emission are indeed directly related to the ejecta seen
Paper: 2019A&A...631A.139R (acknowledgments)
Acknowledgements We thank the anonymous referee for reviewing our work and for insightful and constructive comments which improved the manuscript. The project is supported by CNRS, by the Agence Nationale de la Recherche (ANR-14-CE33-0018, GIPSE), the OSUG@2020 labex and the Programme National de Planétologie (PNP, INSU) and Programme National de Physique Stellaire (PNPS, INSU). Most of the computations presented in this paper were performed using the Froggy platform of the CIMENT infrastructure ( https://ciment.ujf-grenoble.fr ), which is supported by the Rhône-Alpes region (GRANT CPER07_13 CIRA), the OSUG@2020 labex (reference ANR10 LABX56) and the Equip@Meso project (reference ANR-10-EQPX-29-01) of the programme Investissements d’Avenir, supervised by the Agence Nationale pour la Recherche. P.K. and R.J.D.R. thank support from NSF AST-1518332, NASA NNX15AC89G and NNX15AD95G/NEXSS. This work benefited from NASA’s Nexus for Exoplanet System Science (NExSS) research coordination network sponsored by NASA’s Science Mission Directorate.
Paper: 2016MNRAS.457.1636B (acknowledgments)
We would like to thank the anonymous referee for suggestions that helped improve this manuscript. Support for this work was provided by the National Aeronautics and Space Administration through Chandra Award Number G04-15088X issued by the Chandra X-ray Observatory Center, which is operated by the Smithsonian Astrophysical Observatory for and on behalf of the National Aeronautics and Space Administration under contract NAS8-03060. TJG and PPP acknowledge support under NASA contract NAS8-03060. This research has made use of 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. This research has made use of the SIMBAD data base, operated at CDS, Strasbourg, France.
Paper: 2016MNRAS.460.1163H (acknowledgments)
The research leading to these results received funding from NASA OSS grant NNX14AJ56G.
Paper: 2021ApJ...912..112W (fulltext)
ratio of 200. Table 5 Spearman’s Rank Correlation Coefficient of the Logarithmic MLR with Stellar Luminosity, Infrared Magnitude, and Colors Figure 12. The relation between MLR and luminosity. Figure 13. Comparison of the relation between MLR and luminosity with the works of de Jager et al. ( 1988 ), van Loon et al. ( 2005 ), and Beasor Davies ( 2018 ). Unlike the common slope, the intercept, i.e., the coefficient of proportion in the relation, is not the same. As discussed in Section 3.1 , the amount of MLR depends on multiple factors, including the dust species, the dust size, the stellar expansion velocity, and luminosity. The effects of dust species and size are discussed in previous sections, and can be as large as one order of magnitude. The expansion velocity and luminosity should have less effect, probably changing the MLR by a factor of a few. Taking all the factors into account, the MLR may differ by two orders of magnitude for a given observational SED, therefore the comparison of the vertical shift is meaningful only under the same model conditions. Taking T eff into account, the relationships become The fitting results are displayed in Figure 14 together with the value of R 2 , which implies an improved relation in comparison with the luminosity alone. In general, the fitting is reasonably good with the residual , but it deviates somewhat from the measurements, particularly for the silicate dust. This may be understood because MLR depends on other parameters in addition to L and T eff , such as stellar mass, radius or surface gravity (Reimers 1975 ; Beasor et al. 2020 ), and pulsation period (Goldman et al. 2017 ). But these parameters are not conveniently available from observation. Figure 14. The relation between MLR and the luminosity and effective temperature. The red line marks Y = X . 4.4. Relation of MLR with Infrared Brightness After examining the infrared-band brightness, a very good relation is found with the mid-infrared flux in the Spitzer/IRAC4 band at 8 μ m and the MIPS24 band at 24 μ m. Linear fitting yields the following relations with the absolute magnitude, for which the distance modulus is 24.32 for M31 and 24.50 for M33 (Paper I ). The fitting lines are shown in Figures 15 and 16 . The correlation coefficients are all large (−0.7) as listed in Table 5 , which implies a tight relation of the mid-infrared flux with the MLR. This relation is expected from the warm temperature of the circumstellar dust, which emits mainly in the mid-infrared. On other hand, the relation with near-infrared flux is weak and not presented. Figure 15. The relation between MLR and the Spitzer/IRAC4 absolute magnitude. Figure 16. The relation between MLR and the Spitzer/MIPS24 absolute magnitude. 4.5. Relation of MLR with Infrared Color Indexes Examining the infrared colors, it is found that the MLR is sensitive to IRAC1 − IRAC4: As seen in Figure 17 , the scattering around the fitting line is significant, and the correlation of MLR
Paper: 2020ApJ...905..148B (fulltext)
values of FWHM, namely ∣〈FWHM〉 N − 〈FWHM〉 N−1 ∣. The results of this analysis, for N up to 100, are presented in the left panel of Figure 7 . As shown in the figure, the mean FWHM converges to ≃2.85 px after only about 20 iterations, N, at which point the standard error of the mean, which decreases as (as in fact expected for a normal distribution of FWHM values with a standard deviation of the population Σ) exceeds the differential error ∣〈FWHM〉 N − 〈FWHM〉 N−1 ∣. Figure 7. Left: the estimated mean FWHM of the PSF modeled with 2D Gaussian, 〈FWHM〉 N ± Σ N , as a function of the number N of PSF simulations performed (upper panel); the successive difference in the mean values ∣〈FWHM〉 N − 〈FWHM〉 N−1 ∣, along with the standard error of the mean Σ N (dashed curve), are shown on the lower panel. Right: histogram of the emerging FWHM values after 100 PSF simulations, along with the best-fit normal distribution returning 〈FWHM〉 100 ≃ 2.84 px and Σ ≃ 0.21 px. Right panel of Figure 7 presents the histogram of the emerging FWHM values after 100 PSF simulations along with the best-fit normal distribution returning, 〈FWHM〉 100 ≃ 2.84 px and Σ ≃ 0.21 px. Given this, the radius for the extraction region encompassing 3 σ of the photons from a point source formally reads as, px. However, the source PSF is not well represented by a 2D Gaussian and particularly with limited photon statistics, broad wings of the PSF do matter. To illustrate this, in Figure 8 we plot the enclosed photon count fraction for each simulated PSF. As shown, the real 2 σ radius ranges between 2.5 px and 5 px, depending on a particular PSF realization, and the 3 σ radius is, in all cases, at ∼10 px. Hence, in the analysis of the surface brightness presented in this paper, instead of a 2D Gaussian approximation, we build a table model for the core PSF as a result from the averaging of over 100 PSF simulations. Figure 8. Enclosed count fraction for 100 simulated PSF images; horizontal dotted lines from bottom to top correspond to 1 σ , 2 σ , and 3 σ count fractions, respectively. Appendix B Surface Brightness Profiles Along and Across the Jet Axis We have also extracted the radial profiles of the net counts separately for the four quadrants of the concentric annular regions centered on the CGCG 292−057 nucleus, oriented to the east, to the west, to the north, and to the south from the center. The east and west quadrants correspond therefore to the surface brightness profiles along the axis of the inner jets in the system, while the north and south quadrants to the surface brightness profiles across the jet axis. Due to the limited photon statistics in separate quadrants, we had to increase the size of the annuli from 2 px (as adopted in Figure 3 ) up to 7 px. The resulting profiles are presented in Figure 9 . Figure 9. X-ray surface brightness profiles resulting from extracting the net counts from four quadrants of a concentric stack of annular regions (each with the bin size of 7 px)
Paper: 2018ApJ...866...99B (acknowledgments)
We gratefully acknowledge everyone involved in the Gaia and Kepler missions for their tireless efforts that have made this paper possible. We also thank the reviewer for helpful feedback that improved this paper, and Erik Petigura and BJ Fulton for helpful discussions. T.A.B. and D.H. thank Savita Mathur for providing supplementary material for the DR25 stellar properties catalog. T.A.B. and D.H. acknowledge support by the National Science Foundation (AST-1717000) and the National Aeronautics and Space Administration under Grants NNX14AB92G issued through the Kepler Participating Scientist Program. This work has made use of data from the European Space Agency (ESA) mission Gaia ( https://www.cosmos.esa.int/gaia ), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.cosmos.esa.int/web/gaia/dpac/consortium ). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. This publication makes use of data products from the Two Micron All Sky Survey, which is a joint project of the University of Massachusetts and the Infrared Processing and Analysis Center/California Institute of Technology, funded by the National Aeronautics and Space Administration and the National Science Foundation. This research has made use of NASA’s Astrophysics Data System. This research was made possible through the use of the AAVSO Photometric All-Sky Survey (APASS), funded by the Robert Martin Ayers Sciences Fund. This research made use of the crossmatch service provided by CDS, Strasbourg. This research has made use of 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. Software : astropy (Astropy Collaboration et al. 2013 ), dustmaps (Green et al. 2018 ), GNU Parallel (Tange 2018 ), isoclassify (Huber et al. 2017 ), Matplotlib (Hunter 2007 ), mwdust (Bovy et al. 2016 ), Pandas (McKinney 2010 ), SciPy (Jones et al. 2001 ).
Paper: 2015ApJ...812...28S (acknowledgments)
The authors are grateful to an anonymous referee for constructive comments that helped to substantially improve the paper. Computations are performed on a 20 node HYDRA cluster (dual Xeon X5675 with 6 cores per processor and 3.06 GHz clock speed), FORNAX (dual Opteron 6220 with 8 cores and 3.0 GHz clock speed), and KASPAR (dual Xeon X5675 with 6 cores per processor and 3.06 GHz clock speed) computing facilities at the Indian Institute of Astrophysics.