adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020ApJ...903L..39C (fulltext)
of ∼30 km s −1 . The same grid-based Monte Carlo code “STaRS 2 2 http://github.com/csj607/STaRS ” is used to investigate the distribution and kinematics of the neutral hydrogen regions in NGC 6886 and NGC 6881. Figure 2 shows a schematic illustration of the scattering geometry adopted in this work. The scattering region is a partial spherical shell surrounding the central He ii region. The covering factor CF is defined as , where θ is the opening angle of the partial spherical shell. The H i region is assumed to be of uniform density expanding with a single speed . Figure 2. Schematic illustration of the simulation. The neutral region is assumed to be a partial spherical shell with the inner and outer radii R in and R out , respectively, and the opening angle θ . The scattering region is divided into a large number of small cubical grids to trace each photon until escape. Far-UV He ii λ 1025 photons generated in the central He ii region are incident on the H i region. The neutral region is optically thick to Rayleigh-scattered UV photons, and optically thin to Raman-scattered optical photons. Each far-UV photon is traced until escape from the neutral region through multiple Rayleigh scattering, or after Raman scattering. The incident far-UV He ii λ 1025 is assumed to be described by a single Gaussian function determined in Section 3.2 . A simulation model is set with the following free parameters: the covering factor CF of the scattering region, H i column density N HI , and expanding speed . The incident He ii λ 1025 flux is deduced from the observed He ii λ 6560 line flux and using case B recombination theory (Storey Hummer 1995 ). We refer to Hyung et al. ( 1995 ) and Pottasch Surendiranath ( 2005 ) for NGC 6886 and also Kaler et al. ( 1987 ) for NGC 6881 for the values of T e = 1.25 × 10 4 K and n e = 10 4 cm −3 . In Figure 3 , we present our best-fit results for NGC 6886 and NGC 6881 in the middle panels. The gray lines represent the observational data and the black lines show our best-fit simulation results. Despite severe blending of the Raman-scattered He ii features with strong [N ii ] λ 6548 lines, the broad features with a red-tail structure are conspicuous in both PNe. These red-tailed features are attributed to the expansion of H i region. Figure 3. Comparison of the observational data (gray) and our simulation data (black) for NGC 6886 (left) and NGC 6881 (right). The best-fit models are found in the middle panels. In the middle-left panel, the model parameters are CF = 0.3, N HI = 5 × 10 20 cm −2 , and (CF30-N5E20-V25). We also present models with a lower expansion speed of (CF30-N5E20-V20, top-left) and a higher value of 30 km s −1 (CF30-N520-V30, bottom-left) for comparison. In the middle-right panel, the model parameters are CF = 0.6, N HI = 2 × 10 20 cm −2 , and (CF60-N3E20-V30). The top-right and bottom-right panels show the models with (CF60-N3E20-V20) and 40 km s −1 (CF60-N3E20-V40), respectively. In the case of NGC
Paper: 2017ApJ...851...48S (fulltext)
. Figure 2.40. SDSSJ2324+0105. The observational data, B-spline-subtracted image, predicted lensed image, final residual, and background source model are shown from left to right, respectively. Images are orientated such that north is up and East is to the left. For each system, the results of the two source models are split into two rows with the parameterized source model on the top and the pixelized source model on the bottom. The white lines in the last panels are the caustics of the lens model. The source plane panels are magnified by factors from 2 to 32 relative to the image plane panel as indicated in each panel. The color bars indicate the intensity levels in units of electrons per second per pixel 2 . Table 2 Strong-lens Model Parameters of the 40 S4TM Grade-A Lenses Note. Column 1 is the SDSS system name. Columns 2–4 are the Einstein radius, minor-to-major axis ratio, and major-axis position angle of the SIE component with respect to the north. Column 5 indicates the number of Sérsic components used. Column 6 is the average magnification. Column 7 is the total projected mass within the Einstein radius from the best-fit lens model. Column 8 is the estimated stellar mass assuming a Chabrier IMF from Shu et al. ( 2015 ). Column 9 is the inferred dark-matter fraction within half of the half-light radius. Column 10 provides the χ 2 value and the dof. 5. Discussion The S4TM survey is optimized to select strong-lens systems with relatively lower-mass lens galaxies as a complementary sample to the SLACS sample. In Figure 3 , we compare the stellar velocity dispersion, Einstein radius, total projected mass within the Einstein radius, and stellar mass between the two lens samples. The SLACS sample refers to the 63 grade-A lenses with lens models in Bolton et al. ( 2008a ). Figure 3. Distributions of the stellar velocity dispersion, Einstein radius, total enclosed mass within the Einstein radius, and stellar mass for the S4TM (solid histograms) and the SLACS (dashed histograms) lens samples. The stellar mass is derived from HST F814W-band photometry assuming a Chabrier IMF as explained in Shu et al. ( 2015 ). The distribution of the stellar velocity dispersions σ SDSS of the S4TM sample has a strong peak at about 200 km s −1 and declines rapidly on both ends. On the other hand, the distribution of σ SDSS of the SLACS sample is almost flat from 200 to 320 km s −1 . As a comparison, the median σ SDSS of the S4TM sample is 203 km s −1 , while it is 243 km s −1 for the SLACS sample. The median Einstein radius of the S4TM sample is 100, almost 15% smaller than that of the SLACS sample (117). The distributions of the Einstein radii for the two samples further suggest that the S4TM sample is more abundant in systems with Einstein radii smaller than 08 (12/40 versus 3/63) and lack systems with Einstein radii larger than 12 (8/40 versus 30/63). We basically expect the distributions of M Ein to be similar to those of b SIE because Σ crit , which is
Paper: 2016A&A...588A.101F (acknowledgments)
M.P. is a member of the working team of projects AYA2013-40979-P and AYA2013-48226-C3-2-P, funded by MINECO. P. M. acknowledges the support from the European Research Council (grant CAMAP-259276), the government of Spain (MINECO grant AYA2013-40979-P), and the regional government of Valencia (grant PROMETEO-II-2014-069).
Paper: 2018ApJ...863...63S (fulltext)
where we expected to be incomplete, and fewer at bright magnitudes, where we expected to be very complete. Flux uncertainties and missing VISTA fluxes were assigned in the same way as during the contamination test in Section 3.3 . Mock galaxies were not assigned photometric flags. This likely results in an incompleteness of only a couple percent, as the fraction of all sources affected by flags is small. All sources with an S/N of greater than 3.5 in any one band are flagged in another band less than 2% of the time on average (never exceeding 4%). In addition, all images have ∼0.5% of all pixels flagged on average (never exceeding 2%). The photometric catalog of 600,000 mock galaxies was then run through EAZY to generate z phot PDFs, and our high- z galaxy selection was applied. The completeness was defined as the number of mock galaxies recovered divided by the number of input mock galaxies, as a function of input absolute magnitude and redshift. Figure 7 shows the results of our simulation. We define the 50%-completeness limit as the absolute magnitude where the area under the curve falls to less than 50% of the areas under the average of the M UV, i ′ = −25 to M UV, i ′ = −28 curves, which we find to be at M UV, i ′ = −22 ( m = 24 for z = 4). 4. Results 4.1. The Rest-frame UV <italic>z</italic> = 4 Luminosity Function We utilize the effective volume method to correct for incompleteness in deriving our luminosity function. The effective volume ( V eff ) can be estimated aswhere is the comoving volume element, which depends on the adopted cosmology, and C ( M UV, i ′ , z ) is the completeness as calculated in Section 3.4 . The integral was evaluated over z = 3–5. To calculate the luminosity function, we convert the apparent i -band AB magnitudes ( m i ′ ) to the absolute magnitude at rest-frame 1500 Å ( M UV, i ′ ) using the following formula:where d L is the luminosity distance in pc. The second and third terms on the right-hand side are the distance modulus. Our UV luminosity function is shown as red diamonds in Figure 8 . We note that we do not include the luminosity function data points in bins M UV, i ′ ≥ −22 in our analysis, as these bins are below our 50%-completeness limit as discussed in Section 3.4 , where the completeness corrections are unreliable owing to the low S/N of our data. This is confirmed by comparing to the HST CANDELS results in these same magnitude bins, which are more reliable owing to their higher S/N. In Figure 8 we also include the UV luminosity function of z = 4 UV-selected galaxies from deeper HST imaging by Finkelstein et al. ( 2015 ) as green squares. In the bin where we overlap with this data set ( M UV, i ′ = −22.5), both results are consistent; however, if the luminosity function derived from HST imaging is extrapolated to brighter magnitudes, it would fall off more steeply than our luminosity function. While our luminosity function declines from M UV, i ′ = −22 to M UV, i ′ = −24, it flattens out at
Paper: 2019MNRAS.487.3432M (acknowledgments)
ACKNOWLEDGEMENTS We thank the anonymous referee for the comments, especially for the suggestion to separate our original manuscript into two standalone publications, which have improved the clarity of the papers. We also thank Rainer Beck for his careful reading and valuable suggestions and comments as the MPIfR internal referee, and Aristeidis Noutsos, Shane O’Sullivan, and Dominic Schnitzeler for insightful discussions about this project. YKM was supported for this research by the International Max Planck Research School (IMPRS) for Astronomy and Astrophysics at the Universities of Bonn and Cologne. YKM acknowledges partial support through the Bonn-Cologne Graduate School of Physics and Astronomy. AB acknowledges financial support by the German Federal Ministry of Education and Research (BMBF) under grant 05A17PB1 (Verbundprojekt D-MeerKAT). ASH and SKB acknowledge support by NASA through grant number HST-AR-14297 to Haverford College from Space Telescope Science Institute, which is operated by AURA, Inc. under NASA contract NAS 5-26555. ASH is partially supported by the Dunlap Institute, which is funded through an endowment established by the David Dunlap family and the University of Toronto. The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc. This research has made use of the NED which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. The WHAMSS have been funded primarily through awards from the U.S. National Science Foundation.
Paper: 2017A&A...600A..78P (fulltext)
a function of time (symbols). We use Bayesian analysis (Sect. 2.4 ) to calculate how our oscillation model describes the observational data. Our Bayesian analysis returns the posterior PDF for each of the model parameters. These may be plotted as histograms, such as those for and in Fig. 6 , or summarised by quoting the median and 95% credible intervals as in Table 2 . The red shaded region represents the 99% credible interval for the loop position described by our model, including the estimated noise . The parameters (e.g., vertical dotted line denoting ) or model components (e.g., background trend shown by blue line in top left panel) plotted in figures correspond to the median values of the relevant parameters. For comparison, the results for analysing Loop #1 with a model including the fundamental kink mode only is shown in Fig. 16 . The top right panel shows the wavelet analysis of the signal. The colour contours represent the spectral amplitude (square root of the spectral power). The dashed lines show the time-dependent periods of oscillation , which demonstrate a gradual increase during the course of the oscillation of approximately 20%. The middle right panel shows a 2D histogram of the seismologically determined transverse density profile parameters based on our MCMC model samplings (with intensity normalised to unity). The red bars correspond to the median values and 95% credible intervals based on the individual 1D histograms (bottom panels) and quoted in Table 2 . They give a convenient summmary of the localisation of the parameters by our model fit, though we note that they do not reflect the strong inverse relationship between and in determing the damping rate (see Fig. 2 ). Consequently, the 2D contour (likely density profile parameters) is significantly more localised than considering the posterior credible intervals independently would suggest. It is necessary to appreciate this inverse relationship when considering the quality of the constraint on density profile indicated by the data. For example, is generally less well constrained for lower density contrasts than for larger density contrasts, as discussed in Sect. 2 . We note that the 95th percentiles quoted in this paper correspond to a confidence interval, in contrast to Paper I where the errors were estimated values of . For example, Paper I gives the density contrast for Loop #1 as , and here (Table 2 ) it is , which corresponds to a that is approximately five times smaller. On the other hand, the for the here is comparable to that in Paper I. The solid curves in the histogram plots are fits to the data using the exponentially modified Gaussian function of the form where is the complementary error function, is a constant determining the amplitude, and are the mean and standard deviation of the Gaussian component, respectively, and is the rate of the exponential component. Equation ( 18 ) is found to describe the histogram profiles well, in particular when the
Paper: 2019A&A...630A...4K (fulltext)
to the nucleus, e n directed along the orbital angular momentum perpendicular to the orbital plane, and e t being orthogonal to both e r and e n . By comparing 14 cometary orbits, Marsden et al. (1973) derived the following model for the non-gravitational acceleration: with the constant parameters A 1 , A 2 , and A 3 and the empirical activity function The solar distance r ′ = r ( t − Δ t ) includes a time-shift Δ t introduced by Yeomans Chodas (1989) to account for activity asymmetries with respect to perihelion, as studied by Sekanina (1988) . As we discuss later, g ( r ) is not directly proportional to the sublimation flux, as originally stated by Marsden et al. (1973) , Eq. ( 4 ). The Marsden parameters provide an excellent albeit empirical description of the non-gravitational acceleration. The integration of a Marsden-type orbit proceeds by solving Eq. ( 1 ) with the non-gravitational acceleration Eq. ( 2 ). 2.2 Orbit determination based on spacecraft data Moving beyond the Marsden model requires more detailed data from spacecraft missions or radar observations about the magnitude and direction of the observed non-gravitational acceleration in space. For 67P, the Rosetta mission provides a two-year data set for the rotation-axis orientation (see Kramer et al. 2019 ) and the orbital evolution, but an iterative process was required to obtain the three-dimensional non-gravitational acceleration with high precision. We started with the multi-arc orbital solution from the flight dynamics team at ESOC, which is available as SPICE kernel (CORB_DV_257_03___T19_00345.BSP) and is returned from the Horizons system as the position for 67P during the Rosetta mission. In the following we refer to it as r ESOC ( t ). Before and after the Rosetta mission, Horizons returns the solution 67P/K154/2 based on Marsden parameters with a discontinuous jump by 2000 km into the Rosetta period. In addition to this discontinuity in Horizons, further discontinuities in the ESOC position vector r ESOC ( t ) during the Rosetta mission exist, which do not allow us to obtain the acceleration by a second derivative of the position vector (see the discussion by Attree et al. 2019 ). Table 1 Representative initial conditions for the orbit integration at JDB 2 456 897.71970 (≈ −350 days from perihelion), mean equator, and equinox of the terrestrial J2000 frame. Notes. An additional 30 nearby initial conditions have been identified that differ on average by less than 22 km over the (−400, 400) day interval from the partly nonphysical ESOC orbit. Table 2 Non-gravitational acceleration parameters used by Horizons in conjunction with the standard parameters α = 0.1112620426, k = 4.6142, m = 2.15, n = 5.093, and r 0 = 2.808 for g ( r ) in Eq. ( 3 ). An accurate estimate of the non-gravitational acceleration is tied to finding the best possible initial condition during a time with negligible cometary activity. We iteratively refined the orbit to identify the initial conditions
Paper: 2019MNRAS.489..681L (acknowledgments)
ACKNOWLEDGEMENTS We gratefully thank N. M. Frster Schreiber and the KMOS and SINS/zC-SINF team for sharing the reduced KMOS and SINFONI data analysed in this work. We thank P. G. van Dokkum for the GNIRS spectrum and P. Rosati who provided the reduced MUSE data. We thank also P. Martini for having kindly made available the reduced data cube of M82. AC, MT and FL acknowledge the support from the grants PRIN-MIUR 2015, PRIN 2017, and ASI n.2018-23-HH.0.
Paper: 2015ApJ...804...64M (acknowledgments)
Thanks to the anonymous referee for their fast and generous comments. Thanks to Keivan Stassun for his comments on activity diagnostics, to Michael Cushing for providing a pre-release version of SpexTools for uSpeX, and to Coryn Bailer-Jones for providing the spectral response profiles for Gaia . Additional thanks to Adam Kraus and Aaron Rizzuto for useful discussions on the content of this manuscript. PPD Figures were made with the aid of the triangle.py package (Foreman-Mackey et al. 2014 ). This research was supported by NASA grants NNX10AQ36G and NNX11AC33G to E.G. and NASA grants ADAP12-0172 and 14-XRP14 2-0147 to T.B. SNIFS on the UH 2.2 m telescope is part of the Nearby Supernova Factory project, a scientific collaboration among the Centre de Recherche Astronomique de Lyon, Institut de Physique Nuclaire de Lyon, Laboratoire de Physique Nuclaire et des Hautes Energies, Lawrence Berkeley National Laboratory, Yale University, University of Bonn, Max Planck Institute for Astrophysics, Tsinghua Center for Astrophysics, and the Centre de Physique des Particules de Marseille. Based on data from the Infrared Telescope Facility, which is operated by the University of Hawaii under Cooperative Agreement no. NNX-08AE38A with the National Aeronautics and Space Administration, Science Mission Directorate, Planetary Astronomy Program. Some of the data presented in this paper were obtained from the Mikulski Archive for Space Telescopes (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. The CHARA Array is funded by the National Science Foundation through NSF grants AST-0606958 and AST-0908253 and by Georgia State University through the College of Arts and Sciences, as well as the W. M. Keck Foundation. This research made use of the SIMBAD and VIZIER Astronomical Databases, operated at CDS, Strasbourg, France ( http://cdsweb.u-strasbg.fr ), and of NASA Astrophysics Data System, of the Jean-Marie Mariotti Center SearchCal service ( http://www.jmmc.fr/searchcal ), co-developed by FIZEAU and LAOG/IPAG. Facilities: IRTF, UH:2.2 m, CHARA
Paper: 2021ApJ...910..136L (fulltext)
measured ratios range between 1.04 ± 0.05 for the meteorite Malakal and 4.56 ± 0.38 for the meteorite Jilin. The ratio for Jilin is not representative because this meteorite experienced a complex exposure history and 26 Al is not in saturation, making the 22 Na/ 26 Al ratio too high (Begemann et al. 1985 ). Ignoring Jilin for the discussion, the upper limit is reduced to 2.48 for the meteorite Dhajala. Again, excluding Jilin, the average value for the 22 Na/ 26 Al ratio for the studied meteorites is 1.46 ± 0.27. Both the range and the average value are in agreement with the model predictions. In addition, the 22 Na/ 26 Al ratios for a variety of ordinary chondrites studied by Bhandari et al. ( 1994 ) range between ∼1 and ∼2.3, again in agreement with the model predictions. For the meteorite Chelyabinsk, Alexeev et al. ( 2019 ) give 22 Na/ 26 Al ratios of 1.4 ± 1.2 for a depth larger than 100 cm, 2.42 ± 0.17 at a depth of 10–50 cm, and of 2.53 ± 0.24 for depths of 50–100 cm. For the Košice meteorite, the same authors measured 1.45 ± 0.20 for depths below 10 cm and 1.52 ± 0.21 for depths between 10 and 50 cm. All values are well within the range of the predicted 22 Na/ 26 Al ratios we modeled for an L-chondrite with a radius of 45 cm (see Figure 6 ). The data for meteorites indicate that the 22 Na/ 26 Al production rate ratios vary enough to be useful for studies on the temporal variability of the solar modulation function. For the following, more qualitative discussion, we use the reconstruction of the solar modulation function given by Usoskin et al. ( 2017 ) shown in Figure 2 . Because the data are for 1 au, we adjusted them to the meteoroid orbits at ∼3 au by simply subtracting ΔΦ = 60 MeV, which we determined above as the best value for the long-term average gradient in the inner solar system (see also Section 2.3 ). Note that this procedure is surely not entirely correct because the radial gradient in the inner solar system is expected to vary on short timescales. However, for our qualitative discussion, this rough assumption is sufficient. Considering now a meteorite that fell in 1970, the 22 Na production rate is determined by the solar modulation function from ∼1965 to ∼1970, which was 〈Φ〉 ∼ 480 MeV. According to Equation ( 18 ), this corresponds to a 22 Na/ 26 Al (1965–1970) production rate ratio of ∼2.08. For a meteorite that fell in 1980, the relevant parameters are 〈Φ〉 ∼ 420 MeV and 22 Na/ 26 Al (1975–1980) ∼2.25. Finally, a meteorite that fell in 1985 experienced 〈Φ〉 ∼ 670 MeV, corresponding to 22 Na/ 26 Al (1980–1985) ∼1.7. Consequently, the variability in 22 Na/ 26 Al production rate ratios for the three cases is more than 30%, sufficient for detailed studies of the temporal variability of the solar modulation function using freshly fallen meteorites. 5.2. Can We Put Constraints on the Meteoroid Orbits? For the qualitative discussion above, we assumed that the meteorites spent their entire lifetime in the same orbit at ∼3 au and