adsabs/WIESP2022-NER huggingface dataset:
Paper: 2016MNRAS.460.1318B (acknowledgments)
This work has been done under the Indo-South Africa project DST/INT/SA/P-02. This paper includes data collected by the Kepler mission. Funding for the Kepler mission is provided by the NASA Science Mission directorate. The authors wish to thank the Kepler team for their generosity in allowing the data to be released and for their outstanding efforts which have made these results possible. Much 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. LAB wishes to thank the South African Astronomical Observatory and the National Research Foundation for financial support. SJ and ES acknowledges the grants INT/RFBR/P-118 and RFBR Grant no. 12-02-92693-IND_a jointly funded by DST Govt of India and Russian Academy of Science, Russia. Observations on the SAO RAS 6-m telescope are carried out with financial support from the Ministry of Education and Science of the Russian Federation (agreement no. 14.619.21.0004, project ID RFMEFI61914X0004). ES is thankful to the Russian Science Foundation (grant no. 14-50-00043) for financial support of this study. KS acknowledges useful discussion and comments from Philippe Prugniel.
Paper: 2020MNRAS.499..183W (fulltext)
length: this value is extracted from Sbordone Ledoux ( 2018 ) and is equal to 50 mas (0.5 per cent of the slit length, 10 000 mas). This accuracy, as well as the one of the binary separation, are considered in our measurements as rectangular distributions, by excess, where we account for the factor to convert them into normal distributions. standard deviation: as stated above, each point is the mean of all the data of 1-nm bins. In the example we show, we had 49 data points to average. We consider the standard deviation of the mean as another source of uncertainty. It is equal to 0.02 pixel in our example. Using equation ( 4 ), we can derive the combined uncertainty on the pixel scale. It is equal to 0.87 mas using equation ( 5 ). where u 1 is the uncertainty on the angular separation in mas, u 2 and u 3 are the uncertainties of the fits and the standard deviation in pixels, respectively, is the distance of the binary system in pixels (18 pixels in our example), and is the distance of the binary system in mas (4680 mas in our example). Instrumental dispersion calibration: The uncertainty of this component is the standard deviation of the measurements (at airmass 1), and is considered constant over the range. It is equal to 8.3 mas. Atmospheric dispersion measurement: This component has similar subcomponents as the ones of the accuracy of the fits as well as the standard deviation. In addition, we take into consideration the total uncertainty of the pixel scale (equation 5 ). The combined uncertainty on the atmospheric dispersion measurement is also derived from equation ( 4 ). It is equal to 16.18 mas using equation ( 6 ). where is the measured atmospheric dispersion in pixels ( in our example), and PS is the computed pixel scale in mas. Figure 8. Top: Measured atmospheric dispersion, before and after correction by the instrumental dispersion in the blue range. We also show the expected dispersion using Filippenko’s model; bottom: Atmospheric dispersion residuals, before and after correction by the instrumental dispersion. The results obtained for the red arm are shown in Figs A3 and A4 . Table 2. Atmospheric parameters at the time of observation. Table 3. Uncertainties budget showing in details what each source of error is introducing. The contribution of each subsource is computed using equation ( 4 ) and show the results at the wavelength 315 nm of one exposure. The total uncertainty of each source is computed using the root sum squared of its contributions. The last column represents the references used in these computations: (1) Pourbaix Boffin ( 2016 ); (2) study with generated data; (3) experimental standard deviation of the mean value; and (4) Sbordone Ledoux ( 2018 ). The final uncertainty of the method listed, taking into consideration the components described above, is computed using equation ( 7 ). The accuracy of the method is as follows: ±18 mas in the blue range, ±17 mas in the red range. These results are for a 1σ interval.
Paper: 2018A&A...609A..70R (fulltext)
and denote the median of each band in the observed spectrum and model, and gives the standard deviation in each band in the observed spectrum. As also described in Manara et al. (2013a) , this does not represent a true distribution as the noise in the template spectrum is neglected. The models are visually compared to confirm that the best-fit model matches the observed spectrum. The uncertainties are estimated from the range of UV-excess fluxes spanned by models that yield a similar -like value as the best-fit model and match the spectrum visually (details are described in Sect. 4.3 ). Table 5 Parameter ranges of the hydrogen slab model. The measured UV-excess flux is converted to accretion luminosity (). The mass accretion rates () are then calculated byunder the assumption that gas accretes from the disk truncation radius in free-fall onto the star, where is adopted for consistency with pre-existing measurements (see review by Hartmann et al. 2016 ). Table 6 Mass accretion properties in the Cha association. Notes. Columns 2–7 list equivalent widths (EW), accretion luminosity (; using conversions from Alcalá et al. 2014 ), and mass accretion rates () for H and H, respectively. The last two columns list and determined from UV excess. 4.3. Description of fits and uncertainties Figures A.1 – A.4 show the best-fit model for each object. The values of and are listed in Table 6 . Uncertainties in the accretion rates are introduced by uncertainties in the stellar parameters, the distance, and in the individual fits (see, e.g., Herczeg Hillenbrand 2008 ; Manara et al. 2013a ). For our sample, uncertainties in are 0.3 dex. Weak accretors, such as RECX 11, have uncertainties of 0.5 dex because the excess is fainter than the photosphere, thereby increasing the uncertainty in bolometric correction. Upper limits on are difficult to estimate and depend on the ability to distinguish any excess emission from the underlying photospheric spectrum and on an uncertain bolometric correction. In order to estimate upper limits of for non-accreting objects, we perform a similar model fit as described above. The upper limits are compared to the detections in Fig. 8 a in terms of the ratio of . For K stars, the upper limit of for non-accreting stars is similar to of RECX 11. For M stars, the upper limits are almost uniform at (see also Manara et al. 2013b ), with the exception of RECX 12 (). Fig. 7 As in Fig. 6 . J0843, J0844, RECX 5 and RECX 9 show clear indications of UV-excess. For RECX 11, a slight excess is seen. Comments on each fit to the spectra of accreting objects are provided below. <italic>J0843 and J0844</italic>: due to the pronounced observed Balmer jump and strong excess in the Balmer and Paschen continua, the slab model parameters and therefore the are well constrained. Models using templates within 0.5 spectral subclasses yield accretion rates that agree to within 10% of these values. <italic>RECX 5</italic>: the best-fit is calculated using the M4
Paper: 2015MNRAS.454.4379B (acknowledgments)
This work was supported by the Russian Foundation for Basic Research (project no. 14-02-92615 KO_a), the UK Royal Society International Exchange grant IE140055, by the President of Russia grant for young scientists (no. MK-733.2014.2), by the programme of the Presidium of Russian Academy of Sciences P21, and by the Saint Petersburg State University research grant 6.37.341.2015. We would like to thank the anonymous reviewer for useful suggestions and comments on the manuscript.
Paper: 2018A&A...617A..51M (fulltext)
GR 290 in the remainder of this paper. The standard deviations are of similar value to our estimated measurement uncertainty of ±30 km s −1 . Since we do not detect variability in our two spectra that is larger than the uncertainty discussed above, we combined the July and August spectra, shifted the average by +179 km s −1 , and remeasured the lines. We list in Table 3 the RV of the emission in Col. 7, the equivalent width in Col. 8, the RV of the absorption component in the case of P Cyg profiles in Col. 9, and the intersection of the P Cyg absorption with the continuum level in Col. 10. The measurements for P Cyg profiles were performed with two-Gaussian fits 6 . We note that the contribution of nebular emission affects the FWHM. For example, in the case of H α , we measure FWHM = 6.6 Å if the line is fit with a single Gaussian while FWHM = 10.2 Å if the line is treated as a blend of a broad stellar line and a narrow superposed nebular emission. Despite this large difference in FWHM, the RV in both cases is nearly the same. The average V edge = −400 ± 144 km s −1 is similar to the value of v ∞ listed in Polcaro et al. (2016) for recent spectra. However, the He I lines have V edge ≃ 620 km s −1 , implying wind speeds that are faster than ever before observed. The Balmer-H lines also show the signs of a relatively fast outflow, as can be seen from the growing asymmetry in the blue wing of the higher excitation lines of this series. This is illustrated in Fig. 4 where H α and H β were scaled in intensity so as to align the red wings with that of H γ . The other lines plotted are H ∊ and H I 3835 Å. This asymmetry results from the same process that leads to P Cyg absorptions in He I but because of the large optical depth of the H-lines a fully developed absorption component is not visible. Fig. 4. Line profiles of the Balmer-H sequence plotted on a velocity scale corrected for the adopted −179 km s −1 systemic velocity. The lines of H α and H β are scaled by factors of 0.17 and 0.48, respectively, so as to align the red wings with that of H γ . The continuous lines show H γ , H ∊ , and H I 3835 Å. The progressive red-shift at shorter wavelengths appears to be a consequence of the increasing strength of the blue-shifted absorption which, in the helium lines, takes on the form of P Cyg absorption. This effect is explained by the fact that the optical depth of the wind is highest for H α and H β and decreases for lines that have smaller transition probabilities. 3.2. Stellar wind models We used the CMFGEN atmospheric modelling code ( Hillier Miller 1998 ) in order to estimate the current stellar parameters of GR 290. This code solves the radiative transfer equation for objects with spherically symmetric extended outflows using either the Sobolev approximation or the full comoving-frame solution of the radiative transfer equation. CMFGEN incorporates line blanketing, the effect of Auger ionization and clumping, which is parametrized by the volume
Paper: 2021A&A...648L...4T (fulltext)
to all disc particles and obtained f x , f y , f z , and f R , which are frequencies of oscillations along x , y , and z coordinates and cylindrical radius R . In Fig. 1 xy , xz , and yz snapshots for the whole BL model are presented at the time moment t = 450. A barlens is clearly seen in the xy snapshot, while a thick B/P structure is visible in the xz view. Fig. 1. Face-on, side-on, and end-on views of the considered model at t = 450 (6 Gyr). 3. Edge-on structure In Smirnov et al. (2021) , the BL model was disassembled into several orbital groups to analyse the face-on morphology of an overall bar. We assume that each individual group is characterised by a unique set of frequency ratios f R / f x and f R / f y . In Smirnov et al. (2021) , we focus on the face-on morphology of the inner parts of bars in our models. For this purpose, at the initial stage of our orbital analysis, we discarded orbits from the rest of the disc (outside the ring at the co-rotation radius (CR) R c ≈ 3) as well as of the thin part of the bar itself. Orbits from the thin part of a bar are located inside the CR and can be distinguished by the frequency ratio f z / f x > 2.25 ( Smirnov et al. 2021 ). Although the fraction of the 4 kk particles in these orbits is about 14%, all these orbits do not contribute to the thick B/P bulge, but only to the most remote and vertically thin parts of the bar (see Fig. 2 ). For the thick part of the bar, we identified all separate orbital groups (see Fig. 3 ) based on frequency ratios f x / f R and f y / f R and show that the structures to which they contribute are completely different in morphology. In this Letter, we analyse the edge-on morphology of the thick bar and its components in our BL model. Fig. 2. Side-on views of particles in the considered model at t = 450 (6 Gyr). Left : vertically thick part of the bar. Middle : all particles inside the co-rotation. Right : remaining particles which form a vertically thin structure. Fig. 3. Distribution of all orbits in the bar over the ratios of dominant frequencies f x / f R and f y / f R for the considered model at t = 450 (6 Gyr). The annotations illustrate the separation of orbits into orbital groups. Smirnov et al. (2021) identified the following components constituting a complex bar with a barlens: a ‘classic’ elongated bar ( f R / f x = 2.0 ± 0.1, 21.8% of particles from 4 kk) and two components contributing to the barlens (bl u orbits, in our notation, with f R / f x ≤ 1.9, 9.2%, and bl o orbits, with f R / f x ≥ 2.1, 13.5%). A ‘classic’ bar can be further divided into two subsystems. One is assembled from the x 1-like orbits ( f y / f x = 1.0 ± 0.1, 11.4%), and another one is the so-called boxy bar with orbits that are characterised by the ratio f y / f x ≥ 1.1 (10.4%). In Fig. 3 , all these orbital groups occupy separate regions (a spot and straight rays) and can be easily distinguished. The x 1-like bar has a complex structure itself. Three main orbital blocks, which
Paper: 2019MNRAS.489..268S (acknowledgments)
ACKNOWLEDGEMENTS We acknowledge helpful comments from the referee, which helped to clarify several aspects of the observations and analysis presented in this paper. We thank STScI science and technical staff members Peter McCullough and Amber Armstrong for special assistance in planning the DD time observations and Joe DePasquale for producing the colour HST image in Fig. 2 and the similar image superposed on the Chandra X-ray image in Fig. 6 . We thank both Kenji Hamaguchi and Mike Corcoran for discussions over many years concerning the X-ray emission from η Car’s outer shell. JM appreciates helpful discussions with John Raymond and Pat Hartigan. Based on observations made with the NASA/ESA Hubble Space Telescope, obtained at the Space Telescope Science Institute, operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555. Support was provided by NASA through grants GO-15596, GO-15289, GO-14768, and AR-14586 from the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555. NS’s research on Eta Carinae also received support from NSF grants AST-1312221 and AST-1515559.
Paper: 2015MNRAS.450.3191O (acknowledgments)
The work was a part of the research project RVO:61388963 (IOCB) and was supported by the Czech Science Foundation (grant P209/15-10267S). SY thanks ERC Advanced Investigator Project 267219 and AO thanks the UCL Impact scheme. The authors are grateful to Luciano Fusina, Gianfranco Di Lonardo and Adriana Predoi-Cross for providing their 15 NH 3 data prior to publishing.
Paper: 2017ApJ...834..152B (acknowledgments)
We thank S. Antonellini and I. Kamp for providing water emission spectra from their ProDiMo model explorations, and for insightful discussions on water emission in disks and on its dependence on disk model parameters. We also thank A. Carmona for providing the CRIRES spectrum of HD139614. A.B. wishes to thank all colleagues who made this work possible through their pioneering work in infrared spectroscopy of disks. A.B. thanks Simon Bruderer for the seminal work he did in 2011–2012, which provided knowledge and tools to enable this work. A.B. acknowledges financial support by a NASA Origins of the Solar System grant No. OSS 11-OSS11-0120, a NASA Planetary Geology and Geophysics Program under grant NAG 5-10201. E.v.D. acknowledges European Union A-ERC grant 291141 CHEMPLAN. This work is partly based on observations made with ESO telescopes at the Paranal Observatory under programs 179.C-0151, 093.C-0432, 088.C-0666, 079.C-0349, 081.C-0833, 091.C-0671, 082.C-0491, and 088.C-0898. This work is partly based on observations made with the Spitzer Space Telescope , which is operated by the Jet Propulsion Laboratory, California Institute of Technology. This publication makes use of data products from the Wide-field Infrared Survey Explorer, which is a joint project of the University of California, Los Angeles, and the Jet Propulsion Laboratory/California Institute of Technology, funded by the National Aeronautics and Space Administration. This research has made use of the VizieR catalog access tool, CDS, Strasbourg, France. The original description of the VizieR service was published in AAS 143, 23. This work is partly based on observations obtained at the W. M. Keck Observatory, which is operated as a scientific partnership among the California Institute of Technology, the University of California, and the National Aeronautics and Space Administration. The observatory was made possible by the generous financial support of the W. M. Keck Foundation.
Paper: 2019ApJ...872..109A (fulltext)
an injection of excess twist into the filament from one footpoint could drive a combination of longitudinal and rotational plasma motions (Uchida et al. 2001 ; Vršnak et al. 2007 ). However, when the Alfvén-wave packet caused by the injection is reflected at the opposite footpoint, the direction of rotational motions is expected to reverse, but in our case, the Doppler pattern persists (Figure 4 ). Moreover, we found no sign of twisting motions in the surge. Hence, we dismissed the wave-packet interpretation for the current observation. But we cannot dismiss the possibility that the observed rotational motion is a manifestation of unwinding relaxation; in other words, magnetic twist is propagating out of rather than into the filament, which could happen when the twisted filament field reconnects with the ambient, untwisted field (Okamoto et al. 2016 ). Additionally, Jing et al. ( 2003 , 2006 ) speculated that a mass perturbation in a fine thread would propagate along the spine across the obliquely stacked threads making up the filament. In our high-resolution observations, it is clear that mass motions involved in longitudinal oscillations are directed along individual threads, but it is obscure whether there are any perturbations propagating across the threads. To conclude, this study has demonstrated a promising methodology to diagnose the magnetic configuration of filaments and has substantiated the presence of the double-decker configuration with a flux rope atop a sheared arcade (Liu et al. 2012 ). Since no discernible separation is seen inside the filament studied, such a configuration might be more common than previously thought. With this configuration, however, one expects normal polarity in the lower atmosphere but inverse polarity in the higher atmosphere; because of the mixed signs of helicity, one also expects two pairs of plasma draining sites if the filament fully erupts, except that double-decker filaments often erupt partially (e.g., Liu et al. 2012 ; Cheng et al. 2014 ; Zhu et al. 2015 ). These predictions remain to be verified by future observation. Appendix Extraction of Oscillatory Patterns From the representative TD maps selected along the spine (Figure 6 ), we extracted the oscillatory pattern through a semi-automatic procedure that starts by visually choosing 20 points outlining the entire discernible pattern (Figure 7 (b)). Next, we applied a cubic spline to interpolate the intermediate points (Figure 7 (b)). We further refined the interpolations if the intensity profiles deduced from the TD maps could be well fitted by a Gaussian function ( gaussfit.pro ; Figure 7 (c)). Each intensity profile is 20 pixels wide, centering on the minimum intensity location within ±15 pixels of the original interpolated location. The full-width-half-maximum of the best-fit Gaussian function is considered as the uncertainty of the oscillation amplitude. We then fitted the extracted oscillation pattern by the exponentially
Paper: 2020ApJ...892..111F (fulltext)
Disks The lack of correlation in Figure 9 implies a decoupling of the mm grains and gas. We can test this further by assuming instead that there is no decoupling, and by comparing the inferred gas content of the inner disk with the accretion rate. Assuming a standard ISM gas-to-dust ratio, we compare the dust mass and accretion rate in Figure 12 and overplot the resulting timescale for the gas and dust content of the inner disk to accrete onto the star. Similar to Figure 9 , no correlation appears to be present ( r corr = − 0.24 ± 0.26), which is again consistent with decoupling of the mm grains and accreting gas. Second, we find that the lifetimes of most inner disks are relatively short compared to the lifetime of the disk, typically less than 10 4 yr, assuming an ISM gas-to-dust ratio of 100. Figure 12. Inner disk dust mass vs. accretion rate. 0.5 dex of error is assumed for the accretion rate. An ISM-like gas-to-dust ratio of 100 is assumed for the inner disk, the validity of which is discussed in Section 4.5 . No correlation appears to be present ( r corr = − 0.24 ± 0.26), which is again consistent with decoupling of the mm grains and accreting gas. Assuming a gas-to-dust ratio of 100, the lifetime of the inner disks is typically less than 10 4 yr, well below the typical lifetime of a protoplanetary disk. Given the high occurrence rate of inner disks in our sample, a short lifetime would require that the inner disk is regularly replenished by episodes of enhanced accretion from the outer disk. However, depletion of dust in the inner disk due to radial drift such as suggested in the previous section may cause the gas-to-dust ratio of the inner disk to be several orders of magnitude larger (Pinilla et al. 2012 ), implying proportional longer lifetimes. While we do not have a direct measurement of gas content of the inner disk to assess this scenario, viscous accretion disk theory predicts a link between the gas surface density, accretion rate, and viscosity, which we can use to estimate the gas content. We assume the following relations:with ν the disk viscosity, α the viscosity parameter, c s the sound speed, H the vertical height, Ω( r ) the angular velocity, k B the Boltzmann constant, μ ≈ 2 the mean molecular weight, m p the proton mass, G the gravitational constant, and M * the stellar mass. The third equation assumes that the disk is vertically isothermal. These equations result in a relation between the gas surface density and the accretion rate (Manara et al. 2014 ):The gas surface density profile is derived from the dust profile where ξ inner and ξ outer are the gas-to-dust ratios for inner and outer disks, respectively:while for the temperature profile, we use Equation ( 4 ). This means that we can express the gas-to-dust ratio ξ inner :withand equivalently, we can express viscosity α as We note that both quantities α and ξ inner are independent of r and can be evaluated at any radius, and we can compute either quantity while