adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019ApJ...883..134T (acknowledgments)
The Fermi LAT Collaboration acknowledges generous ongoing support from a number of agencies and institutes that have supported both the development and the operation of the LAT as well as scientific data analysis. These include the National Aeronautics and Space Administration and the Department of Energy in the United States, the Commissariat à l’Energie Atomique and the Centre National de la Recherche Scientifique/Institut National de Physique Nucléaire et de Physique des Particules in France, the Agenzia Spaziale Italiana and the Istituto Nazionale di Fisica Nucleare in Italy, the Ministry of Education, Culture, Sports, Science and Technology (MEXT), High Energy Accelerator Research Organization (KEK) and Japan Aerospace Exploration Agency (JAXA) in Japan, and the K. A. Wallenberg Foundation, the Swedish Research Council and the Swedish National Space Board in Sweden. Additional support for science analysis during the operations phase is gratefully acknowledged from the Istituto Nazionale di Astrofisica in Italy and the Centre National d’Études Spatiales in France. This work performed in part under DOE Contract DE-AC02-76SF00515.
Paper: 2018ApJ...859..136F (acknowledgments)
We acknowledge the anonymous referee for fruitful comments. We warmly thank Franck Le Petit for his assistance in the use of the PDR code. C.F. acknowledges the financial support for this work provided by the French space agency CNES along with the support from the Italian Ministry of Education, Universities and Research, project SIR (RBSI14ZRHR). We acknowledge the funding from the European Research Council (ERC), projects PALs (contract 320620) and DOC (contract 741002). This work was supported by the French program Physique et Chimie du Milieu Interstellaire (PCMI) funded by the Conseil National de la Recherche Scientifique (CNRS) and Centre National d’Etudes Spatiales (CNES) and by the Italian Ministero dell’Istruzione, Universitá e Ricerca through the grant Progetti Premiali 2012—iALMA (CUP C52I13000140001).
Paper: 2019ApJ...870...95B (acknowledgments)
Acknowledgments C.C. gratefully acknowledges support from the National Natural Science Foundation of China (NSFC), grant No. 11750110410.
Paper: 2016ApJ...826..161R (acknowledgments)
The authors are grateful to the staff of IRAM for their help during the observations of the IRAM 30 m data, and in particular to the Director for allowing us to use the telescope during the DDT proposal D04–15. This work was partly supported by the Italian Ministero dell’Istruzione, Università e Ricerca through the grant Progetti Premiali 2012–iALMA. P.C. and A.V. acknowledge the financial support of the European Research Council (ERC; project PALs 320620). J.M.-P. acknowleges partial support from the MINECO under grants AYA2010-2169-C04-01, FIS2012-39162-C06-01, ESP2013-47809-C03-01, and ESP2015-65597-C4-1.
Paper: 2020ApJ...901...93B (fulltext)
detected in the spectra, as illustrated for two objects in Figure 9 . In such instances, we simply excluded this narrow wavelength region from the fits. We are confident that ignoring the carbon opacity in our models only marginally impacts the derived values of and log g . Indeed, the carbon abundances measured in DOZ white dwarfs typically fall in the range −3.0 log C/He −2.0 (Dreizler Werner 1996 ; Reindl et al. 2014a ; Werner et al. 2014 ), and the corresponding opacity barely influences the He i and He ii line profiles at high temperatures according to the model-atmosphere calculations of Reindl et al. ( 2018 ). Figure 8. Best model-atmosphere fits to the optical spectra of DA white dwarfs with high signal-to-noise observations (S/N > 30). The observed and synthetic spectra, normalized to a continuum set to unity, are displayed as black and red lines, respectively. The fits are offset vertically from each other by 0.6 for clarity. The SDSS name, the spectral type, and the best-fitting atmospheric parameters are given for each object. The positions of the H i lines are marked by blue ticks. Figure 9. Same as Figure 8 , but for DO/DB white dwarfs. The positions of the He i and He ii lines are marked by green and magenta ticks, respectively. 4.2. Hybrid White Dwarfs Special care was needed to properly analyze the 127 stars in our sample exhibiting both hydrogen and helium spectral features. As indicated by Figure 5 , the great sensitivity of the spectrum to the chemical structure of the atmosphere can be exploited to discriminate between homogeneous and stratified distributions of hydrogen and helium (Bergeron et al. 1994 ; Manseau et al. 2016 ). To this end, we fitted the optical spectra of our hybrid white dwarfs with both our homogeneous and stratified model-atmosphere grids. The physical quantity used to measure the composition (the helium-to-hydrogen number ratio He/H in the homogeneous case, the fractional mass of the hydrogen layer q H in the stratified case) was treated as an additional free parameter in the optimization process. We then compared the two solutions and adopted the best-fitting one. Figure 10 contrasts the homogeneous and stratified solutions for four hybrid white dwarfs in our sample. Once again, while only the blue optical spectra are displayed here, redder wavelengths, up to λ = 7000 Å, were included in the fits as well. For each star, the parameters corresponding to the adopted chemical configuration are highlighted in red. The top two white dwarfs are relatively hot, in which situation the comparison rests largely on the He ii λ 4686 line and to a lesser extent on the He i λ 4471 line. We can safely assert that the first object has a homogeneous atmosphere, because our stratified models produce a He ii λ 4686 feature that is too shallow. Conversely, the second object bears the signature of a stratified atmosphere, the He ii λ 4686 profile predicted by our homogeneous models being too deep. The bottom two
Paper: 2017ApJ...835....8T (acknowledgments)
The authors would like to thank Diego Muñoz and Dong Lai for providing the results of their simulations and for many useful discussions. We thank Suzanne Hawley and the Astrophysical Research Consortium (ARC) for their generous allocation of ARCSAT commissioning time as well as Flynn Hasse and the WIYN 0.9 m synoptic observers, Stephen Gilliam, François Dufour, and William Romanishin. This work makes use of observations from the LCOGT network and observations obtained with Apache Point Observatory’s 0.5 m Astrophysical Research Consortium Small Aperture Telescope. B.M.T. acknowledges support from a Sigma Xi Grant-in-Aid of Research and the University of Wisconsin–Madison Graduate School.
Paper: 2018MNRAS.481L..49C (fulltext)
al. ( 2018 ) carried out polarimetric observations of the NEO (3200) Phaethon at very large phase angles. The analysis of their data led Devogèle et al. ( 2018 ) to conclude that Phaethon is most likely a collisional fragment of the B-class asteroid (2) Pallas, in particular a fugitive from Pallas’ family, confirming previous results based exclusively on spectral reflectance data by De León et al. ( 2010 ). The observations by Devogèle et al. ( 2018 ) and Ito et al. ( 2018 ) are also shown in Fig. 1 . We note that at phase angles ∼100° (not shown in Fig. 1 ), the two polarimetric datasets are not consistent: around phase angle 105°, polarization values from Ito et al. ( 2018 ) reach about 50 per cent while at phase angle 101°, Devogèle et al. ( 2018 ) measure only 38 per cent. An explanation for this discrepancy, possibly due to surface heterogeneity of Phaethon, is beyond the scope of this paper. However, at phase angles , it clearly appears that Bennu exhibits values of linear polarization much higher than those of Phaethon. Our measurements, as well as the very recent observations of (152679) 1998 KU2 (Kuroda et al. 2018 ) are the highest ever found for a NEO observed at large phase angles. Another Solar system object that exhibits such high-polarization values is comet C/1995 O1 (Hale–Bopp), which was observed by Manset Bastien ( 2000 ) in a 9 nm wide gas-free filter centred around λ = 694 nm. Hale–Bopp measurements are also shown in Fig. 1 . The resemblance between the polarimetric data of Bennu and those of Hale–Bopp is striking, and although reinforces the conclusion about similarity between F-type objects and cometary dust proposed by Kolokolova Jockers ( 1997 ), it should be treated with caution, due to the big differences between the two physical environments: in one case we have an obviously active and very large comet, in the other one a much smaller asteroid with no detection of a gas or dust coma. Our data show that the phase-polarization curve of Bennu is steeper than the extrapolation of phase-polarization curves of main-belt B-class asteroids observed at phase angles , and resembles that of F-class asteroids. At phase angles , the polarimetric behaviour of Bennu is probably in between that typical of the F and B classes. Dynamical modelling has suggested that Bennu has most likely have evolved from the Inner part of the Main Belt (IMB; a 2.5 au; Walsh et al. 2013 , and references therein) possibly within either the ‘New’ Polana or Eulalia families (Bottke et al. 2015 ). If this is true, taking into account the finding by Cellino et al. ( 2001 ) that most of Polana family’s members belong to the F-class, our polarimetry might support the hypothesis that Bennu originated in that family, since Eulalia is a C-class asteroid. Based on what is currently known about the similarity in polarimetric behaviour of the asteroid F-class and of some cometary bodies, (101955) Bennu could be compatible with cometary characteristics and a
Paper: 2019A&A...622A..82L (fulltext)
fit all transtions with K ′′ a up to 20 (excluding lines of K ′′ a = 18, which showed a clear trend in residuals between 197.48 and 221.45 GHz) for frequencies below 251 GHz. But in the range 310–506 GHz, the assignments appeared difficult in spite of larger intensities. As for other vibrational states of g - n -PrCN, the oblate pairing consisting of a -type and b -type transitions facilitated the assignments. However, the increasing separation of paired transitions with increase in quantum numbers made assignments more difficult combined with the blending and broadening in the high frequency range and the possible splits caused by internal rotations. Therefore very secure assignments with K ′′ a up to 4 or 5 (for each sub-branch) were first made. Even after the improvement of the parameters several lines at each K ′′ a could not be correctly fitted showing systematic residuals. All these lines are in the ranges of J ′′ from 68 to 73 and from 89 to 93. We attributed this as due coupling with other vibrational states. Finally 935 R -branch a -type transitions were identified in all four frequency ranges. The assignment of b -type transitions was not easy due to low intensities at low frequencies and blending and broadening at high frequencies. The confidently assigned b -type transitions at high frequency were all oblate paired with intense a -type transitions with K ′′ a less than 6. Totally, 487 b -type transitions were fitted. Q -branch b -type transitions were useful to correctly determine the parameter D K . We identified internal rotation causing splits (with the components separated by around 0.6 MHz) at frequencies between 89.25–126.75 GHz, in transitions with large J ′′. In this range, Q -branch b -type transitions have K ′′ a less than 11 and those that could be securely assigned have J ′′ less than 45. For Q -branch b -type transitions with K ′′ a between 13 and 18 in the range 171–251 GHz, only prolate pairs were assigned. These transitions have low J ′′ and hence are not affected by internal rotation. Finally 466 Q -branch b -type transitions were correctly fitted. Moreover, 110 hyperfine split transitions were fitted with fixed hyperfine parameters from v 30 = 1. These are R -branch a -type transitions with J ′′ approaching K ′′ a (less than 11), b -type transitions (both R -branch and Q -branch) with K ′′ a less than 4 at frequencies below 70 GHz. The derived parameters are shown in Table 6 . The fitted transitions are in Table 18 (available at the CDS), and those confidently assigned but not correctly fitted with systematic residuals are separated in Table 19 (available at the CDS). Using the newly derived molecular parameters, confident predictions of a -type transitions can be made up to J ′′ = 68 for K ′′ a ≤ 5 below 380 GHz, and J ′′ = 41 for K ′′ a ≤ 41 below 251 GHz (excluding lines of K ′′ a = 18 that show residuals that cannot be fitted). For b -type transitions, all R -branch predictions with K ′′ a ≤ 11 below 251 GHz
Paper: 2015A&A...584A..90G (acknowledgments)
This work would have been impossible without the vigorous work of Fernando Selman and George Hau, MUSE Paranal instrument scientists. We are grateful to Francesca Iannuzzi and Francesca Fragkoudi for very useful input and discussions on bars in simulated galaxies, particularly on box/peanuts. It is also a pleasure to thank Eric Emsellem for useful discussions on the inner components of barred galaxies and Michael Vlasov for his permission to use a chart from his atlas. We are very grateful to the anonymous referee for a thoughtful and constructive report. M.K.S. and J.F.-B. acknowledge support from grant AYA2013-48226-C3-1-P from the Spanish Ministry of Economy and Competitiveness (MINECO). J.F.-B. also acknowledges support from the DAGAL network from the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme FP7/2007–2013/ under REA grant agreement number PITN-GA-2011-289313. P.S.-B. acknowledges support from the Ramón y Cajal programme, grant ATA2010-21322-C03-02 from the Spanish Ministry of Economy and Competitiveness (MINECO). Based on observations made with ESO telescopes at the La Silla Paranal Observatory under programme ID 060.A-9313.
Paper: 2017ApJ...847..120H (fulltext)
et al. 2007 ), disk formation in the central parsec followed by a migration process (Levin 2007 ; Madigan et al. 2009 ), and rejuvenation of older stars by mergers or severe atmosphere alteration by envelope-stripping (Lee 1987 ; Genzel et al. 2003 ; Davies King 2005 ). A successful scenario should explain the young ages and randomly inclined orbits of the S-stars, via an efficient transport/relaxation mechanism. External formation followed by binary disruption is among the favorite scenarios, as it also explains some of the hyper-velocity stars that escape the Galaxy (e.g., Brown et al. 2009 ). However, the field binary disruption scenario requires some minimum time span to explain the thermal distribution of eccentricities in the S-star cusp (Perets 2009 ). This may be incompatible with the shorter main-sequence lifetime of some S-stars. While it is plausible to assume that S-stars were initially formed in an outer stellar disk in the central parsec, it has proved difficult to find an explanation of how their original angular momenta are strongly perturbed and later redistributed. Finally, many of the scenarios that suggested environmental mechanisms to significantly alter the photosphere of in fact old S-stars (Lee 1987 ; Alexander Morris 2003 ; Hansen milosavljević 2003 ; Davies King 2005 ) either have serious problems with explaining the S-stars’ stellar properties or are not elaborated in detail. For example, it is not clear how quickly after a major atmospheric perturbation a star will relax, and whether such products experience significant mixing (e.g., Gaburov et al. 2008 ). With all the aforementioned observational constraints and theoretical complexities the question of the origin and distribution of young stars in the GC has become one of the most remarkable issues in this field. Here, we perform quantitative spectroscopy of S-stars and obtain their individual stellar parameters. This enable us to locate these stars for the first time in a Hertzsprung–Russell (HR)-like diagram and provide new insights on their evolutionary stage and their puzzling origin. However, such an analysis demands a high signal-to-noise (), comparable to that achieved in spectra of nearby Galactic OB-stars. In this study, we co-add more than 100 hr of adaptive-optics (AO) assisted spectroscopic observations of the GC over twelve years, to construct ultra-deep spectra of eight S-stars. The paper is organized as follows: in Section 2 we list the observations used in this analysis and describe the reduction of the data to obtain deep spectra on a number of stars in the GC. Section 3 describes the stellar atmosphere models used to match the data and our fitting strategy . In the results section (Section 4 ), we present the spectral and luminosity (gravity) classification of the S-stars as well as parameters of individual stars based on the results of atmosphere model fits. In the final paragraph of the results section we compare the inferred stellar
Paper: 2017MNRAS.472.2869W (acknowledgments)
Acknowledgements We want to thank Antonio J. Cuesta, Benjamin Alan Weaver, and Jeremy Tinker of SDSS-III BOSS DR12 for the help on BAO fitting and weight assignment of QPM mocks. We also want to thank Yuting Wang, Gong-Bo Zhao and Bin Hu for helpful discussions. S-JW wants to thank Shuangpeng Sun, Shihong Liao, Junyi Jia, Zhen Jiang and Chunxiang Wang for the help on idl coding, and Wei-Ming Dai and Li-Wei Ji for the help on fortran coding during this work. S-JW also wants to thank the hospitality of Misao Sasaki when the work was finished during the visit at the Center for Gravitational Physics of Yukawa Institute for Theoretical Physics of Kyoto University. We acknowledge the use of the cluster PANGU and NOVA of NAOC. QG is supported by a Newton Advanced Fellowship, NSFC grant (No. 11573033, 11622325), the Strategic Priority Research Program The Emergence of Cosmological Structure of the Chinese Academy of Sciences (No.XDB09000000) and the Recruitment Program of Global Youth Expertsof China, the NAOC grant (Y434011V01). R-GC is supported in part by the Strategic Priority Research Program of the Chinese Academy of Science (CAS), Grant No.XDB09000000 and by a key project of CAS, Grant No.QYZDJ-SSW-SYS006 and by the National Natural Science Foundation of China under Grants No.11375247 and No.11435006.
Paper: 2017ApJ...847...12N (acknowledgments)
We thank the referee for a critical appraisal of this paper. R.N. was supported by Yale Astronomy's Dorrit Hoffleit Undergraduate Research Scholarship, Alice Peter Tan, and Yale-NUS College's Summer Independent Research Program (SIRP) while working on this research. P.O. acknowledges support by the Swiss National Science Foundation through the SNSF Professorship grant 157567 “Galaxy Build-up at Cosmic Dawn.” We are grateful to Akio Inoue for providing Monte Carlo realizations of the IGM transmission at high redshift. The primary data for this work were obtained with the Hubble Space Telescope operated by AURA, Inc., for NASA under contract NAS 5-26555. Support for this work was provided by NASA through grant HST -GO-13871 from the Space Telescope Science Institute, which is operated by AURA, Inc., under NASA contract NAS 5-26555. Some of the data presented in this paper were obtained from the Mikulski Archive for Space Telescopes (MAST). Facilities: HST (ACS , WFC3) .
Paper: 2020MNRAS.491L..56J (fulltext)
the UV magnitude and flux were measured by selecting a 5-arcsec source region and a larger background region avoiding any visible sources. Flux measurements were made using the uvotmaghist software. Flux measurements from individual observations are plotted in Fig. 2 (top panel), where orange circles indicate uvw1 (2253–2946 Å) and blue diamonds are uvw2 (1599–2256 Å). They show that in both the uvw1 and uvw2 filters the first observation measured a higher flux. The following four observations all measured a slightly lower flux by about 1–2 × 10 −13 and there was no significant variability between any of the four observations from 2019 July 13 to 2019 August 10. Figure 2. Top panel: Swift UVOT flux (×10 −13 erg cm −2 s −1 ) for each observation taken over 5 weeks. Observations were made using two filters. The uvw1 filter (orange circles) and uvw2 (blue diamonds). Bottom panel: the X-ray count rate over the five observations from 2019 July 7 to 2019 August 10 in the 0.3–10 keV energy range. There is no significant change in the X-ray count rate in contrast to the decline in the UV flux. X-ray count rates were extracted from each observation using the automatic XRT data builder (Evans et al. 2007 ). The X-ray light curve in Fig. 2 , bottom panel, shows no significant variability across all the observations, including the first one where there is a higher flux in the UV. Since there is very little variation in the X-ray light curve and hardness ratio, it appears the source of the X-rays is consistent across all the observations. Therefore it is possible to combine the X-ray data to produce a single spectrum that represents the source as shown in Fig. 3 . Figure 3. The combined X-ray spectrum with a total exposure time of 10 ks. The spectrum is fitted with a two-temperature apec model without absorption. Model parameters are given in Table 3 , column 4. 3 X-RAY SPECTRAL ANALYSIS Spectral files including ancillary response file ( arf ) and response matrix file ( rmf ) were generated using the XRT automatic analysis tools (Evans et al. 2009 ) and fitting was carried out using xspec . The spectrum has relatively few photons but shows a peak at ∼1 keV and very little flux above 2 keV that are characteristics of a stellar coronal spectrum (Güdel Nazé 2009 ). We therefore fit the spectrum with an apec plasma model (Smith et al. 2001 ) as shown by the red line in Fig. 3 . The low number of counts in the spectrum requires the use of the C-statistic when fitting, rather than standard χ 2 . Single and two-temperature models were compared, with results shown in Table 3 . Fitting with a two-temperature model without absorption gave an improved fit with a reduced χ 2 of 1.27 compared to the one-temperature model’s 1.42. The temperature of the cool plasma component is 0.31 keV that is consistent with the values found for the similar K5-type T Tauri star LkCa 15 (Skinner Güdel 2013 ). However the higher temperature plasma component is almost a factor of 2