adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017A&A...603A..73Y (fulltext)
For example, at the 0.75 bandwidth, the average depth of Night 1 and Night 3 in W2015 is 0.40% while it is 0.512% in our work. Barnes et al. (2016) also calculated the Na i D lines’ absorption depth and their result is 0.45% (average of Night 1 and Night 3) which is also larger than in W2015 but smaller than ours. The differences are probably due to the data reduction method, however, these differences are not significant considering the measurement uncertainties. When fitting the Na i absorption, we used light curve models which include the limb-darkening within the bandwidths while the other two papers use a box light curve model. As for the CLV effect, there is no obvious CLV feature in the light curve in W2015 (Fig. 3 of their paper), while the light curve in Barnes et al. ( 2016 , Fig. 6 of their paper) shows a similar CLV feature to that in our work. In general, the light curves in Barnes et al. (2016) and our work agree more closely than with W2015. Fig. 4 Observed light curves for the Mg i line (red lines). The black line is the CLV model. 4. Comparison of the CLV effect for HD 189733b and HD 209458b The actual CLV effect varies between different exoplanet systems. In order to illustrate and study the different manifestations of the CLV effect, we present the CLV of the HD 209458b system and compare it with the HD 189733b system. These two transiting exoplanet systems have bright host stars and have been extensively studied, thus they serve as typical samples for the study the CLV effect. We modelled the CLV effect of HD 209458b using the parameters shown in Table 2 . In order to compare the strengths of the CLV effect with the Na i absorption, we adopted the measured planetary absorption values from ( Snellen et al. 2008 , S2008). The results are shown in Fig. 5 and Table 4 . We note that the calculation in S2008 does not correct for the CLV effect. However, since the overall CLV contribution is relatively small (the value is at the noise level of their observation data), adopting the S2008’s value as the Na i absorption is justified in our study. Table 2 Parameters of the HD 209458b system. Notes. The transit epoch and the period are from Knutson et al. (2007) and all the other values are from Torres et al. (2008) . Fig. 5 Comparison of the CLV effect between HD 189733b and HD 209458b. Here the calculations are for the Na i D lines (average of and ). The first row is for HD 189733b and the second row is for HD 209458b. The three columns are for the bandwidths of 0.75, 1.5 and 3.0. The green lines are the modelled CLV light curves while the red lines are the Na i absorption light curves, and the black lines are the combination of the two. We note that the y axis scales are different for the two rows: the values for HD 189733b are significantly larger than for HD 209458b. In order to compare the two systems, we plot their CLV models together with the Na absorption at three different bandwidths in Fig. 5 . There are several
Paper: 2018MNRAS.481.5560S (acknowledgments)
ACKNOWLEDGEMENTS This research has made use of data obtained from the High Energy Astrophysics Science Archive Research Center (HEASARC), provided by NASA’s Goddard Space Flight Center. This research has made use of the NuSTAR Data Analysis Software ( nustardas ) jointly developed by the ASI Science Data Center (ASDC, Italy) and the California Institute of Technology (USA). RS and AJ acknowledge the financial support from the University Grants Commission (UGC), India, under the SRF and MANF-SRF schemes, respectively.
Paper: 2020ApJ...896L..44A (fulltext)
could be inaccurate if the system inclination is low or the pulsar’s companion is rapidly rotating (Freire et al. 2008 ). In sum, it is not clear if GW190814's secondary is a BH or an NS. GW190814 poses a challenge for our understanding of the population of merging compact binaries. In what follows, we estimate the merger rate density of the compact binary subpopulation represented by this source, investigate the nature of its secondary component and possible implications for the NS equation of state, discuss how the system may have formed, and study its implications for cosmology. 6.1. Merger Rate Density Given the unprecedented combination of component masses found in GW190814, we take the system to represent a new class of compact binary mergers, and use our analysis of its source properties to estimate a merger rate density for GW190814-like events. Following a method described in Kim et al. ( 2003 ), we calculate a simple, single-event rate density estimate according to our sensitivity to a population of systems drawn from the parameter-estimation posteriors. As in Abbott et al. ( 2020a ), we calculate our surveyed spacetime volume semianalytically, imposing single-detector and network S/N thresholds of 5 and 10, respectively (Tiwari 2018 ). The semianalytic for GW190814 is then multiplied by a calibration factor to match results from the search pipelines assuming a once-per-century FAR threshold. The sensitivity of a search pipeline is estimated using a set of simulated signals. For computational efficiency, this was done using preexisting search pipeline simulations and the mass properties were not highly optimized. However, given that we are estimating a rate based on a single source, the calibration errors are much smaller than the statistical errors associated with the estimate. The simulated sources were uniformly distributed in comoving volume, component masses, and component spins aligned with the orbital angular momentum. For O1 and O2, the simulated BH mass range was 5–100 M ⊙ , but for the first part of O3 we are analyzing here, the injected range was 2.5–40 M ⊙ (following our updated knowledge of the BH mass distribution); the NS mass range was 1–3 M ⊙ , and component spins are 0.95. As GW190814 occurred when LIGO Hanford was not in nominal observing mode, it is not included in the production P y CBC results, and we use G st LAL results to calculate the merger rate. We assume a Poisson likelihood over the astrophysical rate with a single count and we apply a Jeffreys prior to obtain rate posteriors. The analysis was done using samples from the Phenom PHM posterior and separately from the EOBNR PHM posterior, producing the same result in both cases. We find the merger rate density of GW190814-like systems to be . As a consistency check, we used the P y CBC search results to calculate an upper limit. Repeating the rate calculation with a P y CBC-based calibration and zero event count, we obtain an upper limit consistent (to
Paper: 2020MNRAS.494..983R (fulltext)
Distances for each of these endpoints are reported explicitly, as well as the stream width. A5 Hermus We base our endpoints for the Hermus stellar stream on the galstreams footprint, which is based on the discovery paper (Grillmair 2014 ). One endpoint is computed using the galstreams algorithm, while the other does not match with an end of the stream and is identified separately. Distances for each of these endpoints are reported explicitly in Grillmair ( 2014 ), as well as the stream width. A6 Hyllus We base our endpoints for the Hyllus stellar stream on the galstreams footprint, which is based on the discovery paper (Grillmair 2014 ). Both endpoints computed using the galstreams algorithm do not match with the ends of the stream footprint and were identified separately. Distances for each of these endpoints are reported explicitly in Grillmair ( 2014 ), as well as the stream width. A7 Kshir Discovered using Gaia DR2 and confirmed spectroscopically by Malhan et al. ( 2019 ), the Kshir stream is distinct from but highly correlated in phase space with GD-1. We use two spectroscopic member stars as anchor points, corresponding to the highest φ 1 overall and the lowest φ 1 star from SDSS. These stars were chosen due to how close they are to the best-fitting orbit of Kshir and their high separation from each other. We assume both anchors are at the uncertainty-weighted distance of the stream (10 kpc) with ±1 kpc uncertainty, while the angular uncertainties are both assumed to be 0.5 deg. A8 Kwando We base our endpoints for the Kwando stellar stream on the galstreams footprint, which is based on the discovery paper (Grillmair 2014 ). Both endpoints computed using the galstreams algorithm do not match with the ends of the stream footprint and were identified separately. Grillmair ( 2017b ) reports the FWHM of the stream as 22 arcmin (Gaussian σ = 0.16 deg) and a physical width of 130 pc, corresponding to a heliocentric distance of 20.3 kpc. We assume both anchor points are at this distance and that the distance uncertainty is 20 per cent. A9 Magellanic Stream While the Magellanic Stream has been studied with great detail in recent years (Nidever, Majewski Butler Burton 2008 ; Nidever et al. 2010 ), its sheer size and complexity does not easily lend to being described by anchor points. We opt for the anchor points adopted by Pawlowski et al. ( 2012 ), which correspond to overdensities identified by Brüns et al. ( 2005 ). We adopt the same 55 kpc distance for both anchor points, with a 20 per cent distance uncertainty and an angular uncertainty of 2 deg. A10 Murrumbidgee We base our endpoints for the Murrumbidgee stellar stream on the galstreams footprint, which is based on the discovery paper (Grillmair 2014 ). Both endpoints computed using the galstreams algorithm do not match with the ends of the stream footprint and were identified separately. Grillmair ( 2017b ) quotes a heliocentric distance of 20 kpc. We assume both anchor points are at this
Paper: 2019MNRAS.489.3565J (acknowledgments)
ACKNOWLEDGEMENTS The authors thank Yun-Young Choi, Harshil Kamdar, Juhan Kim, Joel Primack, and the anonymous referee for insightful discussion and feedback on our research. J.-h. K. acknowledges support by Research Start-up Fund for the new faculty of Seoul National University (SNU), and by Creative-Pioneering Researchers Program through SNU. This work was also supported by the National Institute of Supercomputing and Network/Korea Institute of Science and Technology Information with supercomputing resources including technical support, grants KSC-2018-S1-0016 and KSC-2018-CRE-0052. The CosmoSim data base used in this paper is a service by the Leibniz-Institute for Astrophysics Potsdam (AIP). The MultiDark data base was developed in cooperation with the Spanish MultiDark Consolider Project CSD2009-00064.
Paper: 2020MNRAS.491.4523F (acknowledgments)
ACKNOWLEDGEMENTS We thank A. El-Zant for his careful reviewing, F. Combes, A. Nusser, F. van den Bosch, O. Ginzburg, T. Nussbaum, Y. Birnboim, and M. Kretschmer for stimulating discussions and physical insights. This work was partly supported by the grants France-Israel International Projects of Scientific Collaboration (PICS), Israeli Centers of for Research Excellence (I-CORE) Program of the Planning and Budgeting Committee(PBC)/Israel Science Foundation (ISF) 1829/12, U.S-Israel Binational Science Foundation (BSF) 2014-273, National Science Foundation (NSF) AST-1405962, German-Israeli Foundation for Scientific Research and Development (GIF) I-1341-303.7/2016, and Deutsch-Israelische Projektkooperation (DIP) STE1869/2-1 GE625/17-1. NIHAO simulations were carried out at the Gauss Centre for Super-computing e.V. (GCS, www.gauss-centre.eu ) at the GCS Supercomputer SuperMUCat Leibniz Supercomputing Centre ( www.lrz.de ) and on the High Performance Computing resources at New York University Abu Dhabi.
Paper: 2018ApJ...854...64S (acknowledgments)
We thank Professors M-D Ding, H-M Wang, and Y-N Su for very helpful discussions. This work is supported by NSFC grants 11790304 (11790300), 11125314, 11773016, and 11533005, No. U1531247 and 41574166, the Recruitment Program of Global Experts of China, the Key Laboratory for Modern Astronomy and Astrophysics (Nanjing University), and the Max Planck Partner Group program. H.T. thanks ISSI and ISSI-BJ for the support to the team “Diagnosing heating mechanisms in solar flares through spectroscopic observations of flare ribbons.”
Paper: 2016A&A...588A..10R (acknowledgments)
The Liège team acknowledges support from the Fonds de Recherche Scientifique (FRS/FNRS) including especially an FRS/FNRS Research Project (T.0100.15), as well as through an ARC grant for Concerted Research Actions financed by the French Community of Belgium (Wallonia-Brussels Federation), and an XMM PRODEX contract (Belspo). Anthony Hervé is supported by grant 14-02385S from GA ČR.
Paper: 2018MNRAS.473..663M (fulltext)
profiles are extrapolated beyond the edge of the grey area, since no hard strong-lensing constraints are found at these distances. Here, a clear separation between models appears: Bradac (v2), CATS (v3.1), Zitrin-NFW (v3) and our fiducial model profiles tend to keep a high density at large radii, while glafic (v3), Sharon (v3) and our external-shear model generate a lower mass profile. At very large distances, the profile from Williams (v3) drops considerably lower than all others. These effects are seen in both panels of Fig. 12 . Figure 12. The integrated (upper panel), differential (middle panel) and ratio (lower panel) mass profiles of the cluster from different studies. The ratio is computed over our fiducial model, the one with clumps in the outskirts and only gold sample. The black line surrounded by the blue-shaded region represents the mass profile for the external shear and 3 statistical uncertainties. The black line surrounded by the shaded orange region represents mass profile for the fiducial model and the 3 statistical uncertainties. The grey area represents the region where multiple images used as constraints are located. The discrepancies in the inner core could be due to the new hard constraints we add to our mass models, while the discrepancies outside the multi-imaged region may be related to different aspects of the modelling technique and their sensitivity to environmental effects. By probing the overall discrepancies in the mass profiles between different analyses, we can begin to understand the magnitude of systematic uncertainties and their overall effects on the modelling. Compared to other parametric models (v3 from CATS, glafic and Sharon), our fiducial model reaches a similar, or slightly higher rms (0.6arcsec). The main difference is a higher ratio between the number of constraints ( k ) and the number of free parameters ( n ), which can be used as a metric on the level of constraints available. Thanks to the large number of spectroscopic redshifts available for the gold systems, we obtain k / n 134/304.5 compared to 70/302.33 for CATS (v3), and 146/1001.46 for glafic (v3) (Kawamata etal. 2016 ). The ratio between constraints and free parameters in our model is comparable to the Sharon (v3) model ( k / n 108/274, Sharon private communication), but they use multiple emission knots in each galaxy as constraints, making them not strictly independent. We note that our current model is able to accommodate a large number of constraints with only a small number of free parameters, which means that we are reaching a limit (in rms) with this method. 6.4 Estimating the level of systematics in mass models Our fiducial model, containing mass clumps in the outskirts, constrained with the gold set of multiple images and released as CATS (v4) as part of the FFs model challenge, shows a statistical error of stat 1percent on the mass density profile in the cluster core (see Fig. 12 and Section 6.3 ). This error is comparable to
Paper: 2019MNRAS.483.2347D (acknowledgments)
ACKNOWLEDGEMENTS We thank the anonymous referee for providing useful suggestions that improved the paper. This research was supported in part by Lilly Endowment, Inc., through its support for the Indiana University Pervasive Technology Institute and for the Indiana MAETACyt Initiative. This work was also supported in part by the NASA Origins of Solar Systems grant NNX08AK36G. This material is based on work supported by the National Science Foundation under Grant No. CNS-0521433 and CNS-0723054.
Paper: 2016A&A...591A..95F (acknowledgments)
DF acknowledges support from the Italian Ministry of Science and Education (MIUR), project SIR (RBSI14ZRHR). The authors would like to thank the WISH team and L. Podio for providing the HIFI CO and CO –9 spectra of TW Hya and HD 163296. We are also grateful to the DIGIT and GASPS teams for providing the PACS spectra of CO. Astrochemistry in Leiden is supported by the Netherlands Research School for Astronomy (NOVA), by a Royal Netherlands Academy of Arts and Sciences (KNAW) professor prize, and by the European Union A-ERC grant 291141 CHEMPLAN.
Paper: 2019ApJ...880...21M (acknowledgments)
T.J.M. is supported by the Grants-in-Aid for Scientific Research of the Japan Society for the Promotion of Science (JP17H02864, JP18K13585). This work was supported by the Australian Research Council through ARC Future Fellowships FT160100035 (BM), Future Fellowship FT120100363 (AH), and by STFC grant ST/P000312/1 (BM). C.C. was supported by an Australian Government Research Training Program (RTP) Scholarship. Sergei Blinnikov is supported by RSCF 19-12-00229 on the development of the STELLA code. Numerical computations were in part carried out on PC cluster at the Center for Computational Astrophysics, National Astronomical Observatory of Japan. This research was undertaken with the assistance of resources obtained via NCMAS and ASTAC from the National Computational Infrastructure (NCI), which is supported by the Australian Government and was supported by resources provided by the Pawsey Supercomputing Centre with funding from the Australian Government and the Government of Western Australia. A.H. has been supported, in part, by a grant from the Science and Technology Commission of Shanghai Municipality (grants No. 16DZ2260200) and the National Natural Science Foundation of China (grants No. 11655002). Software: Kepler (Heger Woosley 2010 ), Arepo (Springel 2010 ), STELLA (Blinnikov et al. 1998 , 2000 , 2006 ), CoCoNuT-FMT (Müller et al. 2010 ; Müller Janka 2015 ).
Paper: 2018ApJ...856...69D (acknowledgments)
The authors thank Peter Berhoozi, Ferah Munshi, and Aldo Rodriguez for providing the stellar mass–halo mass tables. We thank Marla Geha for valuable discussions and ideas. Support from NSF grants AST-1312376 and AST-1613582 is gratefully acknowledged.
Paper: 2019MNRAS.482.5125H (acknowledgments)
ACKNOWLEDGEMENTS We thank J. Stott, M. Swinbank, C. Mason, O. Turner, and N. Leethochawalit for kindly sharing their measurements of galaxy properties. We thank the referee for her/his constructive comments that have helped us to improve the quality of this paper. C-LH acknowledges support from the Harlan J. Smith Fellowship at the University of Texas at Austin. The Flatiron Institute is supported by the Simons Foundation. TY acknowledges fellowship support from the Australian Research Council Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D), through project number CE170100013. CAFG was supported by NSF through grants AST-1412836, AST-1517491, AST-1715216, and CAREER award AST-1652522, by NASA through grant NNX15AB22G, and by a Cottrell Scholar Award from the Research Corporation for Science Advancement. Support for PFH was provided by an Alfred P. Sloan Research Fellowship, NASA ATP Grant NNX14AH35G, and NSF Collaborative Research Grant #1411920 and CAREER grant #1455342. DK was supported by NSF grant AST-1715101 and the Cottrell Scholar Award from the Research Corporation for Science Advancement. AW was supported by NASA through grants HST-GO-14734 and HST-AR-15057 from STScI. The numerical calculations were run on the Caltech compute cluster ‘Zwicky’ (NSF MRI award #PHY-0960291), allocations TG-AST120025 and TG-AST130039 granted by the Extreme Science and Engineering Discovery Environment (XSEDE) supported by the NSF, and allocation PRAC NSF.1713353 supported by the NSF. The authors acknowledge the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for providing HPC resources that have contributed to the research results reported within this paper. URL: http://www.tacc.utexas.edu.
Paper: 2019MNRAS.488..295S (acknowledgments)
ACKNOWLEDGEMENTS Data analysis was performed using the high performance computing cluster POLARIS at the Korea Astronomy and Space Science Institute. This research made use of topcat and stil : Starlink Table/VO Table Processing Software developed by Taylor ( 2005 ) and also the Code for Anisotropies in the Microwave Background ( camb ; Lewis, Challinor Lasenby 2000 ; Howlett et al. 2012 ). Srivatsan Sridhar would also like to thank Sridhar Krishnan, Revathy Sridhar, and Madhumitha Srivatsan for their support and encouragement during this work.
Paper: 2019ApJ...876....6M (acknowledgments)
D.M., H.V., and B.P. acknowledge a postdoctoral fellowship by grant CONACyT Ciencia Básica 255167. H.M.V. acknowledges grant DGAPA-PAPIIT IN109116; O.V. and S.R.-F. acknowledge grant DGAPA-PAPIIT IN112518, Fronteras de la Ciencia CONACyT 281; D.M.C. acknowledges grant DGAPA-PAPIIT IN105916, etc. We acknowledge the use of the Atocatl supercomputer at the Instituto de Astronomía of the Universidad Nacional Autónoma de México (UNAM) and DGTIC-UNAM for the use of Miztli, as well as the ABACUS supercomputer, where the simulations were performed. This paper is dedicated to the loving memory of our friend and colleague, Dr. Bárbara Pichardo, who first triggered the dynamical study of spiral arms in Mexico.
Paper: 2015ApJ...804..138H (acknowledgments)
We acknowledge the support of the staff of the Lijiang 2.4 m telescope. Funding for the telescope has been provided by Chinese Academy of Sciences (CAS) and the People’s Government of Yunnan Province. This research is supported by the Strategic Priority Research Program—the Emergence of Cosmological Structures of the Chinese Academy of Sciences, grant No. XDB09000000, by the NSFC through NSFC-11173023, -11133006, -11233003, -11473002, and by the Israel–China ISF-NSFC grant 83/13.
Paper: 2018MNRAS.479..332B (acknowledgments)
ACKNOWLEDGEMENTS I thank Charlie Conroy and Dan Weisz for insightful comments on earlier versions of this paper, Michael Fall, Kristian Finlator, Oleg Gnedin, Pawan Kumar, Smadar Naoz, Eliot Quataert, Charli Sakari, Chris Sneden, and Eros Vanzella for helpful discussions, and Tom Petty for aural support. Support for this work was provided by The University of Texas at Austin, the National Science Foundation (grant AST-1517226), and NASA through grant NNX17AG29G and HST grants AR-12836, AR-13888, AR-13896, GO-14191, and AR-14282 awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS5-26555. Much of the analysis in this paper relied on the python packages NumPy (Van Der Walt, Colbert Varoquaux 2011 ), SciPy (Oliphant 2007 ), Matplotlib (Hunter 2007 ), and iPython (Pérez Granger 2007 ); I am very grateful to the developers of these tools. This research has made extensive use of NASA’s Astrophysics Data System ( http://adsabs.harvard.edu/ ) and the arXiv eprint service ( http://arxiv.org ).