adsabs/WIESP2022-NER huggingface dataset:
Paper: 2021ApJ...907...19K (fulltext)
y ∈ [−50, 50] L 0 yieldswhich is in good agreement with the growth rate shown in Figure 16 . Evidently, the growth rate for this instability derived in the Boussinesq regime, which is admittedly inapplicable in our simulation, produces values that are in good agreement with our simulation results. Thus, both the fastest growing parallel and perpendicular wavenumbers and growth rates of the instability observed in our simulation are in reasonable agreement with theoretical predictions of these same quantities in the undular instability. 3.6. Interaction of the Lobes The emergence of magnetic lobes was found by Archontis et al. ( 2013 ) to result in significant reconnection and a jet-like disturbance in the corona. In our simulation, we see a similar event around t = 820 t 0 that may affect the linear evolution of the undular mode, which stops around the same time. Figure 17 shows the magnetic energyand kinetic energyin the corona as a function of time. The decrease in the magnetic energy that starts around t = 824 t 0 (green line) corresponds approximately to the same magnitude increase in the kinetic energy which peaks at t = 864 t 0 (blue line). In Figure 18 , we plot a series of field lines, seeded along a vertical line at ( x = y = 0, z > 0) in the corona on top of vertical cuts of the vertical velocity V z . The field lines are colored by B z . Early in the interaction, at t = 824 t 0 , the field lines only barely extend above the photosphere. They slowly rise, and it is clear that by t = 860 t 0 , the two primary lobes are interacting, and field lines with opposite signs of B z begin to reconnect. This creates a significant upflow, as the newly reconnected concave up field lines release their tension. This upflow is only seen low down in the corona before t = 860 t 0 , and with much smaller magnitude, since the field is not significantly expanding into the corona, but by t = 880 t 0 and t = 900 t 0 there is a clear upflow visible in the corona along with a concave-down arcade overlying the interacting loops. This type of event likely represents an Ellerman bomb, which is associated with this type of topology (Pariat et al. 2004 , 2006 , 2009 ; Isobe et al. 2007 ; Archontis Hood 2009 ; Danilovic 2017 ). Figure 17. Magnetic energy (black) and kinetic energy (red) in the corona as a function of time. The green (blue) line at t = 824 t 0 ( t = 864 t 0 ) shows the local maximum of the magnetic (kinetic) energy. Figure 18. Field lines colored by B z overplotted on a vertical plane of V z and a photospheric magnetogram at several times before and during the reconnection event. 4. Conclusions In this paper, we simulated the buoyant rise and emergence of an untwisted toroidal flux rope from deep in the convection zone. The flux rope is able to rise coherently through the convection zone, emerge through the photosphere, and form a relatively large (≈40 Mm across) active region. The emergence mechanism of our flux rope is identified to be the undular
Paper: 2019ApJ...872..145J (acknowledgments)
We thank the US National Science Foundation for its financial support through grant NSF AST-1518057, and NASA for its financial support through the WFIRST Preparatory Science program, grant No. NNX15AJ79G. This work is based in part on observations made with the NASA/ESA Hubble Space Telescope , obtained [from the Data Archive] at the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555. These observations are associated with program #14201. Support for program #14201 was provided by NASA through a grant 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. T.J. thanks the LSSTC Data Science Fellowship Program; her time as a Fellow has benefited this work. We thank an anonymous referee for their helpful review, and Chun Ly for pointing out a possible systematic effect of recently revised atomic physics on our results. This work has made use of data from the Sloan Digital Sky Survey (SDSS). Funding for the Sloan Digital Sky Survey IV has been provided by the Alfred P. Sloan Foundation, the U.S. Department of Energy Office of Science, and the Participating Institutions. SDSS-IV acknowledges support and resources from the Center for High-Performance Computing at the University of Utah. The SDSS website is www.sdss.org.SDSS-IV and is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS Collaboration including the Brazilian Participation Group, the Carnegie Institution for Science, Carnegie Mellon University, the Chilean Participation Group, the French Participation Group, Harvard-Smithsonian Center for Astrophysics, Instituto de Astrofísica de Canarias, The Johns Hopkins University, Kavli Institute for the Physics and Mathematics of the Universe (IPMU)/University of Tokyo, Lawrence Berkeley National Laboratory, Leibniz Institut für Astrophysik Potsdam (AIP), Max-Planck-Institut für Astronomie (MPIA Heidelberg), Max-Planck-Institut für Astrophysik (MPA Garching), Max-Planck-Institut für Extraterrestrische Physik (MPE), National Astronomical Observatories of China, New Mexico State University, New York University, University of Notre Dame, Observatário Nacional/MCTI, The Ohio State University, Pennsylvania State University, Shanghai Astronomical Observatory, United Kingdom Participation Group, Universidad Nacional Autónoma de México, University of Arizona, University of Colorado Boulder, University of Oxford, University of Portsmouth, University of Utah, University of Virginia, University of Washington, University of Wisconsin, Vanderbilt University, and Yale University.
Paper: 2018MNRAS.479.3996H (acknowledgments)
ACKNOWLEDGEMENTS Judith Biernaux acknowledges the support of the F.R.I.A. fund of the F.N.R.S.
Paper: 2017ApJ...840....4A (acknowledgments)
We acknowledge useful comments from an anonymous referee and discussions with Harry Warren, Jim Klimchuk, Karel Schrijver, Amy Winebarger, and Craig Deforest. Part of the work was supported by the NASA contracts NNG04EA00C of the SDO /AIA instrument and NNG09FA40C of the IRIS mission. Note added in proof. While this manuscript went into print, we were informed that work in progress shows that no clear evidence of observed features smaller than about 3.5–4.5 Hi-C pixels (≈250–330 km) was found in an ongoing study, using a (Fourier-based) noise-gating technique (Craig DeForest, private communication, 2017). In our view, this corroborates our result of a lower loop width cutoff w min below the peak (or most frequent) width of w p = 550 km.
Paper: 2019A&A...621A..79U (fulltext)
of the observations. Notes .The airmass column contains the value at the start and end of the observations. The seeing value is corrected for the airmass and corresponds to the seeing at the zenith. Table 2. Comparison of the modelling parametrization of the telluric correction methods. Notes .Molecfit and TelFit packages allow for fitting the atmospheric transmission to the target spectrum. A summary of the capabilities and models employed by each package is shown in Table 2 . Appendix A presents the spectra of the telluric standard and the synthetic transmission spectra obtained with each of the packages. 3.1. TelFit The Python package TelFit ( Gullikson et al. 2014 ) models the atmospheric transmission and fits it to a spectrum with the Levenberg–Marquardt algorithm. TelFit can adjust the wavelength solution of the data to the telluric lines or vice versa with a polynomial function. The order of the polynomial can be set by the user, but the values of the coefficients have to be directly set in the code. TelFit offers a parametric model of the line shape through a Gaussian function and a non-parametric model with the singular value decomposition mode. For each observation, TelFit uses, by default, an average atmospheric profile for mid-latitudes. The website of the Global Data Assimilation System (GDAS) 6 enables the use of a custom atmospheric profile, adapted to the location and time of the observations. The GDAS profiles are available for observations taken since 2004 and are updated every three hours. We used the atmospheric profile that was closest to our observations, with a time difference inferior to 2 h, to first fit the spectrum of the standard star. We experimented with the parametric and non-parametric options for the line shape and attained the same level of correction. Since the other models studied in this work use parametric correction, and to provide a more meaningful comparison, we selected the parametric correction. To perform the fit of the standard star, we left the parameters of the wavelength solution, the continuum level, line shape, resolution, and the mixing ratio of the molecule relevant for the wavelength range considered (H 2 O or O 2 ) as free parameters. TelFit does not allow a different parametrization of spectral orders separated by wavelength gaps. Therefore, each of the three CRIRES detectors is treated independently because the parameters of the wavelength solution differ from one detector to the other. As such, for each fit we obtain a different value for the following parameters: molecule abundances, resolution, temperature, and pressure profiles. We homogenize these parameters by taking their mean values weighted by the inverse of the χ 2 calculated for each detector. Then, we use the homogenized values of the parameters to compute a final atmospheric transmission for each detector. We divide the stellar spectrum by the atmospheric transmission to correct for the telluric lines. After the
Paper: 2019MNRAS.490.3234N (acknowledgments)
ACKNOWLEDGEMENTS DN would like to thank Kate Rubin, Gwen Rudie, and Alice Shapley for insightful discussions and suggestions, as well as the anonymous referee for a constructive report. SG, through the Flatiron Institute, is supported by the Simons Foundation. The primary TNG simulations were realized with compute time granted by the Gauss Centre for Supercomputing (GCS): TNG50 under GCS Large-Scale Project GCS-DWAR (2016; PIs Nelson/Pillepich), and TNG100 and TNG300 under GCS-ILLU (2014; PI Springel) on the GCS share of the supercomputer Hazel Hen at the High Performance Computing Center Stuttgart (HLRS). GCS is the alliance of the three national supercomputing centres HLRS (Universität Stuttgart), JSC (Forschungszentrum Jülich), and LRZ (Bayerische Akademie der Wissenschaften), funded by the German Federal Ministry of Education and Research (BMBF) and the German State Ministries for Research of Baden-Württemberg (MWK), Bayern (StMWFK), and Nordrhein-Westfalen (MIWF). Additional simulations were carried out on the Draco and Cobra supercomputers at the Max Planck Computing and Data Facility (MPCDF).
Paper: 2018ApJ...853...63D (acknowledgments)
J.L.D. acknowledges support for this work provided by NASA through Hubble Fellowship grant HF-51303.01 awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., for NASA, under contract NAS5-26555. B.D.S. acknowledges support from the National Aeronautics and Space Administration (NASA) through Einstein Postdoctoral Fellowship Award Number PF5-160143 issued by the Chandra X-ray Observatory Center, which is operated by the Smithsonian Astrophysical Observatory for and on behalf of NASA under contract NAS8-03060. C.L. acknowledges support from NSF grant AST-1004583. Finally, we thank the anonymous referee for discussions and comments that improved the paper.
Paper: 2015A&A...576A..81S (acknowledgments)
We wish to acknowledge the referee for his very fruitful comments that improved our work. We thank C. Brogan for supplying us with the VLA 330 MHz image. This research has been funded by Argentina grants ANPCYT-PICT 0795/08 and 0571/11. G.C. is Member of the Carrera of Investigador Científico of CONICET, Argentina. L.S. is a Ph.D. Fellow of CONICET, Argentina. The GOODS-Herschel data was accessed through the Herschel Database in Marseille (HeDaM – http://hedam.lam.fr ) operated by CeSAM and hosted by the Laboratoire d’Astrophysique de Marseille. We thank the staff of the GMRT, who have made these observations possible. GMRT is operated by the National Centre for Radio Astrophysics of the Tata Institute of Fundamental Research. This work made use of the High Performance Computing facility, funded by grant 12-RD-TFR-5.02-0711, at National Centre for Radio Astrophysics of the Tata Institute of Fundamental Research.
Paper: 2018MNRAS.480.5286Y (acknowledgments)
ACKNOWLEDGEMENTS PMY thanks the University of Tasmania for a Dean’s Summer Research Studentship and an Australian Postgraduate Award. SSS and MGHK thank the Australian Research Council for an Early Career Fellowship (DE130101399). We also thank an anonymous referee for his useful comments. This research was carried out using the Tasmanian Partnership for Advanced Computing high-performance computing clusters.
Paper: 2017MNRAS.467.1734K (fulltext)
given by We define z ph to be where the scattering optical depth s 1 (where subscript s denotes scattering) and set f cor / f cor,0 1. Near the photosphere, magnetic pressure begins to play a major role in hydrostatic equilibrium (e.g. Blaes, Hirose Krolik 2007 ), and near the effective photosphere, the gas temperature begins to diverge from the radiation temperature, so we acknowledge that the assumptions underlying our scheme do not reflect the detailed physics in this region. But since our goal is only to calculate spectra, for optical depths s 1, the accuracy of this scheme is not important. We can assess the validity of this scheme in the region s 1 by comparing the flux from spectral calculations with the intended flux given by equation ( 9 ), or, equivalently, by comparing the corresponding Eddington ratios. In Section 3.1 , we make this comparison for each set of scaling parameters we use and find that they generally agree to within 10percent. More importantly, we find that normalizing the spectra at different radii so that their corresponding Eddington ratios match has a negligible impact on the observed spectrum when contrasted with the discrepancies between spectral calculations with and without bulk velocities. In other words, because the potential error is significantly less than the effect we are measuring, our scaling scheme is adequate. These are the appropriate equations for scaling data to a different set of fundamental shearing box simulation parameters, in particular, z , x v y and . If we substitute in equation ( 9 ) for , we can alternatively regard F as a fundamental parameter instead of . Shearing box scalings in terms of F are given in Appendix C . This substitution is useful in order to scale to a different set of fundamental accretion disc parameters, since it is straightforward to express F in terms of accretion disc radius, mass and accretion rate. The scalings for z , x v y and F for both Newtonian and Kerr discs, allowing for a non-zero stress inner boundary condition, are given in Appendix D . The final scalings for , T g , v and v s in terms of fundamental accretion disc parameters are given in Appendix E . We only use Kerr scalings for our spectral calculations, but the Newtonian scalings are potentially useful for the purpose of comparing with other works in which Newtonian parameters are used and also for developing physical intuition. 2.3 Dependence of turbulent Comptonization on radius To characterize the contribution of turbulent Comptonization, we must model spectra at multiple radii. Our choice of radii is guided by the scaling of the ratio of bulk to thermal electron energies. We estimate this effect for a disc with no spin and a stress-free inner boundary condition with the Newtonian scalings in Appendix E . The bulk velocity scaling is The photosphere thermal velocity scaling is The scaling for the ratio of bulk velocity to thermal velocity at the photosphere is We also calculate the scaling for
Paper: 2021MNRAS.504.2707G (fulltext)
observations of the six targets were obtained between 2016 and 2020 using a variety of instruments: X-shooter on the Very Large Telescope (VLT; Vernet et al. 2011 ) observatory at Cerro Paranal, Chile; the Intermediate-dispersion Spectrograph and Imaging System 1 (ISIS) on the William Herschel Telescope (WHT) at Roque de los Muchachos Observatory (ORM) in La Palma, Spain; the Optical System for Imaging and low-Intermediate-Resolution Integrated Spectroscopy (OSIRIS; Sánchez et al. 2012 ) on the Gran Telescopio Canarias (GTC) at the ORM; the High Resolution Echelle Spectrometer (HIRES; Vogt et al. 1994 ) on the 10-m Keck I telescope at Mauna Kea Observatory, Hawaii; the Magellan Echellette (MagE) spectrograph on the 6.5-m Baade Magellan telescope at Las Campanas Observatory, Chile; and the FAST spectrograph on the 1.5-m Fred Lawrence Whipple Observatory telescope on Mount Hopkins, Arizona (Fabricant et al. 1998 ). The observation log is reported in Table 1 , and sample spectra are displayed in Figs A1 and A2 in the online appendix. Table 1. Log of spectroscopic observations. X-shooter observations were carried out using a 1-arcsec slit aperture for the ultraviolet (UV)-blue (UVB) arm and 0.9 arcsec for the visible (VIS) arm. In all cases flux in the NIR arm was insufficient for any meaningful analysis. On average the resolving power of our X-shooter spectra is R ≃ 4000–5500 in the UVB arm and R ≃ 6500–8000 in the VIS arm. All data were reduced using the standard procedures within the reflex 2 reduction tool developed by ESO. Telluric lines removal was performed on the reduced spectra using molecfit (Kausch et al. 2015 ; Smette et al. 2015 ). ISIS observations were obtained using the R600B and R600R gratings, in the ISIS blue and red arms, respectively; two central wavelength settings were used for each arm, nominally 3930 and 4540 Å in the blue and 6562 and 8200 Å in the red, which were chosen to cover important atmospheric lines, such as the Balmer series and Ca HK, as well as the Ca ii emission triplet in addition to other potential emission features (e.g. Fe ii /Mg i 5175 Å). The slit width varied between 1 and 1.5 arcsec depending on the observing conditions and we employed a binning of 2 × 2, resulting in an average resolution of ≈2 Å. ISIS spectra were debiased and flat-fielded using the standard starlink 3 packages kappa, figaro , and convert . We carried out optimal spectral extraction using the package pamela . Wavelength and flux calibration were performed using the routines within the software molly 4 (Marsh 1989 ). The OSIRIS spectroscopy of WD J0846+5703 was obtained using the R2500I grism with a 0.6-arcsec slit, and binning the detector 2 × 2. This set-up provides wavelength coverage over Å at a resolution of R ≃ 2500. The data were bias and flat-field corrected using the standard tools within iraf . 5 We performed sky background subtraction and optimal spectral extraction (Horne 1986 ) using the pamela data reduction
Paper: 2018ApJ...857L...8S (fulltext)
host mass and distance is shown in Figure 7 , as well as the limits on θ E , π E , and the lens flux. We infer that the host is a dwarf, likely in the Galactic bulge at kpc. Using the mass ratio and scaled instantaneous projected separation from the model, we find that the planet mass is , and it orbits its host beyond the snowline at a projected separation of au. The estimated physical properties are summarized in Table 2 . Figure 7. Posterior probability distributions of the host mass and distance derived from the Bayesian analysis. Also shown are the limits on the lens flux (blue), on θ E for either bulge or far disk sources (red), and on the microlens parallax for a bulge source (green). Table 2 Physical Properties One of the main uncertainties in our Galactic model is the source distance. However, we note the the posterior probability for the lens mass (and thus the planet mass) is weakly dependent on the source distance. For a source at 8 kpc the mass is only 10% lower than for a source at 15 kpc, well within the range of our uncertainty. The lens distance, and consequently the projected separation between the planet and its host, is more sensitive to the source distance, with a difference of 45% between a bulge source and a far disk source. Future observations can resolve this and give a better estimation of the source distance, as we discuss below in Section 7 . 6. Extinction Law Recent studies of dust properties in the inner bulge suggest deviations from the standard extinction law (Nataf et al. 2016 ; Alonso-García et al. 2017 ). The extinction coefficient measured in our field, , is lower than the value of 1.37 from Nishiyama et al. ( 2009 ). This offset is a disconcertingly large ∼0.4 mag in K S . The offset relative to the value of predicted by Fitzpatrick ( 1999 ) is even larger—about 1.4 mag in K s . The magnitude of the RC can also be predicted in the YJ bands by assuming the color–color relations from Nataf et al. ( 2016 ) and the extinction coefficients from Fitzpatrick ( 1999 ). These are Y RC ≈ 14.63 + 1.63 + (7.35–3.12) ≈ 20.49 and J RC ≈ 14.63 + 1.63 + (5.44–3.12) ≈ 18.58, where we add the corresponding intrinsic color offsets and reddening offsets, respectively, to the observed clump H -band magnitude. Given that the limiting magnitudes for the DECam and VVV data sets are, respectively, Y ∼ 21.2 and J ∼ 19.4 (see Section 4.2 above), the RC should be clearly detected in each of these bandpasses by a wide margin of 0.7–0.8 mag, respectively. However, the photometry in both cases barely covers the RC. Figure 8 shows the HK s CMD of our field (gray points), with the sources detected in Y shown as yellow circles in the left panel, and the points detected in J shown as purple in the right panel. We conclude that the extinction toward this field is steeper than standard (as defined by Fitzpatrick 1999 ). Figure 8. Detection limits of DECam Y -band data (left) and VVV J -band data (right). These limits are brighter than
Paper: 2019MNRAS.483..540L (acknowledgments)
ACKNOWLEDGEMENTS We thank the anonymous referee for the useful report which helped to improve the quality of the paper. We acknowledge Gerrit Schellenberger for the help and fruitful discussion. LL and THR acknowledge support from the German Research Association (DFG) through the Transregional Collaborative Research Centre TRR33 ‘The Dark Universe” (project B18) and support from the German Aerospace Agency (DLR) with funds from the Ministry of Economy and Technology (BMWi) through grant 50 OR 1514. LL also acknowledges support from NASA through contract NNX17AD83G.
Paper: 2019A&A...623A.138O (acknowledgments)
This work was partially supported by FONDECYT trough grant No 1181158, the Advanced Center for Electrical and Electronic Engineering (AC3E, Proyecto Basal FB0008), Núcleo Milenio de Formación Planetaria – NPF and Programa de Iniciación a la Investigación Científica (PIIC) de la Dirección de Postgrado UTFSM convenio No 015/2018. Michel Curé thanks the support from the Centro de Astrofísica de Valparaíso and the Centro Interdisciplinario de Estudios Atmosféricos y Astroestadística.