adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017ApJ...834..201L (fulltext)
28, 40, 56, 79, 111, 155, and where is the rms noise in Stokes I . Green line segments: magnetic field orientation (perpendicular to polarization orientation). Grayscale background: polarization fraction (%) shown with a power law (). The orange double-headed arrow shows the outflow orientation. The small, black, double-headed arrow in the bottom-right of each synthetic observation (right-hand panels) indicates the average (unweighted) magnetic field orientation. In the top row of Figure 2 , which shows the face-on view of the magnetic field, there is no clear overall magnetic field orientation. In contrast, edge-on views from the y - and x -directions (middle and bottom rows of Figure 2 , respectively) show that the magnetic field morphology is vertical when viewed from both of these orthogonal edge-on views. We have produced additional edge-on synthetic observations using intermediate values of θ (not pictured here). Due to the initial azimuthal symmetry of the simulation and the fact that the initial magnetic field is in the z -direction, we find that all edge-on views look very similar. There are also hints of the characteristic “hourglass” morphology in the edge-on views. This is similar to observations of hourglass-shaped fields in sources such as NGC 1333-IRAS 4A (Girart et al. 2006 ) and L1157 (Stephens et al. 2013 ; Hull et al. 2014 ), where gravitational collapse of a strongly magnetized protostellar core is expected to pinch magnetic field lines. 3.1. Polarization Fraction To explore projection effects, we render projected energy density maps and synthetic polarization maps at viewing angles and , where corresponds to the face-on view and corresponds to the edge-on view from the y -direction. Figure 3 shows the orthogonal viewing angles along and z . Figure 4 shows the views from , , and (top to bottom). We present the synthetic dust polarization maps in the left panel with their corresponding energy density projections on the right. Figure 3. From top to bottom: the x – y plane (face-on), x – z plane, and y – z plane (both edge-on) viewed from z , y , and x directions respectively. Left: synthetic dust polarization maps of S2.5 at , plotted in the same manner as the right-hand panels in Figure 2 . The outflow direction was not discernible in the face-on view. Right: the corresponding energy density, where high energy densities in pink indicate the location of the outflow. Figure 4. Same as Figure 3 , but for intermediate viewing angles , and , where where corresponds to the face-on view from the z -direction, and corresponds to the edge-on view from the y -direction. We calculate the average value of polarization fraction for each dust polarization map by averaging the magnitude of the polarized intensity. The average polarization fraction, , is sensitive to the weighting scheme used to derive it. Also, the locations of the peak intensities of the total and polarized emission are not the same for every source (this is the case for
Paper: 2016ApJ...817..135L (acknowledgments)
This paper has gone through internal review by the DES collaboration. We thank the anonymous referee for comments and suggestions that improved the paper. We also thank Helmut Jerjen and Marcel Pawlowski for providing the original VPOS coordinates. T.S.L. thanks Jonathan Hargis, Steven Boada, Daniel Nagasawa, and Katelyn Stringer for very helpful conversations. E.Ba. acknowledges financial support from the European Research Council (ERC-StG-335936, CLUSTERS). This research made use of Astropy, a community-developed core Python package for Astronomy (Astropy Collaboration et al. 2013 ). Funding for the DES Projects has been provided by the U.S. Department of Energy, the U.S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Center for Cosmology and Astro-Particle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas AM University, Financiadora de Estudos e Projetos, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Científico e Tecnológico and the Ministério da Ciência, Tecnologia e Inovação, the Deutsche Forschungsgemeinschaft, and the Collaborating Institutions in the Dark Energy Survey. The Collaborating Institutions are Argonne National Laboratory, the University of California at Santa Cruz, the University of Cambridge, Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas-Madrid, the University of Chicago, University College London, the DES-Brazil Consortium, the University of Edinburgh, the Eidgenössische Technische Hochschule (ETH) Zürich, Fermi National Accelerator Laboratory, the University of Illinois at Urbana-Champaign, the Institut de Ciències de l’Espai (IEEC/CSIC), the Institut de Física d’Altes Energies, Lawrence Berkeley National Laboratory, the Ludwig-Maximilians Universität München and the associated Excellence Cluster Universe, the University of Michigan, the National Optical Astronomy Observatory, the University of Nottingham, The Ohio State University, the University of Pennsylvania, the University of Portsmouth, SLAC National Accelerator Laboratory, Stanford University, the University of Sussex, and Texas AM University. The DES data management system is supported by the National Science Foundation under Grant Number AST-1138766. The DES participants from Spanish institutions are partially supported by MINECO under grants AYA2012-39559, ESP2013-48274, FPA2013-47986, and Centro de Excelencia Severo Ochoa SEV-2012-0234. Research leading to these results has received funding from the European Research Council under the European Unions Seventh Framework Programme (FP7/2007-2013), including ERC grant agreements 240672, 291329, and 306478.
Paper: 2017ApJ...839..114J (acknowledgments)
This work has been supported by the project LG 14013 of the Ministry of Education, Youth and Sports of the Czech republic, the grant project 15-06012S of the Czech Science Foundation, and the institutional research project RVO:67985815. M.S. thanks the support provided by the National Aeronautics and Space Administration through Chandra Award Number GO6-17111X issued by the Chandra X-ray Observatory Center, which is operated by the Smithsonian Astrophysical Observatory for and on behalf of the National Aeronautics Space Administration under contract NAS8-03060. We thank Hector Bravo-Alfaro for re-reducing existing VLA data. Based in part on data collected at the Subaru Telescope, which is operated by the National Astronomical Observatory of Japan. This research has made use of the NASA/IPAC Extragalactic Database (NED), which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. We further acknowledge the usage of the HyperLeda database ( http://leda.univ-lyon1.fr ). Facility: IRAM 30 m .
Paper: 2017MNRAS.467.2288S (fulltext)
Time-averaged LW escape fractions in the near-field, averaged over the first two Myr. All values are given in per cent. The structure of the rows is given as in Table 3 . Most LW escape fractions averaged over 2Myr are high; for SFEs with a flat IMF in Halo B, this behaviour is most pronounced. The four outbreaking I-front cases show slightly reduced LW escape fractions, but only down to 97percent. This change occurs because the initial few time-steps, until an outbreak with escape fractions smaller than 100percent, now carry a slightly larger weight. For the slowly moving I-fronts, the LW escape fraction decreases (with the exception of a 0.1 SFE flat IMF in Halo A). Here, the LW escape fraction slowly increases over time as the I-front moves through the halo. These last results need to be taken as lower limits as we have to run the slowing moving I-front simulations with higher resolution, but can do so only after 1Myr (0.18Myr for the Salpeter IMF) due to the high computational costs of the required refinement technique. LW escape fractions of returning I-fronts decrease or increase depending on when the I-front first breaks out (in the case of a quiet late Kroupa IMF) and starts to return. We therefore cannot give a general trend. 4.3 LW flux-averaged escape fractions in the near-field The LW flux of the SED is decreasing over time (compare Fig. 3 ) by factors of up to 410 4 in the case of the flat IMF. We therefore provide the LW escape fraction weighted by the LW flux emitted by the stellar source rather than averaged over time. We list these values in Table 5 and show them in Fig. 10 . Except for an SFE of 0.1percent, the escape fraction is always larger than 75percent. The difference to the time-averaged LW escape fraction from Section 3.2 is most significant for the flat IMF, as in that case, the LW escape fraction drops when the LW flux drops and the overall contribution is small. Additional shielding by atomic hydrogen does not decrease the LW escape fraction in this limit by more than 0.5percent. Therefore, we only provide the escape fraction obtained by considering both H 2 and H shielding together. Figure 10. Flux-averaged LW escape fractions in the near-field for our parameter space. Different SEDs are shown by different symbols, Halo A is colour-coded in light blue, and Halo B is colour-coded in black. Table 5. LW flux-averaged LW escape fractions in the near-field, averaged over the total LW flux of the simulation. All values are given in per cent. The upper three rows refer to Halo A, and the lower three rows refer to Halo B. Shielding by H 2 only and by the combination of H and H 2 yields the same results. 4.4 Time-averaged escape fractions in the far-field In the far-field, the LW escape fractions are generally larger than in the near-field, varying from 59percent to 100percent. Our values are listed in Table 6 . Including shielding by neutral hydrogen can reduce the LW escape fraction by up to 29percent (comparing
Paper: 2019ApJ...878..157X (acknowledgments)
S.X. acknowledges the support for Program number HST-HF2-51400.001-A provided by NASA through a grant from the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Incorporated, under NASA contract NAS5-26555. S.X. also thanks Chris McKee for useful conversations. S.J. acknowledges the support from the Sherman Fairchild Fellowship at Caltech. A.L. acknowledges the support from grant NSF DMS 1622353. Simulations were performed on Blue Waters supercomputer at NCSA, under the allocation PRAC NSF.1713353 supported by NSF. We have made use of NASA’s Astrophysics Data System and the yt astrophysics analysis software suite (Turk et al. 2010 ).
Paper: 2016MNRAS.463.4093C (acknowledgments)
S. Choudhuri would like to acknowledge the University Grants Commission, India for providing financial support through a Senior Research Fellowship. S. Chatterjee is supported by a University Grants Commission Research Fellowship. SSA would like to acknowledge CTS, IIT Kharagpur for the use of its facilities and to thank the authorities of the IUCAA, Pune, India for providing the Visiting Associateship programme.
Paper: 2020MNRAS.492.1110S (fulltext)
δ Tau, 2RXS J042836.6+191036 with ε Tau and 2RXS J042834.7+155721 with θ 1 Tau, respectively, which were observed with count rates of 0.44 ± 0.04, 0.03 ± 0.01, 0.02 ± 0.01, and 0.81 ± 0.04 cts s −1 , respectively. Hence, the dichotomy between γ Tau and θ 1 Tau (HD 27371 and HD 28307) on the one hand, and δ Tau and ε Tau (HD 27697 and HD 28305) on the other hand is immediately apparent in the observed RASS rates, with clear detections for the former (active) pair, and almost marginal detections for the latter pair of Hyades K giants. However, later pointed ROSAT observations of δ Tau (in ROSAT sequence RP200442) with a count rate of 0.0250 ± 0.0012 cts s −1 and of ε Tau (in ROSAT sequence RP200576) with a count rate of 0.0101 ± 0.0007 cts s −1 provided clear confirmations of all RASS detections. Using these count rates we can compute X-ray fluxes, X-ray luminosities, and X-ray surface fluxes, using the count-flux-conversion by Schmitt, Fleming Giampapa ( 1995 ), and thus arrive at the numbers quoted in Table 1 . It is important to keep in mind that detailed spectral information is not available from the ROSAT data, the flux conversion is therefore fraught with considerable uncertainty, which we estimate to be on the order of 50 per cent. And we need to keep in mind that, while active stars are variable, these X-ray detections are based on only a small number of visits. Nevertheless, a bifurcation is clearly seen into two active and two inactive K giants, very much like (as shown below) is the result of chromospheric activity monitoring, even though X-ray data do not yield any significant ranking order within each of these two pairs. 3.2 Chromospheric activity 3.2.1 Ca <sc>ii</sc> HK emission: the S-index For this paper, we use the results of spectral monitoring with our TIGRE facility (Schmitt et al. 2014 ), as well as the S -index time series obtained in the framework of the Mount Wilson HK project. The TIGRE facility is a fully robotic telescope with a 1.2 m aperture, located at the La Luz Observatory near Guanajuato, Mexico. Its only instrument is the two channel fiber-fed Échelle spectrograph HEROS with the wavelength range from 3800 to 8800 Å with a 100 Å gap at 5800 Å and a spectral resolution of, according to our recent performance measurements, R ≈ 21 000; a detailed description of TIGRE is given by Schmitt et al. ( 2014 ). In addition, we use data obtained by the Mount Wilson HK Project, which have been made available to the public and can be downloaded from ftp://solis.nso.edu/MountWilson_HK/ ; a detailed description of the data is also provided at this web site. The available data specifically include the star identification; the calibrated S index, which we use in this paper as the basis for our analysis; a code indicating with which instrument the data was taken; as well as the date of the observation and other material. It is important to keep in mind that the hardware used by the TIGRE and Mount Wilson HK Projects is
Paper: 2019MNRAS.483L..64D (fulltext)
a sum over all mass within λ C of the test point. We take for galaxies with NSA information and for those without, where V max is the maximum rotational velocity estimated by correcting the full-width half-max of the radio detection for turbulence and projection effects (Tully Fouque 1985 ). These contributions to the total potential derive primarily from the test galaxy’s dark matter. Note that in the case of cluster galaxies, the potential of the cluster itself is part of the external contribution. We calculate by summing the contributions of all unscreened mass within λ C . We then calculate the equilibrium H i -OC offset predicted for a given galaxy: if it is unscreened and 0 otherwise, where M ( r * ) is the dark matter plus gas mass between the H i and optical centroids. This follows from the requirement that the extra force on the stellar disc due to its offset from the halo centre compensate for its not feeling the fifth force, so that the stars, gas, and dark matter continue to move together (Jain VanderPlas 2011 ). We calculate M ( r * ) by assuming a constant density ρ 0 within r * (justified post-hoc: r * for the fifth-force models we are sensitive to is 10 −2 − 10 −1 kpc, much less than the halo scale radius r s ), and estimate it separately for each galaxy using the empirical relation between central baryonic and dynamical surface mass densities (Lelli 2014 ; Lelli et al. 2016 ; Milgrom 2016 ). This yields As spans a very small angle on the plane of the sky we compare separately its orthogonal RA ( r *, α ) and Dec. ( r *, δ ) components with those of the measured displacement for each galaxy. We feed these calculations into a Bayesian likelihood formalism. First, we generate N MC = 1000 Monte Carlo realizations of the predicted signal for each Alfalfa galaxy, sampling independently for each one the galaxy–halo connection (from 200 independent AM realizations), the distribution of mass in the smooth density field (from 10 particle-mesh BORG realizations), the contribution to Φ ex and from haloes too faint to be recorded in 2M+ + (calibrated with the darksky-400 N -body box), and the Gaussian observational uncertainties on the structural galaxy properties used to derive M ( r * ) and Φ in . The full probability distributions that we marginalize over are given in table 1 of Desmond et al. ( 2018b ). We estimate the probability that a given galaxy is unscreened as f ≡ N (|Φ ex | + |Φ in | |Φ c |)/ N MC . The likelihood function then has separate screened ( r * = 0) and unscreened (equation 4 ) components, with relative weights 1 − f and f , respectively. We model the unscreened component using a normalized histogram of the distributions of r *, α and r *, δ over all N MC realizations, obviating the need for assumptions on the form of the likelihood function such as Gaussianity. We convolve this likelihood with the Gaussian H i measurement uncertainty for each galaxy, θ i , and treat galaxies as uncorrelated and RA and Dec.
Paper: 2020ApJ...890...31Z (acknowledgments)
We appreciate Richard N. Manchester, Hao Tong, Shi Dai, Jumei Yao, Wenjun Huang, and Qihui Chen for helpful discussions. This work is supported by National Natural Science Foundation of China (Nos. 11178001, 11573008, U1631106). Hongguang Wang is supported by the 2018 Project of Xinjiang Uygur Autonomous Region of China for Flexibly Fetching in Upscale Talents. Yanrong Zhang appreciates Guangzhou University–University of Padova Joint PhD Program. The Parkes radio telescope is part of the Australia Telescope National Facility which is funded by the Australian Government for operation as a National Facility managed by CSIRO. This paper includes archived data obtained through the Australia Telescope Online Archive and the CSIRO Data Access Portal ( http://data.csiro.au ). We appreciate the anonymous referee for thorough and valuable comments. We thank Richard N. Manchester, Hao Tong, Shi Dai, Jumei Yao, Wenjun Huang, and Qihui Chen for helpful discussions.
Paper: 2018MNRAS.476.2199L (fulltext)
for this drop is that the rcb has been pushed closer to the surface of the underlying rock core, and with the envelope more nearly completely radiative, differences between how we treat the radiative transfer become magnified. Extremely gas-poor environments demand that atmospheric density profiles be sufficiently steep to contain a given amount of gas within the planet's Hill sphere, and this steepening pushes the thermal photosphere deeper into the envelope (compare the black dotted curves in the right-hand and second-from-right panels of Fig. 5 ); the rcb is, of course, pushed inwards as well. For ρ out /ρ MMEN = 10 −8 , the thermal photospheres of the dusty and dust-free optically thin models are only about twice the radius of the rocky core, and the corresponding rcb densities and opacities are 2–10 times larger than those of the radiative diffusion models. These larger rcb densities and opacities lower the radiative luminosities, slowing cooling and by extension accretion – hence the especially low GCRs seen in Fig. 4 characterizing ρ out /ρ MMEN = 10 −8 . At even lower nebular densities (data not shown), the rcb can be pushed all the way to the rocky core – i.e. the envelope becomes purely radiative – at which point the model sequence terminates (see also the isothermal endmember models in Lee Chiang 2015 , their fig. 4 and related discussion). Although the models shown in Fig. 4 are technically those of nebulae whose gas densities are fixed from t = 0 to 1 Myr and drop to zero thereafter, they are still reliable guides for nebulae whose gas densities deplete smoothly over time. The very fact that rates of gas accretion change only by factors of ∼3 when the nebular density varies by four orders of magnitude suggests that the detailed time history of nebular depletion introduces, at most, only order-unity effects. This expectation is confirmed in Fig. 6 , which shows how super-Earths amass ∼4–10 per cent-by-mass gas envelopes in a nebula that decays exponentially with an e-folding time-scale of 0.1 Myr. Figure 6. How 5 M ⊕ cores build their envelopes in an exponentially depleting nebula. The gas-to-core mass ratio (GCR ≡ M gas / M core ; solid and dashed curves using the right-hand axis) are computed by first fitting a scaling relationship to our numerical results for GCR versus nebular density (we find GCR ), and then integrating over the time derivative of GCR as the nebular gas depletes with an e-folding time of 0.1 Myr (grey curve using the left-hand axis). We halt envelope growth once ρ out /ρ MMEN falls below 10 −8 since the rate of gas accretion slows considerably (by about an order of magnitude compared to ρ out /ρ MMEN = 10 −7 ; see Fig. 4 ) at this level of depletion. 4 DISCUSSION 4.1 Creating super-puffs beyond 1 au So far in this paper, we have concentrated exclusively on planets located 0.1 au from their central stars. This restriction is due to our use of opacities generated from the model of Ferguson et al. ( 2005 ), which