adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018ApJ...858...66M (fulltext)
The stopping time is a measure of the timescale on which the gas transfers its angular momentum to dust and can be expressed as (Perets Murray-Clay 2011 )where is the gas thermal velocity, R s is the radius of the solid particle, ρ s is its density, assumed to be 1 g cm −3 regardless of the size, and v rel is the relative velocity between the gas and the dust grain. The dimensionless drag coefficient C D is a function of the Reynolds number Re of the flow around the particle and derives from an empirical law fitted on recent experimental data (Perets Murray-Clay 2011 ):where Re is the Reynolds number. It is given by (Supulver Lin 2000 )where l g is the mean-free path of the gas. The turbulent diffusion of the grains is modeled using a Monte-Carlo scheme where individual particles are given random impulses to mimic the stochastic transport due to turbulent eddies (see, e.g., Ciesla 2010 , 2011 ). Overall, the new position z d of a dust grain after a timestep dt is computed as follows in the vertical direction:where R 1 ∈ [−1; 1] is a random number, σ 2 is the variance of the random number distribution, D p is the diffusivity of the solid particle, and v adv is the term accounting for the nonuniform density of the gas in which the particles diffuse as well as the nonuniform diffusivity of the particles, and the forces experienced by the particle (see Equation ( 11 )). The diffusivity of the dust grains D p is related to the gas diffusivity through the Schmidt number Sc as (Youdin Lithwick 2007 ):where ν = αH 2 g Ω K is the turbulent viscosity of the gas (assumed to be equivalent to the gas diffusivity) expressed through the nondimensional α parameter measuring the level of turbulence within the disk (Shakura Sunyaev 1973 ) and St is the Stokes number, corresponding to the stopping time multiplied by the local Keplerian frequency. Larger values of α yield a more efficient redistribution of the dust grains through turbulent diffusion. Finally, the advection term in the transport equation is given by Ciesla ( 2010 , 2011 ) as This set of equations allows us to derive the vertical position z d of individual particles at each timestep and to subsequently estimate the dose of irradiation they received. 3. Irradiation of Grains The energy received by water molecules per unit time due to cosmic-ray irradiation W irr ( n ) as a function of the column density of gas n above the particle is taken from Yeghikyan ( 2011 ). These authors computed this term by using the cosmic-ray intensity I ( E ) (cm −2 s −1 st −1 MeV −1 ) from Cooper et al. ( 2003 ) and considering a planar geometry. This leads to a differential flux spectrum inside the disk F ( E ) given by W irr ( n ) is then derived from the molecular cloud material stopping power S ( E ), i.e., its capability to absorb the cosmic-ray energy, as estimated from the Stopping and Range of Ions in Matter program (Ziegler et al. 2010 ). Note that the stopping power mostly depends on the mass density and not
Paper: 2020A&A...641A..34B (fulltext)
the closure relations are determined by the Klein and Tolman conditions of thermodynamic equilibrium ( Ruffini et al. 2015 ). The solution to this system of equations leads to a continuous and novel dense core – diluted halo DM profile from the center all the way to the galactic halo (see Siutsou et al. 2015 ; Argüelles et al. 2016 ; Mavromatos et al. 2017 , for its applications). Similar core-halo profiles with applications to fermionic DM were also obtained in Bilic et al. (2002) and more recently in Chavanis et al. (2015) from a statistical approach within Newtonian gravity. The above corresponds to the original version of the RAR model, with a unique family of density profile solutions that behaves as ρ ( r )∝ r −2 at large radial distances from the center. This treatment was extended in Argüelles et al. (2018) , by introducing a cutoff in momentum space in the distribution function (DF; i.e., accounting for particle-escape effects) that allows defining the galaxy border (see Appendix A ). This extension of the RAR model was successfully applied to explain the Milky Way rotation curve, as shown in Fig. 1 , implying a more general dense core – diluted halo behavior for the DM distribution as follows: Fig. 1. Milky Way rotation curve and DM density profile from the extended RAR model with a core mass of M c = M ( r c ) = 3.5 × 10 6 M ⊙ . Top : DM (black) and baryonic (bulge + disk) contribution to the rotation curve v rot (total in red). Bottom : DM density profile. The baryonic model and the data are taken from Sofue (2013) . The parameters of the extended RAR model in this case are fermion mass m c 2 = 56 keV, temperature parameter β 0 = 1.1977 × 10 −5 , degeneracy parameter θ 0 = 37.7656, and energy cutoff parameter W 0 = 66.3407. For the RAR model fitting of the Milky Way, we follow Argüelles et al. (2018) ; see also Appendix A . – A DM core with radius r c (defined at the first maximum of the twice-peaked rotation curve), whose value is shown to be inversely proportional to the particle mass m , in which the density is nearly uniform. This central core is supported against gravity by the fermion degeneracy pressure, and general relativistic effects are appreciable. – Then, there is an intermediate region characterized by a sharply decreasing density where quantum corrections are still important, followed by an extended and diluted plateau. This region extends until the halo scale-length r h is achieved (defined at the second maximum of the rotation curve). – Finally, the DM density reaches a Boltzmann regime supported by thermal pressure with negligible general relativistic effects, and shows a behavior ρ ∝ r − n with n > 2 that is due to the phase-space distribution cutof. This leads to a DM halo bounded in radius (i.e., ρ ≈ 0 occurs when the particle escape energy approaches zero). As was explicitly shown in Argüelles et al. (2019b , a , 2018) , this type of dense core – diluted halo density profile suggests that the DM might explain
Paper: 2017MNRAS.466..228E (acknowledgments)
Acknowledgments 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, Fundao Carlos Chagas Filho de Amparo Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Cientfico e Tecnolgico and the Ministrio da Cincia, Tecnologia e Inovao, 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 Energticas, Medioambientales y Tecnolgicas-Madrid, the University of Chicago, University College London, the DES-Brazil Consortium, the University of Edinburgh, the Eidgenssische Technische Hochschule (ETH) Zrich, Fermi National Accelerator Laboratory, the University of Illinois at Urbana-Champaign, the Institut de Cincies de lEspai (IEEC/CSIC), the Institut de Fsica dAltes Energies, Lawrence Berkeley National Laboratory, the Ludwig-Maximilians Universitt Mnchen 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. We are grateful for the extraordinary contributions of our CTIO colleagues and the DECam Construction, Commissioning and Science Verification teams in achieving the excellent instrument and telescope conditions that have made this work possible. The success of this project also relies critically on the expertise and dedication of the DES Data Management group.
Paper: 2018ApJ...868L...9P (fulltext)
in Kharchenko’s catalog and count the summed flux of member stars inside-out until the radius where the flux is half of the total. We also use the bootstrap realizations to estimate the error in r h . Here, not all the member stars but only the visible ones are used for the calculation of r h . The mass segregation effect (Pang et al. 2013 ) might bias the r h we measure. To test this effect, we also count the half-number radius, the radius where the number of most probable member stars is half the total, and find that it is almost indistinguishable from the r h of each OC. The error of r h is quite small at the order of 0.01–0.05 pc. 3.2. The Line-of-sight Velocity Dispersion The accuracy of RVs allows us to compute the line-of-sight velocity dispersion ( σ 1D ) among member stars. We use stars having high S/N and RV errors less than 0.125 km s −1 . Typically, these stars of high S/N also have [Fe/H] measurements. To further reduce possible biases from membership determination, we fit the [Fe/H] and RV distribution with a two-dimensional Gaussian mixture model Φ tot , which is constituted of two components, the cluster Φ c and the field Φ f . Under this assumption, the likelihood of a star with given parameters is , where n c is the fraction of the cluster component; is Gaussian distribution function; , , and are the dispersions of ; RV c (RV f ) is the mean RV of cluster (field); and M c ( M f ) is the mean metallicity of cluster (field), respectively. Note that and are not constants, but are composed of two terms, the intrinsic dispersion () and the individual observational uncertainty (), which varies by one star: Parameters are fitted so that the total likelihood of all stars reaches maximum. We use nested sampling to derive the probability density function (PDF) of parameters. Since the resulting PDF is almost Gaussian, we take the mean and standard deviation of the PDF, as the best estimation of the parameters and their errors. As an example in Figure 2 , the blue dots show the [Fe/H] and RV values for individual selected stars in NGC 1245, where the two-dimensional Gaussian mixture model (Equation ( 1 )) is projected into one-dimension to show how well the model fits the data. The [Fe/H]–RV distribution is fitted well by the sum of field (green lines) and member (red lines) components. Figure 2. Distribution of [Fe/H] and RV (blue dots) for the open cluster NGC 1245 is fitted with a two-dimensional Gaussian model. For illustration, we project this model into one-dimension. The distributions of [Fe/H] (right histogram) and RV (top histogram) are constituted of two components, the field (green lines) and the cluster (red lines). The histogram is normalized such that the integral over the range is 1. The members of OCs observed in APOGEE-2 are spatially random sampling of the OC members. Therefore, we do not expect that this random member list will bias the computation of the velocity dispersion of OCs. Typically, the RV measurements of
Paper: 2019MNRAS.490.3705S (acknowledgments)
ACKNOWLEDGEMENTS The authors would like to thank the referee for their comprehensive review and Andrea Ravenni for useful discussions. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 725456; CMBSPEC). JC was also supported by the Royal Society as a Royal Society University Research Fellow at the University of Manchester, UK. EL was supported by the Royal Society on grant NoRGF/EA/180053.
Paper: 2018ApJ...862...71S (acknowledgments)
J.R. acknowledges Dr. Aoki for providing the Subaru spectrum for G64-12. This research was supported by the National Key Basic Research Program of China under grant No. 2014CB845700 and the National Natural Science Foundation of China under grants No. 11473033 and 11603037. This work is also supported by the Astronomical Big Data Joint Research Center, co-founded by the National Astronomical Observatories, Chinese Academy of Sciences and the Alibaba Cloud.
Paper: 2019ApJ...874L...1N (fulltext)
traveling intensity perturbation of a hot, 8–10 MK, coronal loop, in a C7.4 class flare, with SDO /AIA. The intensity variation was seen to bounce approximately four times between the loop’s footpoints, before fading, i.e., exhibiting a “zigzag” pattern in a time–distance plot made along the apparent direction of the magnetic field. The oscillation period and decay time were about 630 s and 440 s, respectively. The apparent phase speed was about 460–510 km s −1 which matched the sound speed in the loop. The authors suggested calling this phenomenon a reflecting longitudinal wave, while Reale ( 2016 ) introduced the term “sloshing” oscillations for this phenomenon. A similar, bouncing back and forth between the footpoints, EUV intensity perturbation was detected in another C-class flare by Kumar et al. ( 2015 ). The period of the oscillation and decay time were about 409 s and 1121 s, respectively. The characteristic phase speed of the wave is about 560 km s −1 , which was roughly consistent with the sound speed at the temperature of about 10–16 MK. Wang et al. ( 2015 ) observed a clear sloshing pattern in the 131 Å intensity oscillation with SDO /AIA. The oscillation period, decay time, and the apparent phase speed were estimated as about 12 minutes, 9 minutes, and 500 km s −1 , respectively. Mandal et al. ( 2016 ) reported a simultaneous observation of a propagating and reflecting intensity disturbance in a hot coronal loop observed in EUV and X-rays with SDO /AIA and Hinode /XRT, respectively. In addition, three other cases of intensity pulses sloshing between footpoints were analyzed. In all considered cases the projected traveling speed was found to be lower than the local sound speed, consistent with the slow wave interpretation. Pant et al. ( 2017 ) observed antiphase oscillations of the 171 and 193 Å intensity oscillations at two ends of a nonflaring fan loop system observed with SDO /AIA. The oscillation period was estimated to be 27 minutes, the damping time of 45 minutes, and the apparent phase speed of about 75 km s −1 . The time–distance map made along the loop showed a sloshing (i.e., “zigzagging”) motion pattern. We need to point out that sloshing oscillations are different from other manifestations of slow waves in the corona, propagating (not reflecting) slow waves that are detected in polar plumes and near footpoints of long loops (see, e.g., de Moortel 2009 ). Numerical modeling of slow oscillations in a coronal loop reproduced main properties of standing (SUMER) and sloshing oscillations, i.e., the observed oscillation period, P slow ≈ 2 L / C s , where L is the loop length and C s is the sound speed; and associated the rapid damping of the oscillation with the effect of thermal conduction (Ofman Wang 2002 ). Nakariakov Zimovets ( 2011 ) demonstrated that a similar periodicity could occur in arcades. The preferential excitation of lower, i.e., the fundamental and second parallel harmonics by an impulsive source, was found
Paper: 2019A&A...622A.124A (acknowledgments)
This work was supported by NYUAD Institute Grant G1502 “NYUAD Center for Space Science”. RA acknowledges support from the NYUAD Kawadar Research Program. The HMI data is courtesy of NASA/SDO and the HMI Science Team. Computational resources were provided by the German Data Center for SDO funded by the German Aerospace Center. SMH and LG thank the Max Planck partner group program for supporting this work. Author contributions: CSH and LG designed the research goals. RA proposed the machine-learning methodology for predicting flows and performed experimental and computational analysis. CSH prepared data and performed helioseismic analysis. All authors contributed to the final manuscript. We thank Aaron Birch and Bastian Proxauf for helpful insight into the ring-diagram pipeline.
Paper: 2019MNRAS.488.2079B (acknowledgments)
ACKNOWLEDGEMENTS We would like to thank Anz̆e Slosar for useful comments and discussions. The simulations used in this work were run on the Cobra supercomputer at the Max Planck Computing and Data Facility (MPCDF) in Garching near Munich. AB and FS acknowledge support from the Starting Grant (ERC-2015-STG 678652) ’GrInflaGal’ of the European Research Council.
Paper: 2020ApJ...892....3H (fulltext)
by an analytic MW potential, as well as to resolve analytically the interaction between the dwarf gas and the MW halo gas. The gas removal from dwarf galaxies is analytically calculated using the Close et al. ( 2013 ) model, for which two physical processes are considered, i.e., the balance between the gravitational force of the dwarf galaxy and the external ram pressure (i.e., from the Gunn Gott 1972 formula), and the mass loss introduced by Kelvin–Helmholtz instabilities (e.g., Nulsen 1982 ). We have verified that the above technique of “pure N -body for stars plus analytical ram pressure for gas” is able to reproduce the gas removal process resolved by hydrodynamics simulations done in Yang et al. ( 2014 ) and in Hammer et al. ( 2018 , 2019 ). The above allows us to probe the interaction of dSph stars with the MW gravitational field in a full Newtonian scheme. By adopting a gravitational force softening of 0.005 pc, it implies that there should be no stellar particles that can be affected by the undesirable effects due to the softening in media with central stellar density ∼500 times smaller than at the Sun neighborhood (see H19 ). From left to right, Figure 4 shows three different steps in evolution of such a stellar system after the gas removal, i.e., when it may resemble a dSph. It includes (1) when the gas has just been fully removed, (2) just after pericenter at the peak of tidal shock effects, and (3) well after pericenter, when the system almost vanishes owing to the heavy tidal stripping process that unavoidably follows tidal shocks. Figure 4. Summary of two N -body simulations of a DM-free and gas-rich dSph progenitor, with three columns on the left (right) for model C (model Q), respectively. They are shown at steps 1, 2, and (3), from left to right, respectively (see text). The first line shows the appearance of the simulated galaxies in gray colors. For other lines, green, red, black, and gray symbols represent stars in step 1, from very low velocity and very near the center to very high velocity and very far from the center (gray symbols with extremely low density); see left panels of the (d)–(f) rows. From top to bottom: (a) projected view on sky; (b) V los vs. sky projected radius, with the vertical blue dashed line showing r half that includes the selected stars; (c) the evolution of σ los with time showing a peak very near the pericenter passage (see point in middle panel), and with the gray curve indicating the theoretical expectation from Equation ( 5 ) for f MW shocks = 0.25 (0.08) for model C (model Q), which also includes self-gravity from gas and stars; (d) the velocity distribution projected in gal-( Y , Z ) plane in Galactocentric coordinates, and with the arrow showing the MW direction; (e) the phase diagram indicating that many stars are leaving the system after gas removal (left panel); and (f) V los vs. showing that near pericenter (middle panel) most stars have their motions only on the line of sight. In Figure
Paper: 2019A&A...627A..51G (fulltext)
other two levels 2s2p 2 2 P 1/2 and 2s 23 d 2 D 3/2 . Fig. B.6. Same as Fig. B.1 but for four Fe XXII lines. The R -matrix data from Liang et al. (2012) are plotted in red. The R -matrix calculation of electron-impact excitation of Fe XXIII was made by Fernández-Menchero et al. (2014) , based on 238 fine-structure levels in both the configuration interaction target and close-coupling collision expansions. We compare the collision strengths for four strong Fe XXIII lines with their calculations, on which the resonance has a large effect. As shown in Fig. B.7 , the two sets of data agree very well within a difference of ≤5% for T e = 0.1 − 10 keV. Fig. B.7. Same as Fig. B.1 but for four Fe XXIII lines. The R -matrix data from Fernández-Menchero et al. (2014) are shown in red. In Fig. B.8 we compare our calculations of the total excitation of four main Fe XXIV lines with the R -matrix results reported in Liang Badnell (2011) . The R -matrix work was done with the intermediate coupling frame transformation method for 195 levels, and the Auger- as well as radiation-damping effects were both taken into account. For 3s 2 S 1/2 , 3d 2 D 3/2 , and 3d 2 D 5/2 , the R -matrix and our data agree well with ∼5%, while for 3p 2 P 3/2 , the difference between the two approaches becomes slightly larger, ∼10%, for the collision strengths at energy of ≤1 keV. Fig. B.8. Same as Fig. B.1 but for four Fe XXIV lines. The R -matrix data from Liang Badnell (2011) are plotted in red. The illustrative comparison indicates that the difference between the modern large-scale isolated resonance calculation and the recent interacting R -matrix becomes now smaller than previously reported in Badnell et al. (1994) . The two methods are well within 20% for the direct transitions from the ground states to a few selected excited levels at n = 3. As shown in Fig. 6 , some excited levels are heavily populated through cascade, indicating that the line intensities could actually be significantly affected by the excitation rate coefficients to higher states. In the following section, we will give an extensive comparison for all the relevant rates. B.3. Comparing with recent R-matrix results: Scatter plots The scatter plot in Fig. B.9 shows the comparison of the effective collision strengths from the current and R -matrix calculations, for all the excitations from the ground states. The R -matrix results are a collection of the works mentioned in Appendix B.2 . The plot confirms that the two methods agree on the strongest transitions within uncertainties of ∼20%. However, the discrepancies between the two methods on the weaker transitions are substantially larger, up to a factor of two for Fe XVII and Fe XXIV , and one order of magnitude for the rest ions, at the lowest effective collision strengths. We note that orders of magnitude difference for weak transitions are also found among different R -matrix calculations (intermediate coupling frame transformation, Breit-Pauli, Dirac atomic