adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020MNRAS.492.4819P (fulltext)
from equation and L circ is determined by the radius of the circular orbit r circ , To avoid the singularity at L = L max , it is helpful to introduce a new variable s by α = (1 − s 2 ) 1/2 L max ( r )/ L circ . Equation ( 4 ) then reads: As zero-order approximation, Φ * was set to zero, and an approximation was obtained for ρ( r ). The normalization constant A was recalculated after each iteration to provide the total mass M * for the cluster. The next approximation for the potential Φ * was obtained from a numerical solution of the Poisson equation After obtaining self-consistent profiles ρ( r ) and Φ * ( r ), one can build a random realization of the DF. First, we find L of the particle using the standard von Neumann rejection technique for equation ( 3 ). Energy E = E 0 and angular momentum L determine the orbit. Secondly, we set the radius of the particle, which is uniformly distributed in the radial angle variable, w . The dependence r ( w; E, L ) can be obtained directly by orbit integration, if the number of particles is not too large, or interpolating between reference orbits from a library. The radius r immediately gives the square of radial velocity , and the absolute value of transversal velocity . The velocity components ( v θ , v φ ) are uniformly distributed over a polar angle ψ in the velocity polar coordinates ( v ⊥ , ψ). Finally, the spatial coordinates are obtained as a uniform distribution on a sphere. For our default model with N = 10 5 and the central point mass M = 100, this procedure gives a nearly spherically symmetric N -body realization with centre-of-mass offset 1.5 × 10 −3 . Due to numerical noise, the initial amplitudes of the spherical harmonics A l (see equation 12 ) for 1 ≤ l ≤ 4 are of the order of 10 −3 . 2.3 <italic>N</italic>-body simulations The simulations presented in this work have been carried out using the specially adapted direct N -body code φ- gpu (Berczik et al. 2011 , 2013 ). The basic concepts of the code are based on the publicly available φ- grape (Harfst et al. 2007 ) code, an Aarseth N -body 1-like code (Aarseth 1999 ) including an efficient mpi parallelization and support for the special-purpose hardware GRAPE (Fukushige et al. 1991 ) (currently Graphic Processing Units – GPU). The current version of the φ- gpu code has been fully rewritten in the C++ programming language for the most effective use of GPUs. The φ- gpu code is fully parallelized using the mpi library. The mpi parallelization was done in the same ‘j’ particle parallelization scheme as in the earlier φ- grape code. All particles are divided equally between the working nodes (using the MPI_Bcast() command) and in each node we calculate only the fractional forces for the particles in the current time-step, i.e. the so-called ‘active’ or ‘i’ particles. We get the full forces from all particles acting on the active particles after the global MPI_Allreduce() communication routine is applied. The code supports the use of
Paper: 2015ApJ...806..247B (acknowledgments)
The South Pole Telescope is supported by the National Science Foundation through grant PLR-1248097. Partial support is also provided by the NSF Physics Frontier Center grant PHY-1125897 to the Kavli Institute of Cosmological Physics at the University of Chicago, the Kavli Foundation and the Gordon and Betty Moore Foundation grant GBMF 947. This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. We acknowledge the use of the Legacy Archive for Microwave Background Data Analysis (LAMBDA). Support for LAMBDA is provided by the NASA Office of Space Science. This work was supported in part by the Kavli Institute for Cosmological Physics at the University of Chicago through grant NSF PHY-1125897 and an endowment from the Kavli Foundation and its founder Fred Kavli. The McGill group acknowledges funding from the National Sciences and Engineering Research Council of Canada, Canada Research Chairs program, and the Canadian Institute for Advanced Research. M. Dobbs acknowledges support from an Alfred P. Sloan Research Fellowship. S. Dodelson is supported by the U.S. Department of Energy, including grant DE-FG02-95ER40896. T. de Haan is supported by a Miller Research Fellowship. Cluster studies at SAO are supported by NSF grant AST-1009649.
Paper: 2015MNRAS.453.3662G (acknowledgments)
We would like to thank the anonymous referee for providing many insightful suggestions which have led to improved clarity of the work presented here. MRG would like to thank the generous hospitality of Kirk and Angela Korista and the Department of Physics Astronomy at Western Michigan University during the initial stages of this work. KTK would like to thank the University of Leicester for their hospitality during the completion of this work.
Paper: 2018MNRAS.476L..25I (fulltext)
variables, and powerful radio pulsars, such as the pulsar in the Crab nebula. In laboratory plasmas, jets, or directed flows, are observed in devices such as plasma focus (Beskin et al. 2017a ). The main puzzle of jets is their high degree of collimation. It is not clear how hot matter of jets, ionized gas in a strong magnetic field, confined in the cylindrical radius direction, which is the direction perpendicular to the axis of the jet. Neither hot gas nor external magnetic field is usually observed in the interstellar and intergalactic environment surrounding a jet. We will study the problem of radial equilibrium of the jet in the framework of ideal magnetohydrodynamics (MHD). This approximation is well suited for astrophysical jets. Dissipative effects are weak in hot plasma. In addition, large mean-free paths of particles, the presence of a strong magnetic field, in which cyclotron radii play the role of particle mean-free paths, and small cyclotron radii of ions in comparison with the characteristic scales of jets allow us to use ideal MHD for the description of astrophysical jets. It has been shown before that the problem of equilibrium of the jet in the radial direction within the framework of MHD is related to electric currents flowing inside the jet. If the jet carries uncompensated current, i.e. the total current of the jet is not zero, the current can be pinched to keep the hot jet in equilibrium (Blandford Payne 1982 ; Heyvaerts Norman 1989 ; Sulkanen Lovelace 1990 ; Li, Chiuch Begelman 1992 ; Pelletier Pudritz 1992 ; Sauty Tsinganos 1994 ). However, the question arises how to close the uncompensated current loop. If you let the return current flow outside the jet, it is not clear where it goes since track of the return current is usually not visible. It is more convenient to assume that return current flows inside the jet on its periphery. This raises the problem of the equilibrium of the jet in the region of return current. The return current repels from the direct one, and within the framework of a purely radial structure, there is no equilibrium, if there is no contribution of the surface current at the edge of the jet (Lery et al. 1998 ; Beskin Malyshkin 2000 ; Lyubarsky 2009 ; Beskin Nokhrina 2010 ). Here we will explore another possibility – the emergence of spiral structure in the region of return current flow. 2 CYLINDRICAL FLOW Let us consider stationary MHD equations in a coordinate system moving with the plasma flow along its axis with the velocity u . Here ρ is the plasma density of the jet, is velocity of the plasma, B is the magnetic field, and P (ρ) is the plasma pressure, which is a function of the density through the equation of state of the matter in the jet. Suppose first that all quantities depend only on the cylindrical radius coordinate r , that is, the jet is axisymmetric and homogeneous along axis z . In this case, the velocity and the magnetic field can be expressed as functions of r , Velocity v φ
Paper: 2019MNRAS.484..520A (fulltext)
parameter c = r c / r t , given by the ratio between the core ( r c ) and tidal ( r t ) radius. On the other hand, the tidal radius depends on the cluster orbit through the relation where Ω is the GC angular velocity, and d 2 Φ/d r 2 the second order derivative of the background gravitational field. 1 In the simplest approximation that the BHB separation is much smaller than its distance from the GC and that it dominates the dynamics, the equation reduces to being r p the GC periastron. Although equation ( 2 ) was originally derived for point-like mass moving on circular orbits, it approximate reasonably well the tidal radius of an extended object moving on an eccentric orbit during the passage at periastron (King 1962 ; Arca-Sedda et al. 2016 ). Once the GC orbit is assigned, the tidal radius can be calculated and combined with the value of W 0 chosen in order to get r c . Another degree of freedom is represented by the GC pericentre ( r p ) and eccentricity ( e ), as well as the orbit inclination with respect to the BHB orbital plane, i , and the mutual orientation of the two orbital ascending nodes ω. Note that the definition of the inclination angle i is as the one between the BHB and the GC orbital angular momentum vectors, where i = 0 corresponds to prograde and i = π to retrograde configurations. Finally, the galaxy background can significantly alter the first part of the GC orbital evolution. According to the widely used Dehnen ( 1993 ) models, the galactic density profile can be described as where M g , r g represent the galaxy mass and length scale, respectively, whereas γ represents the inner slope of the density profile. Therefore, a lower limit to the total number of parameters needed to characterize such a system is given by the combination of the BHB parameters (4), the GC structural and orbital properties (6) and the galaxy parameters (3), namely: BHB parameters: semi-major axis a BHB , eccentricity e BHB , total mass M BHB and mass ratio q BHB ; GC structural parameters: Mass M , potential well W 0 , concentration c ; GC orbital parameters: semi-major axis a , eccentricity e and pericentre r p ; galaxy parameters: galaxy mass M g , typical radius r g and density profile slope γ. Assuming only 3 values for each parameter would imply then a total number of ∼1.6 × 10 6 different models. In order to keep the number of models reasonable in terms of computational request and human times, we limited the number of degrees of freedom in our problem as follows: we assumed an equal mass BHB and kept fixed its apocentre; we fixed the GC W 0 , M and r p ; we assumed the GC and BHB orbits in a coplanar, counter-rotating configuration; we fixed the galaxy background. This choices reduced our problem to only three parameters: the BHB mass M BHB and its eccentricity, e BHB , and the GC eccentricity e . We assumed two different values for M BHB = 10 7 − 10 8 M ⊙ , three values for e BHB = 0, 0.5, 0.9 and two for e = 0.5, 0.9, thus
Paper: 2016ApJ...833...94N (fulltext)
data using the observations from its Solar Optical Observing Network (SOON). Although in the past, the (SOON) data set was often used as a continuation of the RGO data set of sunspot areas, it has a known deficiency in the measurements of smallest sunspots and pores (e.g., Muñoz-Jaramillo et al. 2015 ). On the other hand, the observations of sunspot areas from the KMAS, which start in 1954, are more uniform and include the smallest sunspots and pores comparable to the RGO sunspot size limit. By their cumulative distribution, KMAS group areas are similar to RGO data (Muñoz-Jaramillo et al. 2015 , who established a proportionality constant between RGO sunspot group areas and KMAS as 1.07 or 0.93 to convert from KMAS to RGO). Thus, for the period 1977–2014, we employ sunspot areas observed at KMAS (Tlatov et al. 2016 ). 3. TWO POPULATIONS OF SUNSPOT GROUPS 3.1. Group Areas Sunspot groups are not static objects: as an active region develops its area increases and reaches a maximum within 2–3 days after its emergence (e.g., Pevtsov et al. 2003 ). According to a review article by van Driel-Gesztelyi Green ( 2015 ), a lifetime of active regions with fully developed sunspots varies between weeks and months. The rise phase takes between 3% and 15% of a group’s lifetime, which also translates to a few days from the beginning of the emergence of a group to the maximum of its development (maximum area). To take into account these evolutionary changes in group areas, for each group in the RGO–KMAS data set, we selected the largest area of this group. Since some groups will reach their maximum area on the side of the Sun not visible from Earth, one could expect a slight enhancement in small group areas. However, with a rapid growth phase of only a few days, we do not expect that such enhancement will have a significant effect on our conclusions. Figure 1 (a) shows the distribution of (logarithm of group areas) for the combined RGO–KMAS data set. The distributions were constructed using an equal bin size of Δ = 0.25 in the logarithm of group areas. The distribution is clearly bimodal, with one component peaking at about 16 M.S.H. (millionth of solar hemisphere, ) and the other at about 316 M.S.H. (). The former corresponds to a round feature with 7.6 Mm in diameter (a pore or a rudimentary sunspot) and the latter is about 350 Mm in diameter, or about the size of a regular sunspot. To separate the two components, we fit the observed distributions by a combination of two Gaussian functions. The fitting was done with the help of the data analysis software package “ORIGIN,” which uses the Levenberg–Marquardt algorithm to iteratively adjust the amplitude, mean, and standard deviation of each fitted Gaussian, until the best-fitted solution is found (e.g., Press et al. 1992 ). The fitted Gaussians are shown in Figure 1 (a) as blue profiles, and their sum is shown as a red double-peaked profile. Figure 1. (a) Distribution of group areas (log S ) from RGO data.
Paper: 2019ApJ...881...32B (fulltext)
( 1986 ) asand C H is the heat transfer function defined byIn these equations, γ is the specific heat ratio, k B is the Boltzmann constant, μ is the mean molecular weight of a gas particle, m H is the mass of a hydrogen atom, and s a is a ratio of the drift velocity and the thermal gas velocity defined byEquation ( 10 ) can be solved for the dust temperature at which the heating of the dust by the gas is in equilibrium with the radiative cooling,which can be further rewritten as where the C values are constants with respect to s a defined by and α values are functions of s a defined by With this reduction of the equation, Equation ( 18 ) is solved for T dust using the minpack 's hybrd and hybrj routines with the modified Powell method (Moré et al. 1980 ). For the purposes of guiding the algorithm, initial guesses were computed byApplying the temporal evolution of the gas temperature, density, and drift velocity determined in the previous section, we are able to compute the dust temperature across the evolution of the shock. In the pre-shock gas, the gas and dust are assumed to be in thermal equilibrium, or T dust = T gas . In addition, we deviate from the formalism used in Jiménez-Serra et al. ( 2008 ), by setting an additional constraint of the velocity when used to calculate the rate of dust heating. Specifically, we assume that as the gas and grains begin to recouple, the drift velocity will eventually fall below the random thermal velocities of the gas. In the hot gas regime, this thermal velocity may be nontrivial. As such, the velocity of collisions may be more accurately described for these purposes as As can be seen in Figure 2 , the grain temperature is found to initially track with the velocity structure within the shock, peaking at the same corresponding time as the peak velocity owing to adiabatic heating. However, as the gas continues to heat up, the grain temperature undergoes a secondary heating regime owing to the conductive heating from the hot gas. As such, while the sputtering rate (see Section 2.2.3 ) will fall off rather rapidly, the ability for species to redeposit onto the grains is delayed until the gas can cool sufficiently. 2.2.3. Sputtering One major physical process that is common in astrophysical shocks is the nonthermal desorption of molecules from the ice into the gas phase through the collision of moderately fast gas-phase particles with the dust grains (e.g., sputtering). This process is a key mechanism for lifting complex molecules into the gas phase nondestructively. For typical C-shock velocities (e.g., 10–40 km s −1 ), the sputtering is significant enough to desorb most species from the ice mantle without destroying them or having significant grain core erosion, both of which are common in high-velocity J-shocks (Requena-Torres et al. 2006 ). In order to include these processes, the sputtering yields must be computed for each species on the ice, which is dependent on the conditions of the shock, the nature
Paper: 2018MNRAS.479.2615F (fulltext)
them from single stars. Triple and quadruple star disruptions naturally bring binaries in the vicinity of the MBH. Fig. 6 shows the cumulative distribution function of binary S-stars semimajor axis and eccentricity as a function of a in for Model 1, which shows a clear correlation between the final and initial binary semimajor axis. More than of binary S-stars have a b ≲ 0.5 au to avoid tidal disruption by the MBH and the eccentricity cumulative distribution is nearly independent of the initial a in and is ∝ e , i.e. the eccentricity distribution is constant. Future data, as those expected by the James Webb Space Telescope, may observe near-infrared sources with higher photometric precisions, hence helping in identifying binaries in the GC. Figure 6. Cumulative distribution function of binary S-stars semimajor axis (left) and eccentricity (right) as function of a in for Model 1. More than ≈90 per cent of binary S-stars have a b ≲ 0.5 au to avoid tidal disruption by the MBH. The cumulative distribution of eccentricities is nearly independent on the initial a in and is ∝ e , i.e. the eccentricity distribution is constant. 4 DISCUSSIONS AND CONCLUSIONS Most of the observed HVSs support the standard Hills scenario, but the main-sequence HVS HE0437-543 and the candidate HVB require different production mechanisms than the standard binary disruption. While HE0437-5439 is likely to come from the centre of the Large Magellanic Cloud (Erkal et al. 2018 ), the candidate HVB as well as the other HVSs whose traveltimes exceed their main-sequence lifetime would require disruption of triple starsby the MBH, which would produce a HVB that can later evolve into a blue straggler star as a consequence of binary evolution (Perets 2009 ). However, the recent detailed N -body simulations byFragione Gualandris ( 2018 ) have showed that the ejection rate is too low to explain these hypervelocity outliers. In this work, we discussed the 2+2 quadruple tidal disruption scenario by means of high-accuracy scattering experiments. While less abundant than triple stars, 2+2 quadruple stars constitute of the stars in the solar neighbourhood (Tokovinin 2014a , b ) and have been shown to have interesting dynamics dealing with white dwarf-white dwarf collisions (Fang et al. 2018 ) and gravitational wave emission (Seto 2018 ). We studied a wide range of parameters of the quadruple stars and performed simulations with both point masses and finite stellar radii. We found that only a very small fraction () of encounters results in the ejection of a HVB. Taking into account that the estimated ejection rate for the Hills mechanism is (Yu Tremaine 2003 ) and that 2+2 quadruple stars are roughly a third of triple stars, we conclude that also 2+2 quadruple disruption leads to a HVB production rate of , similarly to the triple disruption scenario. We conclude that quadruple disruption, as long as triple disruption, is an unlikely source of HVBs, which would require a non-GC
Paper: 2016MNRAS.460.2796S (acknowledgments)
ALS acknowledges support from NOVA (Nederlandse Onderzoekschool voor Astronomie). We thank Adam Ingram, Lucy Heil, Victoria Grinberg, Michiel van der Klis, and the participants of ‘The X-ray Spectral-Timing Revolution’ Lorentz Center workshop (2016 February) for useful discussions that contributed to the development of this paper. We also thank the anonymous referee for their helpful comments. This research has made use of data and software provided by the HEASARC; NASA's Astrophysics Data System Bibliographic Services; Numpy v1.9.3 and Scipy v0.16.0 (Jones et al. 2001 ); Astropy v1.0.4 (Astropy Collaboration et al. 2013 ); Matplotlib v1.4.3 (Hunter 2007 ); ipython v3.2.0 (Perez Granger 2007 ); and the AstroBetter wiki.
Paper: 2017MNRAS.468L..31D (acknowledgments)
Acknowledgments This work was supported by NSF-AST 1514835. We use data products from the Wide-field Infrared Survey Explorer , which is a joint project of the University of California, Los Angeles, and the Jet Propulsion Laboratory/California Institute of Technology, funded by the National Aeronautics and Space Administration. Funding for the Sloan Digital Sky Survey IV has been provided by the Alfred P. Sloan Foundation, the US Department of Energy Office of Science and the Participating Institutions. The SDSS web site is www.sdss.org . When the data reported here were acquired, UKIRT was operated by the Joint Astronomy Centre on behalf of the UK Science and Technology Facilities Council.
Paper: 2015ApJ...804...48I (acknowledgments)
The 45 m radio telescope is operated by Nobeyama Radio Observatory, a branch of the National Astronomical Observatory of Japan. We wish to thank the 45 m telescope team, including Hiroyuki Iwashita, Tetsuhiro Minamidani, Cheko Miyazawa, Yoshio Tatamitani, and Hiroyuki Nishitani for the support of our observation. SDO data are courtesy of NASA/SDO, as well as the AIA and HMI science teams.
Paper: 2016MNRAS.462.4416W (acknowledgments)
This work has been supported by the National Natural Science Foundation of China (11403043, 11473034 and 11273029), and the Strategic Priority Research Programme ‘The Emergence of Cosmological Structures’ of the Chinese Academy of Sciences (grant no. XDB09010200).