adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018A&A...618A..82M (fulltext)
could be directly understood from the second term on the RHS of Eq. ( 34 ). Also the damping due to the plasma resistivity is clearly shown by the presence of the third term on the RHS of Eq. ( 34 ). Since the viscosity is small for coronal conditions, the deceleration is not very strong in the long wavelength limit, but in the long run would gradually become significant. 3. Discussions and conclusions In the solar atmosphere MHD waves interact with the plasma. When a gradient in the density is present, a wave guide is created that guides waves leading to the appearance of collective modes. In this work a magnetized cylindrical structure whose density is higher than its surroundings has been taken into consideration. The interplay of the equilibrium twist and rotation exhibits interesting features of the nature of the torsional wave that are of interest for coronal seismology. We considered a twisted and rotating magnetic flux tube, which resembles a solar spiral or tornado, and studied the influence of its helical properties on other dissipating parameters linked to the oscillations of the torsional waves. The analytic expressions obtained for the dependence of the phase speed of the torsional wave on the equilibrium twist and rotation enabled a quantitative study of the interplay of various actors on its propagation. The comparison of the explicit expressions obtained for the phase speed of the torsional wave in the present study with the implicit version, in all cases proved adequate for the robustness of the explicit expressions. This is reflected in the figures where the numerical results are compared with analytic solutions. The full dispersion relation for axisymmetric oscillations in the long wavelength limit has been obtained by showing the joint effects of the equilibrium twist and rotation with the viscosity and gravity (Eq. ( 19 )). An explicit expression for the dependency of the torsional wave frequency in the presence of viscosity, equilibrium, and environmental conditions on the wave number has been obtained (Eq. ( 20 )). This explicit dispersion relation also gives the damping and deceleration of the torsional wave guided by solar plasma structures. The fine consistency between the full dispersion relation and the explicit expressions for the frequency, damping, and speed of the torsional wave justifies the model and results. The magnetic twist owes its efficiency on the torsional wave frequency to not only the flux-tube rotation and coronal conditions, but also to the viscosity and gravity. In a zero plasma- β structure that is non-rotating, the magnetic twist acts very weakly on the torsional wave frequency (Fig. 3 ). If in such conditions the viscosity and solar gravity become negligible, the magnetic twist, no matter how large, would be completely neutral without any effect on the frequency. The damping of the torsional wave due to viscosity in solar structures is proportional to the strength of the magnetic twist and
Paper: 2021MNRAS.503.3965M (fulltext)
refer to φ w = 6 V, hν = 10.29 V (for λ ν = 121.57 nm), T d ≈ 205 K (for latitude θ = 70°), and non-thermal photoelectron distribution with A = 0.1. The parameter q is observed to increase with φ o . It is also noticed that the effect of dust charge fluctuation on the wave dispersion is pronounced for small z o values; for instance, q > 1 for z o = 30 and φ o > 0.2 (Fig. 5a ) and for φ o = 1.0 and z o 104 (Fig. 5b ). The positive plasma potential φ o infers enhanced population density of the photoelectrons in the vicinity compared to the case φ o = 0; this may induce additional shielding to the floating dust particles and effectively reduces the coupling between charged dust particles. It may result in a decrease in the phase speed and frequency of the propagating DA wave. This nature has been depicted in Figs 5(c) and (d) . The calculations corresponding to non-zero φ o are of significance to the lunar exospheric plasma, which exhibits a photoelectron sheath with a positive potential and varies with the altitude. Another interesting point evident from the wave dispersion (Figs 2 – 5 ) is the occurrence of a cut off with z o , Δ, and φ o for which ω r takes imaginary values, and it refers to a damped wave solution for the excited DA modes. Figure 5. (a) and (b) ( top panel ): Parameter q A = [(Λ ch /Ω ch ) n de ] A and (c) and (d) ( bottom panel ): wave dispersion ( ω r / k ) in long-wavelength regime ( kλ e 1) for the non-thermal distribution of photoelectrons. Left-hand and right-hand columns refer to parameters’ variation with φ o and z o , respectively. The calculations refer to the parameters φ w = 6 V, hν = 10.29 eV, T d ≈ 205 K, and A = 0.1. The black (solid), red (dash), green (dot), blue (dash–dotted), magenta (dash–dot–dotted), and brown (short dash) in the left -hand and right-hand columns correspond to the magnitude of the varying parameter z o = 30, 50, 100, 150, 200, and 250, and φ o = 0.1, 0.5, 0.8, 1.0, and 1.2, respectively. As understood from the discussion, the significant coupling between floating dust and electrons within the photoelectron sheath is a favourable condition for generating such low-frequency DA modes on the sunlit locations, and these physical conditions strongly depend on dust charge z o and constituent sheath plasma parameters (i.e. density and potential). It is of interest to comment on the influence of the solar wind on the excitation of these DA modes. The solar wind is an essential constituent configuring the electrostatic environment over Moon. Due to larger photoemission current (∼5 μA m −2 ), the surface charging over the sunlit location and sheath population (∼500 cm −3 ) is dominated by emitted photoelectrons, and the solar wind plasma (density ∼5 cm −3 and current ∼10 nA m −2 ) essentially helps in attaining dynamic equilibrium of the constituent charging currents. In the wave generation processes, the relative motion of solar wind to the photoelectron sheath plasma contributes in exciting
Paper: 2020ApJ...893...15A (fulltext)
Thus, while large-scale structure for different types of DM can appear identical, careful attention to structure on subgalactic scales can be a powerful tool to distinguish DM models. A powerful probe of the gravitationally bound structures of dark matter is strong gravitational lensing. Given a matter over or under density, the deflection angle along the line of sight is given by an integral over the induced gravitational potential (Narayan Bartelmann 1997 ),where χ is the distance along the line of sight, χ s is the distance to the source, and is the gravitational potential. In the thin lens approximation (Narayan Bartelmann 1997 ), valid in the limit that the thickness of the lensing galaxy is small compared to the distance to the lens, this takes a simplified form,where D LS , D L , and D S are the angular diameter distances from the lens to the source, from the observer to the lens, and from the observer to the source, respectively, and z is the distance along the line of sight. From this expression one can straightforwardly compute the lensing due to any gravitational potential Ψ. The gravitational potential is in turn determined by matter density via the Poisson equation, ∇ 2 Ψ ∝ ρ . The linearity of this equation implies that the total lensing due to the separate contributions, e.g., of a halo and halo substructure, is simply the sum of the individual contributions. That is,where is the external shear due to large-scale structure, and are the lensing due to the halo and halo substructure respectively. The well studied case is the lensing due to the spherical substructures expected from hierarchical structure formation in the context of noninteracting particle dark matter. However, as mentioned in the 1 , other types of substructure can exist in models of dark matter outside the WIMP paradigm. As a prototypical example, we will consider dark matter condensates, namely superfluids, which exhibit substructure in the form of vortices. We now proceed to develop this in detail. 3. Case Study: Dark Matter Superfluidity The canonical example of a condensate dark matter model is axion dark matter. Axions were introduced as a solution to the strong-CP problem of the SM (Peccei Quinn 1977 ; Weinberg 1978 ; Wilczek 1978 ), and soon thereafter proposed as a dark matter candidate (Abbott Sikivie 1983 ; Dine Fischler 1983 ; Preskill et al. 1983 ). It was later argued that axions could form a Bose–Einstein condensate and exhibit superfluidity (Sin 1994 ; Hu et al. 2000 ; Silverman Mallett 2002 ; Hui et al. 2017 ; Sikivie Yang 2009 ; Berezhiani Khoury 2015 ; Ferreira et al. 2019 ). To emphasize the superfluid nature of axions, we can rewrite the field equations in terms of fluid equations. Being comprised of extremely light particles at an incredibly high number density, axion dark matter is well described by a coherent scalar field. Moreover, because dark matter as we observe it is cold, the system is well described by a nonrelativistic
Paper: 2017ApJ...850..185Y (fulltext)
polarization fraction. Note also that the magnetic pole and equator are the two main contributors to the surface emissions in the current configuration (see the explanations given later). We next discuss the phase-averaged quantities. The semi-amplitudes and polarization fractions are shown in the upper and lower panels of Figure 6 , respectively. Note that, rigorously speaking, the semi-amplitude is not a phase-averaged quantity, but we consider it here just for comparison. In this paper, the semi-amplitude is defined as the quantity related to the total variation of the polarization angle during the rotational period divided by four, which is expressed aswhere the supremum is taken over all possible partitions of the range for ψ . This definition coincides with that given in Taverna et al. ( 2015 ) in most cases but not always (see below). The photon energy is set to following Taverna et al. ( 2015 ). In the left column, the mode conversion is neglected on purpose for comparison with the previous work, whereas it is incorporated in the right column. The results without mode conversion are consistent with those in Taverna et al. ( 2015 ). The discontinuous suppression of the semi-amplitude on the diagonal line observed in our result (see the top left panel of Figure 6 ) but absent in their result is mainly due to the fact that our definition of the semi-amplitude is not completely the same as theirs. Figure 6. Semi-amplitudes of the polarization angle (upper panels) and phase-averaged polarization fractions (lower panels) for different configurations of a rotating neutron star. The left and right columns correspond to the cases without and with the mode conversion, respectively. The photon energy is set to . We explain this in more detail using Figure 7 , which shows how the polarization angle changes with the rotational phase for different combinations of η and γ . Figure 7. (a) Top views of the observed patches (orange circles) on the polarization-limiting surface in the middle row and the polarization directions (red arrows) at different rotational phases in the bottom row for (left), (center), and (right). The trajectories of the (extended) magnetic north pole are drawn in green. Blue arrows and black crosses in the bottom panels indicate the directions of the average magnetic fields at the different rotational phases and the position of the coordinate origin, respectively. (b) Polarization angles as functions of the rotational phase. The angle η is fixed to , whereas the angle γ is changed: . In the middle panels of Figure 7 (a), we schematically draw the top views of the observed patch on the polarization-limiting surface for , , and . The green circles indicate the trajectories of the (extended) magnetic north pole on this surface. Note that they are not exact circles in general. In the bottom panels, we give the corresponding average magnetic fields (blue arrows) and polarization directions (red arrows), which are estimated from the
Paper: 2021ApJ...912...53W (fulltext)
is obtained by taking the sum of the contributions from all dark matter halos We have made use of the python code colossus (Diemer 2018 ) to generate the dark matter halo mass function using the parameterized formula fitting to an N -body simulation proposed by Tinker et al. ( 2008 ), and the concentration–dark matter halo mass relation proposed by Prada et al. ( 2012 ). We have tested systematics by using Raeymaekers ( 2007 ) for N -body simulation fits for the dark matter halo mass function, and Dutton Macciò ( 2014 ) for the concentration–halo mass relation. The results show a different choice of dark matter halo population modeling will not change the final event rate by orders of magnitude. Assuming f PBH = 100%, the result of Equation ( 10 ) is shown in Figure 1 . The main contribution to the integral in Equation ( 10 ) is from low-mass dark matter halos, therefore the result depends sensitively on the lower end for halo mass. To illustrate the dependence on lower mass cutoff , we plot as a function of in Figure 1 . Also note that the integrand of Equation ( 10 ) does not depend on M PBH due to compensation between the primordial black hole number density and the cross section. To determine the lower limit of the dark matter halo mass for M PBH = 0.01/0.1/1 M ⊙ , we follow the criterion by Equation (11) of Bird et al. ( 2016 ) requiring that the dark matter halo evaporation timescale due to dynamical relaxation exceeds the dark energy domination timescale (∼3 × 10 9 yr). This choice results in the lower integral limit of Equation ( 10 ) being 0.3/3/21 M ⊙ for M PBH = 0.01/0.1/1 M ⊙ . Choosing a lighter dark matter halo cutoff would contribute more binary primordial black hole merger events if the halos would not have evaporated at present and the NFW profile is still preserved. High resolution dark matter N -body simulations are needed to precisely resolve the smallest structure of subsolar mass primordial black hole halos but this is beyond the scope of this paper. Figure 1. Event rate for binary primordial black hole merger as a function of the lower cutoff of dark matter halo mass. The markers are the event rate for M PBH = 0.01/0.1/1 M ⊙ , which are 528/140/45 Gpc −3 yr −1 , respectively. With the above choices of lower integral cutoff, as shown in Figure 1 , the event rate for M PBH = 0.01/0.1/1 M ⊙ is 528/140/45 Gpc −3 yr −1 . As a comparison, for M PBH = 30 M ⊙ , the event rate is Gpc −3 yr −1 , as computed in Bird et al. ( 2016 ). Therefore, the merger rate of subsolar primordial black holes increases by one to two orders of magnitude compared to that of 30 M ⊙ . To compare with observations, the sensitive volume, which is defined to be the orientation-averaged volume assuming a match-filtering signal-to-noise ratio (S/N) > 8 (Abbott et al. 2018b ), is Gpc 3 for M PBH = 0.01/0.1/1 M ⊙ for Advanced LIGO at its design sensitivity. Given the above estimated event rate, under our assumption of delta function mass distribution, the
Paper: 2019ApJ...871..177G (fulltext)
spherical target and the stripped mass from the envelope as functions of time. Time is scaled with the mean pusher transit time (≈19.4 ns). The stripped mass is normalized with the total envelope mass and is identified as forward ( v z > 0) traveling mass above the mass center (MC, blue line). Also plotted is the stripped mass obtained when its velocity becomes larger than 4 km s −1 (green line). The evolution of the stripped mass and core velocity obtained with this criterion is shown by the green line in Figure 11 . Our second procedure simply counts as stripped any envelope material with coordinates above the center of mass of the target. This second method leads to the blue line in Figure 11 . As can be seen, the precise criterion to decide when the mass element has been stripped mainly affects the rising part of the curves, which is steeper with the second criterion. Nevertheless, both criteria lead to a similar asymptotic behavior. The curves show a similar overall trend to the astrophysical case presented in Figure 7 . Furthermore, by taking the ratio between the asymptotic core velocity (AACV) and the average ejecta velocity (AEV) this results in values of AACV/AEV ≈ and 1.6 × 10 –2 for the astrophysical and the laboratory simulations respectively, which fulfills Equation ( 9 ). Figure 12 shows a plot of the angular distribution of stripped mass from the two-layer companion/target. The baseline curve labeled t = 0 depicts the mass distribution function of the Cu envelope of the target before the impact (the shell in gray in the sketch) as a function of the axial angle θ (). Figure 12 shows the laboratory and astrophysics distributions at the common time . The lines for the laboratory and astrophysical simulations lie above the reference line, indicating that much of the stripped material is coming from the lower hemisphere of the target. The consequences of using planar or spherical blasts in the laboratory setting are quite evident from the figure, and support the conclusions from the simple toy model stated in Section 2 above. For curved fronts the agreement between the astrophysical and laboratory simulations is excellent. Figure 12. Distribution of stripped mass of the companion star/two-layer target envelope as a function the azimuth measured from its center of mass (CM). The curve labeled “ t = 0” is the initial mass distribution before the collision. The curves labeled “Companion Star” and “Hemispherical” refer to the astrophysical and experimental simulations respectively. For comparison, the simulation of the experiment using a planar blast wave generated with the same laser conditions (labeled “Planar”) is also presented. 5. Discussion and Conclusions We presented the results of a proposed design for a laboratory experiment at high energy density in which a spherically expanding blast wave collides with a spherical target composed of a dense core surrounded by a low-density shell. The experiment was optimized to match
Paper: 2020MNRAS.494.2618L (fulltext)
because, first, the amount of early injected electrons, which has already been well cooled, will be larger, and secondly, the cooling break will appear at lower energy for a larger t age . Figure 5. Influence of pulsar’s initial rotation period P 0 on the predicted nebula extent. See Section 3.1.2 for details. 3.1.3 Braking index The braking index n determines the injection profile of electrons and its influence on the energy-dependent morphology is complex. For a large braking index, e.g. n = 3, the age of the pulsar t age is much longer than the initial spin-down time-scale τ 0 (see Fig. 1 ). The spin-down luminosity in this case decreases as t −2 except at very early time (equation 3 ). For a smaller braking index, the spin-down luminosity decreases more quickly with time for t > τ 0 . However, τ 0 will become comparable or even larger than the age of the pulsar. As a result, the global decline slope of the spin-down luminosity from the birth of the pulsar to the present day is flatter for a smaller n . Note that radiation at smaller (larger) radius mainly arises from electrons injected at later (earlier) time, so a smaller n results in a steeper decrease of the electron density profile and hence leads to a less extended morphology and vice versa. Such a tendency can be seen from Fig. 6 except at the low-energy end and the high-energy end. At the low-energy end, the extent of the PWN is mainly determined by the age of the system, so it is larger for a smaller n . At the high-energy end, the cooling time-scale of electrons is very short (within a few thousand years) so the radiating electrons are just recently injected. In other word, the spatial distribution of high-energy electrons is not sensitive to the long-term temporal behaviour of the spin-down luminosity and hence the curves for all three values of n converge at high-energy end. Figure 6. Influence of pulsar’s braking index n on the predicted nebula extent. See Section 3.1.3 for details. 3.1.4 Spectral index We assume a single power law of index p for the injection spectrum (equation 5 ). The SED shows a peak around GeV that is naturally formed due to cooling of electrons and superposition of electrons injected at different epoch, without invoking a broken power-law function for the injection spectrum. The peak in the SED roughly corresponds to the transition in the slope of the present-day electron spectrum from −3 to >−3, so the injection spectral index p can influence the peak energy in the SED. We show the present-day electron spectrum ( E e versus ) with decomposing the contributions from different injection epoch in Fig. 7 for different p . We note that the spectrum shows a hump structure around the cooling break instead of a simple broken power law. This is because the broken power-law spectrum can be formed via synchrotron or IC cooling only when the injection rate of electrons is constant, while the latter decreases with time as the pulsar spins down. Figure 7. Electron
Paper: 2020MNRAS.491.4325Z (fulltext)
is injected from a planar boundary of a lateral extent large enough to contain the spread of cloud debris but small compared with the wind radius, and collides with a uniform heavy cloud initially embedded in a uniform, dilute ambient gas representing the galactic medium. We denote the velocity and density of that flow by β ws and n as , respectively, but note that this flow may represent either the shocked ambient flow, if cloud crashing and cloudlets acceleration occur in that region, or the wind itself if the shock crossing time of the cloud is considerably longer than the wind expansion time t f given in equation ( 4 ). The planar approximation of the initial flow is justified by the small ratio between the cloud radius to the putative cloud’s distance from the wind’s source, as explained in the previous section. Cooling of the gas is also included in the simulations in a manner specified next. Each simulation has been run for long enough time to allow complete destruction of the cloud and subsequent acceleration of the cloudlets to high velocities. The output data of the simulation gives the spatial distribution of cloud fragments at any given time, their velocities, densities, and temperatures. These data are used to produce column density histograms in velocity space along different lines of sight, which serve us later (equation 9 ) in creating synthetic spectra that can be compared with observed BAL spectra. It also enables us to get a grasp on the general dynamical and thermal evolution of the cloud fragments, and to understand which of the following processes happen faster in different stages of the system: further fragmentation accompanied by shock heating versus cooling. 3.1 Set-up We use the pluto code (Mignone et al. 2007 ) version 4.0 in 2D Cartesian coordinates in the non-relativistic HD module on an ideal gas with an adiabatic index γ = 5/3. We performed several experiments differing in the size of the simulation box, the implementation of cooling and the ratio of densities, κ, between the cloud and the wind. In each of these experiments, the ranges of the Cartesian coordinates are of the form 0 x x max and | y | y max , the unit length is equal to 1 pc and the resolution along each Cartesian direction is 0.01 pc. The initial condition of the simulation is a static circular cloud of uniform density n c with a radius R c = 1, surrounded by a static ambient medium of uniform density n a . The cloud centre is located initially at y = 0 and x = x c > 0, as close as possible to the wind source at x = 0, but far enough to allow the bow shock created by the cloud–wind collision to remain in the simulation box at all times. The boundary condition at x = 0 is a planar flow of uniform density n ws moving at a speed of ws along the x- direction towards the cloud. On the remaining boundaries we use open boundary conditions. It is worth noting that in the absence of cooling the evolution of the system depends only on the density ratio κ =