adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019ApJ...881...65R (fulltext)
the combined energy range of 10 eV–6 keV. The lower energy corresponds to proton velocities of about 44 km s −1 . This means that pristine LISM neutral H atoms cannot be seen by IBEX unless it moves in the direction opposite to the LISM (Heerikhuisen Pogorelov 2011 ). The highest energy corresponds to velocities of about 1000 km s −1 . Bulk velocities of this magnitude are not observed during SW–LISM interaction, except perhaps in coronal mass ejections. However, thermal velocities of this order are possible beyond the TS. An interesting feature discovered by IBEX is the narrow region of enhanced ENA flux, which is now referred to as the IBEX ribbon. Before any explanation of this phenomenon was proposed, it was noticed that the directions toward the ribbon correlated with the line-of-sight directions perpendicular to the ISMF lines draping around the HP in the simulation of Pogorelov et al. ( 2009 ). One of the possible explanations of the ribbon flux was mentioned by McComas et al. ( 2009b ) and Schwadron et al. ( 2009 ), and described in more detail by Heerikhuisen et al. ( 2010 ). It is based on the assumption that PUIs born in the LISM region in front of the HP due to charge exchange of the neutral SW H atoms with interstellar ions, if injected with a pitch angle close to 90° to the draped ISMF direction, preserve their anisotropy long enough to produce ENAs propagating toward IBEX in the plane perpendicular to . This scenario is supported by numerical simulations, when anisotropy is assumed. We also note that the relative brightness of the ribbon is highest at energy ∼1.1 keV, which corresponds to the speed of slow SW of roughly 460 km s −1 . In this paper, we test the crucial assumption of this IBEX ribbon scenario, namely that the anisotropy of a realistic PUI distribution can be preserved long enough to experience another charge exchange. We use fully kinetic particle-in-cell (PIC) simulations to investigate the time evolution of the PUI distribution function in the outer heliosheath (outer) just outside of the HP. The initial distribution function is derived from the neutral H simulations performed by Heerikhuisen et al. ( 2016 ) and takes into account PUIs of the neutral SW and IHS. 2. PUIs in the Outer Heliosphere The ribbon mechanism developed by Heerikhuisen et al. ( 2010 ) requires that the PUI distribution function in the OHS preserves a certain degree of anisotropy for a sufficiently long time for a subsequent charge exchange to occur (∼2 yr for 1.1 keV H ions). This is not quite obvious because ring-beam distributions, typical for ions externally inserted into a background plasma, tend to scatter onto a shell distribution (Williams Zank 1994 ). Heerikhuisen et al. ( 2010 ) assumes that the ring-beam distribution is scattered onto a partial shell distribution depending on the injected pitch angle. In contrast, Chalov et al. ( 2010 ) neglects scattering, but appeals to magnetic mirroring effects that ensure spatial localization
Paper: 2016ApJ...817..105K (fulltext)
( 2011 ) do show the formation of cores within 1 Myr. From Equations ( 31 ) and ( 32 ), is required for km at 5 AU in 10 MMSN. Figure 4 shows (or α for ), which can prepare the conditions for the formation of Jupiter given by –300 km at 5–6 AU in disks with . Saturn’s core forms in the disk after the formation of Jupiter. The gap opening by Jupiter assists the rapid growth of Saturn’s core at the outer edge of the gap, where the radial drift due to gas drag is negligible. The collisional fragments produced during core formation accelerate the growth of the core, and the supply of fragments produced in the outer disk induces further rapid growth. These effects allow the rapid formation of Saturn’s core within years after gap opening by Jupiter, and the rapid formation may explain the massive core of Saturn (Kobayashi et al. 2012 ). For the rapid growth of Saturn’s core in 10 MMSN, 100 km radius or smaller planetesimals are required, which is satisfied for ( for ) in the 10 MMSN disk (see Figure 6 ). This upper limit is similar to the condition for Jupiter. If Jupiter can be formed via other mechanisms in the MMSN disk, then kilometer sized or smaller planetesimals are required for the rapid formation of Saturn’s core, which corresponds to for . Figure 6. Turbulence strength or α , required for the formation of Mars, Jupiter, and Saturn for the MMSN (top panel) and 10 MMSN (bottom panel). The formation of the core required for Jupiter via core accretion is difficult to accomplish with the MMSN. The value of α is estimated under the assumption of . On the other hand, the formation age of Mars estimated from the amount of W isotope in the mantle is 2–4 Myr (Dauphas Pourmand 2011 ). For 10 MMSN (MMSN), the early formation of Mars may be explained by planetesimals with radii of km (5–30 km) (Kobayashi Dauphas 2013 ). The disk inside the snow line ( AU) has (Hayashi 1981 ). From Equations ( 31 ) and ( 32 ), the formation of Mars requires at 1 AU ( at 1.5 AU) in the 10 MMSN disk, while 10 −4 –10 −3 is suitable at 1–1.5 AU in the MMSN (see Figure 6 ). In addition, main-belt asteroids with radii smaller than 50–100 km have a slope of , which is similar to the outcome of collisional cascade (Bottke et al. 2005 ), while larger main-belt asteroids except for the largest bodies (Ceres, Vesta, and Pallas) have shallower slopes. The main-belt asteroids, except for the largest asteroids, have a peak of in the radius range of 50–100 km. The onset of runaway growth causes a similar shaped peak around the runaway radius (see Figures 1 and 5 ). Assuming that the radius of 50–100 km in the main belt corresponds to the planetesimal radius, at 2 AU ( at 3 AU) in the 10 MMSN and at 2 AU ( at 3 AU) in the MMSN. In Figure 6 , the constraints on the turbulence strength ( or α ) are summarized based on these considerations. Jupiter cannot be formed via planetesimal accretion in the MMSN; therefore, there is no hatched region for Jupiter in the MMSN. If the solar system
Paper: 2016ApJ...830L..33M (fulltext)
In contrast, for cycle 22, the south dominates over the north for small sunspots, but gradually the difference minimizes as we reach toward larger sunspots. Though, we must emphasize that there are cycles for which we do not see any large change in the two hemispheres for any sunspot size range. Cycles 16, 17, and 23 are examples of such cases. In order to quantify the asymmetry between the two hemispheres, we use the “north–south asymmetry index” ( A ns ) as ( S an - S as )/( S an + S as ), where S an and S as are the yearly averaged sunspot area values for the northern and the southern hemispheres, respectively. We plot the time variation of A ns for different sunspot sizes in different panels of Figure 4 . At a first glance, we notice that the variation of A ns is very smooth for the smallest sunspot sizes (Figure 4 (b)), as compared to any other size range (Figures 4 (c)–(f)). This can be understood from the definition of A ns , which varies between ±1, depending upon the presence of at least one single sunspot in any of the hemispheres and absence of any spot in the other hemisphere. Since the smaller sunspots occur more frequently at the time of solar minima compared to the appearance of a large sunspot, the variation of A ns is relatively smoother in the former case. We also notice (from Figure 4 (a)) that at the maximum phase of cycle 21, A ns , has values very close to zero as in the case of cycle 23. However, at the cycle 22 maximum, A ns attains a maximum value of ≈−0.5. This is consistent with the results as obtained by Javaraiah ( 2005 ). This scenario changes a little as we move from small to large sunspot sizes. There is a hint of long-term variation in the asymmetry index. For the largest-sized sunspots (Figure 4 (f)), the northern hemisphere dominates in the cycle minima until cycle 20, after which the southern hemisphere dominates. This trend is also visible in Figure 4 (e). Figure 4. Time evolution of the asymmetry index, A ns , for corresponding sunspot sizes. 3.3.1. Periodicities in the N–S Asymmetry Since the solar cycle has a dominant period of 11 years, it is expected that the N–S asymmetry index will also have some periodicity. There has been a lot of work done in order to probe this periodicity (Carbonell et al. 1993 ; Ballester et al. 2005 ; Chang 2009 ; Javaraiah 2015 ; Ravindra Javaraiah 2015 ). Here, we revisit the periodicity in the N–S asymmetry for different sunspot sizes. We use the A ns curves, shown in different panels in Figure 4 , and the wavelet analysis in order to obtain the periodicities in A ns . Results of the wavelet analysis on the different A ns curves is shown in Figure 5 . The global wavelet power (time-averaged power as shown in the “wavelet power spectrum panel”) is calculated along with a significance level of 90%. This significance level is obtained for the white noise (Torrence Compo 1998 ), and contours are overplotted on top of the wavelet spectrum in order to highlight the region
Paper: 2019MNRAS.490.5739X (fulltext)
of reionization, then decreases during the late stage. When the IGM global neutral fraction drops below , on the small scales a positive cross-correlation appears. At this stage, most of the IGM has been ionized, while the neutral hydrogen in haloes starts to dominate the cross-power spectrum, producing the positive correlation seen in the small-scale power spectrum. As the remaining neutral islands in the IGM shrink and decrease in number, the halo H i come to dominate the fluctuations on larger and larger scales, as a result this positive-to-negative transition point on the cross-power spectrum moves to larger scales. Figure 4. The cross-power spectrum between the neutral fraction and the dark matter density. The solid and the dashed lines represent positive and negative values, respectively. The cross-power spectrum between the 21cm brightness temperature and the DM density are plotted in Fig. 5 . We use the two panels to show the early and late stages of reionization, respectively. We find that at the very beginning of reionization, the 21cm-DM cross-power spectrum is positive on all scales, implying that the neutral hydrogen density fluctuations still follow the DM density fluctuations, although some rare density peaks have been ionized. As more and more IGM around density peaks becomes ionized, the 21cm-DM cross-power spectrum decreases, and transits to negative starting from the largest scales. Then, the large-scale 21cm-DM cross-power becomes more and more negative. The amplitude reaches its maximum at around , and decreases again. Different from the f H i -DM cross-power, the 21cm-DM cross-power spectrum is always positive at small scales, indicating that when weighted with the local density, the small-scale 21 cm signal is always dominated by the H i in haloes. At the very late stage of reionization, the positive-to-negative transition scale increases rapidly. Figure 5. The 21 cm brightness temperature and the dark matter density cross-power spectrum. Top panel: early EoR; Bottom panel: late EoR. The solid and dashed lines represent the positive and negative values, respectively. In this plot, the 21cm-DM cross-power spectrum at z = 9.90 shows some oscillations, which are likely statistical fluctuations. To check this interpretation, we estimate the errors on cross-power spectra using the jackknife resampling method. In Fig. 5 , we plot the errors for the z = 9.90 and z = 9.00 curves. To avoid clutter, errors of other curves are not shown there. We see that indeed at z = 9.90 the error bars are larger than the oscillation feature on the power spectrum curve, so it is likely that these are just statistical fluctuations. We also check that at z = 9.00 the errors are comparable with the z = 9.90. However, at z = 9.00 the cross-power spectrum is much larger than z = 9.90. In logarithmic axes apparently its errors are less obvious than z = 9.90. We then turn to the individual contribution from the IGM and haloes to the 21cm power
Paper: 2020MNRAS.494.2905V (fulltext)
light-to-Alfvén speed ratio is fixed at c / v Ai = 10 4 . The top panel shows the normalized real part of the wave frequency, and the bottom panel shows the normalized imaginary part of the wave frequency as functions of the normalized wavenumber k . The vertical lines in the bottom panel mark the positions of kd e = 1 for the given mass ratios. In Fig. 3 , we show the dispersion relations for the FM/W wave in quasi-parallel direction of propagation. This mode shows the typical quadratic whistler-mode behaviour at . The FM/W wave is dominated by electron dynamics unless kd i ≲ 1. With decreasing m i / m e , ω r /Ω i decreases while | γ |/Ω i increases. We attribute this behaviour to increasing cyclotron damping by electrons, which is, for the quasi-parallel FM/W wave, most efficient when n = −1 in equation ( 17 ). Thermal electrons with v ∥ ≈ − w e thus efficiently resonate with the quasi-parallel FM/W mode when in our normalization. In the electron–positron case with m i / m e = 1, a significant number of electrons resonate with the FM/W mode at ω r /Ω i = ω r /|Ω e | ≲ 1 and at kd i = kd e ≲ 1 (see fig. 2 by Gary Karimabadi 2009 ). Like in the case of the quasi-parallel A/IC wave, the resonant cut-off of the dispersion relation for ω r → |Ω e | is also present in the cold-plasma limit (Stix 1992 ). The quasi-parallel FM/W mode reaches the wavenumber range kd e ≥ 1 with | γ | ω r for all mass ratios shown in Fig. 3 . Figure 3. Dispersion relations of the FM/W wave in quasi-parallel propagation for different values of m i / m e . The light-to-Alfvén speed ratio is fixed at c / v Ai = 10 4 . The plot follows the same format as Fig. 2 . In Fig. 4 , we show the dispersion relations for the FM/W wave in quasi-perpendicular direction of propagation. The dispersion relation does not depend significantly on m i / m e for m i / m e ≳ 10. The quasi-perpendicular FM/W wave reaches the ion-Bernstein regime at ω r /Ω i ≈ 1. In the perpendicular limit ( θ → 90°), the susceptibilities for both ions and electrons include sums over all n of contributions that are each proportional to 1/( ω r − n Ω j ), leading to this resonant behaviour. As long as m i / m e ≫ 1, electron harmonics do not interfere with the ion harmonics since all contributions to decrease with increasing n . For m i / m e = 2, however, the second-order ion resonance makes a contribution to that is comparable with the first-order electron resonance. The quasi-perpendicular FM/W mode reaches the wavenumber range kd e ≥ 1 with | γ | ω r for all shown parameter combinations with m i / m e ≤ 125. Figure 4. Dispersion relations of the FM/W wave in quasi-perpendicular propagation for different values of m i / m e . The light-to-Alfvén speed ratio is fixed at c / v Ai = 10 4 . The plot follows the same format as Fig. 2 . 2.2 Dependence on light-to-Alfvén speed ratio We now investigate the dependence of the dispersion relation on c / v Ai . For this study, we fix the mass ratio at m i / m e = 100
Paper: 2017MNRAS.466.4813C (fulltext)
and model-independent approach. To compare e red and l orb with the data, we follow the procedure of Forero-Romero etal. ( 2013 ), drawing contours in the e red l orb plane to identify those regions of the parameter space that are compatible with current observations. The contours are generated through 10 7 Monte Carlo (MC) iterations, where at each step the values of velocity, mass and distance are drawn from a Gaussian distribution within the intervals shown in Table 7 . The value of v rad was taken from van der Marel etal. ( 2012 ), r from van der Marel Guhathakurta ( 2008 ), while M MW and M M31 are consistent with van der Marel etal. ( 2012 ) and Boylan-Kolchin etal. ( 2013 ). Due to the large discrepancy existing among the and the MC was ran twice using the two different estimates. It turns out that the intervals generated with are extremely large and possess no constraining power, so that we do not consider them in the following analysis. Table 7. Values and intervals used to generate MC contours. M MW , M M31 are expressed in 10 12 h 1 M units, interhalo distance r in h 1 kpc while v rad and v tan in kms 1 . The intervals on r and v rad correspond to the 2 values of van der Marel etal. ( 2012 ), while for v tan they were chosen in agreement with (Sohn etal. 2012 ). In Fig. 2 , we bin the Mod1 objects in the e red l orb plane and compare them to the observational 95percent confidence intervals. The fraction of objects f LG that falls within those boundaries is shown in the last column of Table 8 . This quantity allows us to gauge the ability of a model to account for the observed global dynamics of the system disregarding its transient state. We note that the number for CDM varies between 0.15 and 0.19 which is consistent with Forero-Romero etal. ( 2013 ), who found a 0.08 to 0.12 fraction at the 68percent confidence interval. In the case of modified gravity cosmologies, the results largely confirm the conclusions obtained with the previous analyses of velocities. In fact, models such as FofR04, FofR05, SymmB and to a lesser extent FofR06 produce LG-like pairs within the observational boundaries at substantially lower rates than CDM. In particular, the fraction of Mod1 LGs falling within the 95percent confidence level is reduced by a factor of 3 in SymmB and around a factor of 2 in FofR04 and FofR05, showing from another perspective that the LG dynamics can only be poorly accounted for within these models. These discrepancies are explained by the increased velocity of M31, affecting the values of e red [through the kinetic energy term in equation ( 30 ) and l orb , which tend to shift the distribution farther away from the region allowed by the data]. Figure 2. LG-like pairs versus MC contours showing the 1 and 2 confidence levels. Pairs have been chosen and MC intervals have been generated using the low v tan estimate of Sohn etal. ( 2012 ). Table 8. Energy, angular momentum, spin parameter and fraction of Mod1 pairs falling within
Paper: 2017ApJ...845..109Z (fulltext)
changing from the inner to the outer an MC based on the fitting results. There is a roughly increasing trend of the tangential velocity away from the axis of the MC that continues in the ambient solar wind. However, this increase is not strictly monotonous, which may be attributed to fluctuations. 3. Possible Dependence of the Viscosity on Solar Wind Parameters 3.1. Data and Method The above analysis has shown some viscosity signature from a rough statistic. Here we study the role of viscosity further through detailed investigation of the change of the tangential velocity crossing the MC boundaries. By manually checking the change of the 1 minute averaged tangential velocity profile near the front/rear boundaries of the MCs, we further narrow down the sample by selecting the crossings during which the tangential velocity clearly increases from the inner to the outer the MC. Finally, nine crossings that we think represent the picture fairly well in Figure 1 are selected and shown in Figure 3 . A few data points indicated by the red symbols in Figures 3 (a), (b), and (d) are discarded in the following analysis because they deviate significantly away from the main trend of the crossings due to inevitable fluctuations. Figure 3. Change of the tangential velocities with time. Panels (a)–(d) show the change of the tangential velocities at the front boundary of the MC, and (e)–(i) at the rear boundary. The verticle solid line indicates the boundary of the MC. The ordinate axis is the tangential velocity, where the positive value means the anti-clockwise rotation and the negative value means the clockwise rotation. It is not clear how to obtain the viscous coefficient in the solar wind. Alternatively, we use the following formula to roughly assess the strength of the viscosity between the ambient solar wind and MC plasmain which is assumed to be a function of the viscous coefficient , and and are averaged tangential speeds in MC and solar wind, respectively. The formula follows the idea that if there is no viscosity should approach zero as well as , and if there is a strong viscosity should be equal to , leading to be infinitely large. Considering that there is more or less uncertainty in determining the boundary of an MC, we calculate the values of and based on the measurements within one and a half hours next to the boundary with the nearest half an hour ignored. The uncertainty in is propagated from the uncertainties in the averaged tangential velocities and . The averaged solar wind parameters, e.g., density, temperature, and turbulence intensity of the magnetic field, inside and outside of the boundary are also calculated within the same intervals to investigate the possible dependence of the strength of the viscosity between the ambient solar wind and MC plasma. 3.2. Results The scatter plots between and solar wind parameters inside and outside of the MC are shown in Figure 4 . The turbulence intensity of the magnetic field is estimated based on
Paper: 2017MNRAS.469.1949L (fulltext)
the following, we summarize the procedures used in this paper to simulate the auroral radio emission arising from a stellar magnetosphere shaped like a non-central dipole. To calculate the magnetic-field vector generated by an offset dipole, the magnetic-dipole moment is located in a generic grid point ( O ) within the star, identified by its spatial coordinates ( x , y , z ) in the native reference frame Oxyz , where the origin O coincides with the centre of the star, the z -axis is parallel or coincident with the dipole axis, and the x -axis lies into the plane parallel to the z -axis and containing the rotation axis. In the reference frame anchored with the offset dipole, the new coordinates ( X , Y , Z ) of the grid points are defined by the simple coordinates translation ( x x , y y , z z ). In the translated O XYZ reference frame, the Z -axis coincides with the dipole axis and the X -axis is located in the plane passing through the Z -axis and parallel to (or containing) the rotation axis. The corresponding magnitude of the magnetic-dipole moment is defined as follows: where r Z is the distance of the offset dipole from the stellar surface along the direction parallel to Z -axis, and B p is the polar magnetic-field strength, that was fixed equal to 3000G. The three components ( B X , B Y , B Z ) of the magnetic-field vector are calculated in each grid point taking into account the non-central position of the magnetic-dipole moment. In the new reference frame O XYZ , the magnetic vector components are easily calculated using the equations of the simple dipole (see appendix A.2 of Trigilio etal. 2004 ). The magnetic vectorial field is then rotated in the observer reference frame Ox y z , where the origin O coincides with the centre of the star, the x -axis is parallel to the line of sight, and the plane y z coincides with the plane of the sky. The method was described in appendix A.3 of Trigilio etal. ( 2004 ). In the reference frame ( O XYZ ) anchored with the offset dipole, it is also possible to locate the grid points falling inside the auroral cavity using the equation of the dipolar magnetic-field lines, defined by using the equation r L cos 2 , where r is the distance of the grid points to the origin O (where is located). Following the method described in Paper I , it is then possible to find those grid points that radiate auroral radio emission within a beam pattern that intercepts the line of sight and that are tuned at the assigned simulation radio frequency . To take into account the non-central position of the magnetic-dipole moment ( O O ), the distance to the centre of the star from the grid points that are able to radiate detectable auroral radio emission has to be larger than the stellar radius. The star shadowing effect has also been taken into account. APPENDIX B: OFFSET DIPOLE: SIMULATIONS The simulations of the auroral radio-emission visibility from a non-central dipolar field were performed assuming the model parameters