adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020MNRAS.492.5870K (fulltext)
such as the Schmidt–Kennicutt relationship between the surface density and the star formation rate (Schmidt 1959 ; Kennicutt 1998 ) or the density threshold for star formation (Lada, Lombardi Alves 2010 ). In particular, the observed star formation rates for several of these clouds are an order of magnitude below those predicted by these relationships between the density and the rate (Longmore et al. 2013a ). A case in point is the cloud GCM-0.02-0.07 ( M ∼ 10 5 M ⊙ ; Johnston et al. 2014 ) nicknamed the Brick because of its high optical depth in the infrared and lack of internal infrared emission from star formation. Only 3–4 per cent of the mass of this cloud is in dense star-forming regions often called high-mass cores. The elliptical ring also includes the massive star-forming region Sgr B2 with some of the highest rates of star formation in the galaxy and the region Sgr C rich with H ii regions indicating earlier active star formation. Fig. 5 shows the locations of the clouds on their elliptical orbit. Figure 5. Schematic of the clouds in elliptical orbit in the Galactic Centre adapted from Kruijssen et al. ( 2014 ). The view is of the plane of the Galaxy from above. [Kruijssen, Dale Longmore ( 2015 ) suggest that the orbit is not exactly elliptical in that the path from Sgr C may not close back to the location of the Brick.] The clouds on the ring orbit in the direction from the Brick toward Sgr B2. The Brick and other clouds with low star formation rates are near the pericentre, 45 pc from the supermassive black hole Sgr A* (Johnston et al. 2014 ), while Sgr B2 is at the apocentre, twice as distant at 100 pc. Longmore et al. ( 2013b ) suggest that the Brick and nearby clouds are currently undergoing tidal compression that will result in intense star formation by the time the clouds arrive at the location of Sgr B2. The mini-starburst Sgr B2 would then be the result of compression in its earlier passage through the pericentre of the orbit. Observations indicate that the clouds on the orbit between the Brick and Sgr B2, the dust ridge clouds, may be just beginning to fragment (Walker et al. 2018 ). Alternatively, fragmentation and ultimately the star formation rate may be controlled by the turbulent-entropic instability. If orbital shear slows the effective turbulent dissipation rate by supplying fresh turbulent energy, then the difference in orbital shear along the elliptical orbit from pericentre to apocentre may prevent star formation near the pericentre and allow it at the apocentre. Following Kruijssen et al. ( 2014 ), we derive a rotation curve, v circ = 23.5 r 0.38 km s −1 from the mass distribution measured around the Galactic Centre by Launhardt, Zylka Mezger ( 2002 ). At the radial distance from the Brick to the Galactic Centre, 45 pc, the orbital shear across a 10 pc diameter cloud is Δ v = 8.4 km s −1 , while the one-dimensional turbulent velocity dispersion in the Brick is 4.3 km s −1 (Johnston et al. 2014 ). At the
Paper: 2021MNRAS.504.1427A (fulltext)
from September 22. Additionally, in the image related to the observation from September 22, there is a radial polarization pattern across the Southern half of the core region. This suggests that the magnetic field B is roughly circular and centred on the brightness peak of the core, as one might expect from a helical field when one views it down the axis. This polarization pattern remained through 2016 March. The γ-ray LC from the Fermi -LAT and X-ray LC from the Swift -BAT do not show any obvious flux enhancement during the time of the radio flaring activity, although they show some activity (both BAT and LAT) about 40 d before the radio flare. During this time, there were no optical or VHE observations because of the Sun. Figure 4. A sequence of total (contours) and polarized (colour scale) intensity images of Mrk 421 at 43 GHz obtained with the VLBA. The image is convolved with an FWHM of 0.24 × 0.15 mas 2 along PA = −10°. The global total intensity peak is 329 mJy/beam, and the contours 0.35, 0.70, 1.4, etc. up to 89.6 per cent of the global intensity peak. The colour scale is the polarized intensity, and the black line segments within each image show the direction of the polarization electric vector, with the length of the segment being proportional to the polarized intensity values. The black vertical line indicates the position of the core. See Section 3.2 for details. The low polarization fraction at 43 GHz on 2015 August 1 implies that the magnetic field was very highly disordered in the core at this epoch. A radial polarization pattern, as measured at 43 GHz on 2015 September 22, can result from turbulent plasma flowing across a conical standing shock, as found in the simulations of Cawthorne, Jorstad Marscher ( 2013 ) and Marscher ( 2016 ). However, in such a scenario the linear polarization pattern is always present, since it is created by the partial ordering of the magnetic field by the shock front. Periods of polarization across the entire core should not be observed. An alternative picture ascribes the radial polarization pattern to the circular appearance of a magnetic field with a helical or toroidal geometry that is viewed within ∼0.2/Γ radians of the axis of the jet (Marscher et al. 2002 ), where Γ is the bulk Lorentz factor of the emitting plasma. In this case, the ratio of the observed polarization measured in the image, , to the value for a uniform magnetic field direction, , 6 implies that the helical field is superposed on a highly disordered field component that is ∼10 times stronger. If the helical field becomes disrupted by a current-driven kink instability, particle acceleration could cause a flare (Nalewajko 2017 ; Zhang et al. 2017 ; Alves, Zrake Fiuza 2018 ). The polarization pattern then becomes complex, with the possibility that the polarization becomes very low at some point (Dong, Zhang Giannios 2020 ). Such a flare would be expected to start at X-ray and VHE γ-ray energies upstream of the core, then
Paper: 2018MNRAS.473.2415S (fulltext)
open circles joined with dotted line are for shock free accretion solutions. See the text for details. Until now, we have studied the various properties of accretion shock waves around black holes. These shocks are non-dissipative in nature as the specific energy remains conserved across the shock front (Chakrabarti 1989 ). Indeed, shocks of this kind are radiatively inefficient as well. Apparently, the realistic shock waves are likely to be dissipative, where a part of accreting energy is escaped from the shock location through the disc surfaces. This essentially causes the reduction of the overall specific energy profile in the PSC region (Singh Chakrabarti 2011 ). The plausible mechanism that perhaps regulates the energy dissipation at the shock is the thermal Comptonization process (Chakrabarti Titarchuk 1995 ) that eventually reduces the thermal energy in the PSC. Based on the above consideration, we assume that the loss of energy across the shock front is proportional to the temperature difference between the immediate pre-shock and post-shock flow and therefore, the energy loss across the shock front is calculated as (Das, Chakrabarti Mondal 2010 ) where a and a denote the immediate pre-shock and post-shock sound speed and f represents the fraction of the available thermal energy lost at shock. In this analysis, we treat f as a free parameter and chose its value as f 0.998 for representation. Evidently, this is the measure of energy dissipation across the shock (Das etal. 2010 ; Singh Chakrabarti 2011 ; Sarkar Das 2013 ; Kumar Chattopadhyay 2013 ). We then compute the maximum energy dissipation () by freely varying all the flow parameters and plot it as function of in for various values of accretion rates in Fig. 12 (a). Here, solid, dotted and dashed curves represent the results corresponding to , 0.075 and 0.1, respectively. We observe that for a given accretion rate (), initially shows gradual increment with in , which eventually approaches towards a saturation value in the gas pressure dominated domain. Meanwhile, it was shown by numerical simulation (Machida etal. 2006 ) that in the magnetically dominated state, accretion disc becomes cool as compared to a gas pressure dominated disc. This clearly indicates that the gas pressure dominated flow is expected to possess higher thermal energy content and accordingly, we obtain enhanced when in is larger. Moreover, as is increased, cooling becomes more effective in the flow resulting the lowering of thermal energy content. Therefore, for a given in , remains smaller for flows having higher . When in is large, the effect of synchrotron cooling due to the increase of accretion rate becomes practically insignificant. Hence, when in 10 4 , asymptotes to a saturation value irrespective to the flow accretion rate. Figure 12. Variation of maximum energy dissipation at shock () as a function of at inner sonic point ( in ) for three different values of accretion rates, , 0.075 and 0.1 denoted by
Paper: 2018ApJ...863...68L (fulltext)
( 75 ) and ( 78 )) normalized by the total number of mergers. In these two examples, we set χ 1 = χ 2 . The top panel is for a 0 = 100 au (see Figure 19 ) and we include merging systems with the number of mergers is 673 for e out = 0, 790 for e out = 0.3, 1159 for e out = 0.6, and 2828 for e out = 0.9, so that N max = 5450. The lower panel is for a 0 = 20 au, and we include systems with the number of mergers is 146 ( e out = 0), 180 ( e out = 0.3), 227 ( e out = 0.6), and 411 ( e out = 0.9), so that N max = 964. The other parameters are the same as in Figure 19 . If the distributions of and are uncorrelated, as we may expect to be the case for , when the octupole effect is significant (see Figure 9 and the discussion in the last paragraph of Section 4.2 ), the distribution of χ eff can be derived directly from and , the distribution functions of and . Define where is the maximum possible value of χ eff for given m 1 χ 1 and m 2 χ 2 (this maximum is achieved at ). Then Equation ( 75 ) becomeswhere and . Note that and . Given P 1 ( μ 1 ) and P 2 ( μ 2 ), the distribution function of is In the special case when μ 1 and μ 2 are uniformly distributed, we have P 1 = P 2 = 1/2, and Equation ( 80 ) giveswhere we have assumed without loss of generality. Thus, even for uniform distributions of and (see the case of e out = 0.9 in Figure 19 ), the effective spin parameter χ eff is preferentially distributed around χ eff = 0 (see Figure 20 ). Note that the spin–orbit misalignment distribution and χ eff distribution obtained above refer to relatively wide BH binary systems ( a 0 ≳ 10–100 au) that experience a merger due to large LK eccentricity excitation. Such systems necessarily have . For BH binaries with smaller separations ( a 0 ≲ 1 au) and not much less than unity, the spin–orbit misalignment distribution can be quite different (see Liu Lai 2017 ). Antonini et al. ( 2018 ; see also Rodriguez Antonini 2018 ) have carried population studies of BH mergers in triple systems (based on DA secular equations) and have found a similar peak around χ eff = 0 in the χ eff distribution. Rodriguez Antonini ( 2018 ) also showed an example of the final spin–orbit misalignment distribution with a peak around 90°, in qualitative agreement with our result. They did not distinguish the difference in the spin–orbit misalignment distributions between largely quadrupole systems (small ε oct ) and strong octupole systems (large ε oct ). We do not agree with the reason(s) they gave for the peak in the χ eff distribution. In particular, it is important to recognize that, during the orbital decay, the adiabaticity parameter (Equation ( 61 )) transitions from ≪1 to ≫1, and this transition determines . 5. Summary and Discussion In this paper we have studied BH binary mergers in triple systems. A sufficiently inclined tertiary companion excites large eccentricity in the BH binary orbit through gravitational perturbations (the LK mechanism), significantly shortening its
Paper: 2017A&A...604A..72Q (fulltext)
conclude that the errors assigned by Reid et al. (2014) to and are conservative, and there do not appear to be any bias, given the available maser samples so far. For the rotation curve, the situation is somewhat different. Although the values found for are very close to the initial values adopted, the statistical spread is larger than expected. Reid et al. (2014) reported an error of 0.4 in the rotation curve which is optimistic compared with our findings. From our simulations, we would constrain the rotation curve value as given the BeSSeL data sample selection. The larger error possibly indicates that our assumed velocity distributions are too wide. Fig. 10 Marginalized posterior probability density distributions for correlated circular velocity parameters from 100 simulated galaxies mimicking the BeSSeL sample. Left panel : circular orbital speed of the Sun. Middle panel : orbital angular solar speed. Right panel : difference between the circular solar motion and average source peculiar motions. When more sources are added to the sample selection (sets A and B), Fig. 8 shows an initial improvement in the accuracy of the fundamental Galactic parameters. For set A, the errors in , , and can improve up to , , and , respectively, when more northern hemisphere sources are added. In contrast when southern hemisphere sources are also added (set B), the errors can decrease to , , and , respectively. Further improvements would require much better astrometry for weak sources, requiring for example much more sensitive observations. 4.2.2. Solar motion and average source peculiar motions Figure 7 shows the distribution obtained for solar velocity components (, , and ). The uncertainties derived were around 2 for all solar velocity components with respect to the initial parameters. For , the standard deviation found was 1.2 , which compares well to the results published by Reid et al. (2014) (i.e., ). In addition, the spread for and are narrower with values of 2.4 and 0.5 , respectively. Compared to the BeSSeL results (i.e., and ), we can affirm that the solar motion results published by Reid et al. (2014) have conservative estimates. For the radial and tangential average peculiar motions ( and ), Table 4 shows that indeed these peculiar velocities can be fitted with high accuracy using the simulations; however, the relative spreads are high compared with other parameters (see Fig. 7 ). Even so, compared to the BeSSeL results (i.e., and ), the errors in the simulated sample selection are still lower (i.e., and ). The reason for higher dispersions for and could be related to the number of parameters that must fit independently. The parameters and have largely the same effect on the observations for nearby sources and therefore are directly correlated (see Table 5 ). For , the correlation is high with two components (, ), which affects the fitting and hence the estimated accuracy. 4.2.3. Parameter correlations Pearson product-moment correlation
Paper: 2020ApJ...896...62G (fulltext)
The real-time duration of the video is 9 s. (An animation of this figure is available.) The first column of Figure 2 presents the evolution of the photospheric continuum I c . We can observe that this expanding structure begins to form a central dark spot (Δ t = 252 s), which eventually grows in size while the EG splits into two smaller granules (Δ t = 504 s). Then, the southernmost descendant granule located at undergoes the same evolution (Δ t = 756 s), until it splits into four granules (Δ t = 1008 s). The second column of Figure 2 shows the integrated signal of V measured . The emerging flux region first appears at Δ t = 0 s, being located at [5′′, 4′′]. Magnetic field patches with apparent opposite polarity appear close to each other. These magnetic patches expand and occupy the EG area (Δ t = 252 s), showing a pattern of alternate polarities (salt-and-pepper or sea-serpent configuration). Some of these flux kernels intensify: notably, this occurs in the region where the first dark spot is forming (Δ t = 504 s). Later, this structure becomes an intergranular lane (Δ t = 756 s). Doppler velocity maps, displayed in Figure 2 (third column), highlight the plasma motions in the region. They indicate that the structure is generally characterized by conspicuous blueshifts, corresponding to upflows up to 1–1.5 km s −1 . In the central dark patch, downflows of ≃0.5 km s −1 are found (Δ t = 504 s and, partly, at Δ t = 1008 s), in agreement with previous observations. The same coherent behavior is also visible in the online movie. The maps of magnetic flux density (Figure 2 , fourth column) reveal that the distribution of the magnetic areas derived from V integrated is representative of the real magnetic flux patches. Indeed, a mixture of polarities is found where V integrated maps show an apparent salt-and-pepper configuration, suggesting once again that the quantity described in Equation ( 3 ) is a good qualitative proxy for the longitudinal component of the magnetic field, . The evolution of magnetic flux density qualitatively follows that of V integrated , as mentioned above. A polarimetric analysis of the dead-calm region (see Martínez González et al. 2012b ) in FoV QS , used as a control box for comparison, is presented in Figure 3 . This region of very quiet Sun entered the IMaX FoV after the first 200 s of the EG evolution. In Figure 3 (top panels) we present the maps of the integrated signal of V measured , at the same representative times shown for FoV EG in Figure 2 (second column). No new magnetic structures are seen to emerge during the analyzed time series. Figure 3 (bottom panel) shows a plot of the mean polarization signal averaged over FoV EG (black symbols) within the region of interest occupied by the EG inside FoV EG (red symbols), and over FoV QS (empty circles), along the sequence. The signal in FoV EG is on average 2.7 times stronger than in FoV QS . Moreover, the polarization signal in FoV QS remains almost constant during the
Paper: 2020MNRAS.498.5745T (fulltext)
formation of multiple populations, as we are going to outline in the next. Figure 17. Left-hand panel: Difference between the mass-loss of 2Ge and 1G stars, , against the F 275 W − F 814 W colour extension of the HB. The groups of M3- and M13-like clusters are represented as orange and blue dots, as labelled. Right-hand panel: Present-day and initial cluster mass against the colour extension of the HB (top and bottom panels, respectively). 5.3 Extra-mass-loss in the 2Ge as a tracer of the multiple populations formation The discovery that 2Ge stars of all GCs lose more mass than the 1G (Fig. 11 , see also Tailo et al. 2015 , 2019b , a ) and, in particular, the correlations between the extra-mass-loss Δμ e and the extra-helium content of the 2Ge population δ Y max (Fig. 12 ) and the total (present-day and initial) mass of the clusters (Fig. 13 ) represent powerful constraints for the mechanisms of formation of multiple stellar populations. Since evolutionary effects alone cannot provide such increase (see Appendix D ) the need of an additional mechanism arises, allowing us to gain information on the environment where the distinct generations were born. Tailo et al. ( 2015 ) discuss, in the specific context of the blue hook morphology of ω Cen, how the formation environment of the different multiple populations can affect the stellar evolution during the RGB phase, mainly by affecting the initial stellar rotation. Here, we apply the same scenario to the less extreme environments encountered by the 2Ge of most clusters (see the schematic illustration in Fig. 18 ). Very young low-mass stars ( M /M ⊙ ∼ 0.1−1.0) behave as the T -Tauri stars in the Galactic field, pre-main sequence stars in the convective phase of contraction towards the main sequence. T -Tauri’s typically rotate with a period of 1–12 d, and their rotation rate is maintained constant in time by the magnetic coupling of the star with the accretion disc (magnetic disc-locking, see Armitage Livio 1996 ). Bouvier et al. ( 1997a ) and Bouvier, Forestini Allain ( 1997b ) developed a model to explain why main sequence stars in young clusters (such as α Persei) show a wide distribution of rotation velocities. They showed that, the earlier would pre-main sequence stars break the magnetic disc-locking, the faster would be their rotation when they reach the main sequence. Afterwards, during the main-sequence phase, the surface rotation rate (together with the rotation of the whole convective envelope) slows down thanks to the angular momentum loss associated with the stellar winds, but the core mostly preserves its fast rotation. Figure 18. Schematic interpretation of the different rotation of 1G and 2G stars. During the pre-main-sequence evolution, the rotation of the central contracting star is magnetically locked to the rotation of its residual accretion disc, having a diameter of 50–100 au. While the 1G formation occurs in an ambient of moderate density (yellow region), where the disc–disc
Paper: 2017ApJ...838...53B (fulltext)
good for travel distances of about 8°–20° with measurement errors increasing with increasing travel distance. At very low travel distances (about Δ 6°), it can be seen that the forward-modeled travel times do not fit the measured ones. The reason for this effect lies in the fact that the simulated data only incorporates modes with harmonic degree l ≤ 170. This cut-off acts as an additional filter at a harmonic degree at which the filter with the lowest phase speed (L170) is centered. The modes selected by the filter thus do not form a proper wave packet, which may lead to artifacts in the cross-correlations (see, e.g., Zharkov et al. 2006 ). Additionally, it is noteworthy that the quality of the match of the power spectrum (compare matched versus unmatched) does not have a large effect on the travel times at higher travel distances (Δ > 8°). At smaller travel distances (Δ 8°), however, one can observe a substantial effect of the quality of the match of the power spectrum on the travel times. We note here that this conclusion may be different for other flow models. We also note that the absence of a proper averaging of multiple kernels in distance and latitude has a considerable effect for smaller travel distances (Δ 10°) but seems to vanish for the largest distances in the case of the flow model considered here. For comparison, forward travel times using ray kernels are also shown in Figures 5 and 6 . It can be seen that the magnitude of the ray kernel travel times is, in general, very similar to those obtained using Born kernels. However, Born kernels seem to better reproduce some features in the measured travel times, e.g., jumps in the magnitude of the travel times from one filter to another such as from Δ = 221 to Δ = 241 (see the middle panel in Figure 6 ). Because the given flow model varies on rather large length scales, a relatively good performance of the ray kernels is expected. This may be different for meridional flow models, which vary on smaller length scales, see Section 1 . We also note here that the measured travel times obtained using the filter with the highest phase speed (L035) encounter a spurious constant offset, see Figure 7 . This offset was corrected for by substracting the mean travel time at each distance. Such an offset is not present in real solar data (Kholikov et al. 2014 ; Jackiewicz et al. 2015 ). In Jackiewicz et al. ( 2015 ), this offset was not noticed in the simulated data, as a few distances had been dropped from the analysis, which resulted in an equivalent correction. The origin of this offset is not completely understood but may be connected to the relatively coarse spatial resolution of the simulated data (256 pixels on 180°). Figure 7. Measured travel times, without offset correction. Left and middle panels: same travel times as displayed in Figure 5 (top left and top middle panels). Right panel: measured GB04 and forward-modeled travel times from the Born kernels for all travel distances, at