adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020ApJ...898L..36L (fulltext)
to the formula J = n e e ( V i − V e ), where n e is the electron number density and e is the elementary coulomb charge, respectively. Inside the first current sheet, both MMS2 and MMS4 observed mainly out-of-plane electron current J M ′ (Figures 2 (d), (n)). The current densities associated with the reversal of magnetic field component B L ′ peak at 0.7 μ A m −2 and 0.4 μ A m −2 , respectively. Similar trends to electron velocities mean that the current is mainly carried by electrons. Multispacecraft timing analysis (Schwartz 1998 ; Dunlop et al. 2002 ) indicates that the current sheet moves sunward in the current sheet normal direction (where N = L ′ M ′ N ′ (0.3747, 0.0649, 0.9249)) with a speed of 52 km s −1 . The derived normal direction is identical with the N ′ calculated from minimum variance analysis. The duration for the current sheet is ∼0.18 s (∼05:57:08.6 and ∼05:57:08.78 UT). Thus, the thickness of the current sheet is calculated to be 52 km s −1 × 0.18 s = 9.36 km ∼5.5 d e , where d e is the electron inertial length. In the turbulent magnetosheath, similar intense small-scale current sheets are favorable sites for electron-only reconnection (Stawarz et al. 2019 ). Four-spacecraft timing analysis can also be applied to gain quantitative information about the orientation and motion of the flux rope. In the case of a cylindrically symmetric flux rope, it can be shown that the times at which each spacecraft observes the peak field strength define a plane that is perpendicular to the tetrahedron direction of motion and contains the axis of the flux rope. The flux rope is found to be moving at v ∼ 43.5 km s −1 along n = (0.83, 0.26, 0.49) GSM relative to MMS. The velocity of the flux rope is found to be mainly in the x – z plane, indicating that the axis of the structure is nearly in the y direction. This orientation is also consistent with the result calculated by MVA also shown previously. As mentioned above, the flux rope crossing time is 4 s during 05:57:07 UT and 05:57:11 UT. This corresponds to a crossing distance of 174 km based on the normal speed of 43.5 km s −1 of the flux rope, or 2.26 d i . This indicates that the spacecraft are observed an ion-scale flux rope. As previously analyzed, the spacecraft are slightly off-center when crossing the flux rope. This means that any estimate of the flux rope size is a lower bound. MMS2 observed well-defined parallel electric fields (Figure 2 (f)), which indicated the invalidation of the ideal electron frozen-in condition ( E ′ = E + V e × B = 0). The electron dissipation measure ( J · E ′) is calculated in Figure 2 (j) based on current densities and electric field in the electron frame. J · E ′ is positive within the current sheet and is close to zero when MMS crossed outside the exhaust region. The electric field structure is parallel to the background magnetic field at MMS2. The dissipation is positive and basically all dominated by . The energy dissipation is mainly
Paper: 2021MNRAS.502.6080O (fulltext)
that there is a background and foreground sources contamination. Fig. 9 shows the stellar position of the proper motion members and Fig. 10 the parallax histogram. It can be seen that they are distributed throughout the analysed region. Figure 8. Histogram of stellar proper motion probability in the left overdensity region. The dotted line corresponds to 1σ a members, the full line indicates the lowest value of the probability of the members. Figure 9. Stellar positions of the proper motions members of the left overdensity (black dots). The red squares represent the location of the known O members. Figure 10. Histogram of parallaxes of 1σ a proper motion members in the left overdensity region. Table 7. Left overdensity proper motion members up to G = 17.5 mag. The columns gives the position (α, δ), the Gaia DR2 identifier, the parallax π and its error σ π , the proper motion components and their errors μ α cosδ , , μ δ , and the G magnitude. Full table is available online. 5.3 Open cluster (bottom overdensity) We now focus on the bottom overdensity. We first eliminate the Cygnus OB2 proper motion members obtained in Section 4.2 . After applying the procedure of Section 3 , the parameters of the new group are obtained (see Table 5 ). The membership probability of the stars is calculated by equation ( 4 ). 25 proper motion members of the new stellar group are identified (see Table 8 ). The index E takes the value of 0.94, this indicates that the group and field stars are almost completely separated. Fig. 11 shows the stellar position of the proper motion members. It can be seen that this group is an open cluster, whose centre is (α, δ) = (306.99820°, 40.54240°).The radius of 9 arcmin is determined from the radial surface density profile of the members (Fig. 12 ). Figure 11. Stellar positions of the proper motions members of the open cluster (black dots). The red squares represent the location of the known O members. Figure 12. Stellar density profile of the open cluster (bottom overdensity) as a function of the distance from the cluster centre. Then, we calculate the mean parallax of the members π a and estimate the distance d π a . Both values are presented in Table 5 . The estimated distance d π a = 1457 ± 11 pc indicates that this group is also located in the foreground of the main Cygnus OB2 group. Besides, an inspection of the magnitudes in Table 8 reveals that this open cluster is formed by faint stars. Recently, Castro-Ginard et al. ( 2020 ) found 582 new open clusters distributed along the Galactic disc performing a blind search for open clusters using Gaia DR2 data. We noticed that one of these objects, UBC585, corresponds to this open cluster. Our mean parallax and mean proper motion are in good agreement with the values obtained for UBC585, while we find eight more members for UBC585. Table 8. 25 proper motion members of the open cluster (bottom overdensity). 6 CONCLUSIONS In the Cygnus OB2 region, we analyse a sample of stars from
Paper: 2018MNRAS.480..610G (fulltext)
The two measurements are not inconsistent taking into account the large errors in both of them, however they suggest that weak CIV systems may have lower bias than strong ones, which would help explain why the individually detected CIV systems have a higher bias than the overall population. Next, we comment on the result of Blomqvist et al. ( 2018 ), who have simultaneously completed a similar analysis to ours including eBOSS data. Apart from having a larger data set (especially at low redshift), there are other differences in the two analyses. One is the different continuum-fitting methods. Blomqvist et al. ( 2018 ) apply the same continuum-fitting method used in other BOSS analyses of the Lyα forest, hence the need for a distortion matrix in their work (i.e. their Section 5.2 ). They also use an upgrade from the Baofit code we use called picca , 4 although this should not affect the results. Their fitting range extends to a larger scale than ours: from 10 to 180 , compared to our range from 5 to . We focus on comparing their result for the bias and redshift distortion factors when restricting the rest-frame wavelength range to 1420 Å Å, the same one we have used which avoids the contribution from SiIV lines. The mean result of Blomqvist et al. ( 2018 ) for their entire redshift range is β c = 0.35 ± 0.21, and b c (1 + β c ) = −0.019 ± 0.002. In comparison, we obtain a higher value of the redshift distortion factor, β c = 1.09 ± 0.56, although the large errors do not make the two values incompatible. Our value for the bias b c agrees with theirs: from our equation ( 20 ), if we use their central value β c = 0.35, we find that our measurement b c (1 + 0.44β c ) = −0.017 implies b c (1 + β c ) = −0.0199 ± 0.0015 for β c = 0.35, in full agreement with Blomqvist et al. ( 2018 ). The low central value of β c found by Blomqvist et al. ( 2018 ) is not consistent with our constraint derived from the bounds in from measurements of the incidence rate of CIV absorbers as a function of their equivalent width in Section 7.1 , implying 0.6 β c 1. Taking into account the measurement error, however, their result can be compatible with this constraint. It is worth noting that their result for β c is substantially different when dividing the data set into two redshift intervals. For low redshift ( 2.2 and a mean effective redshift eff = 1.69), they find β c = 0.05 ± 0.19, whereas for high redshift ( > 2.2 and eff = 2.41, closer to our measurement with eff = 2.29) their result is β c = 0.67 ± 0.29. Their result at high redshift is therefore fully consistent with ours, and with our derived constraint from the value of . The very low value of β c at low redshift derived by Blomqvist et al. ( 2018 ) suggests that the absorber bias factor b τ c may be increasing rapidly with decreasing redshift, perhaps because CIV is being destroyed in low-mass haloes and being formed in massive haloes. However, more data at low redshift that allow reduced error bars and
Paper: 2021A&A...646A.134J (fulltext)
of prominence evolution involving B oriented across the 2.5D plane, or in full 3D (e.g., Hillier et al. 2012a , b ; Terradas et al. 2015 ; Keppens et al. 2015 ; Kaneko Yokoyama 2018 ; Popescu Braileanu et al. 2021 ). Nevertheless, we expect the misalignments between the pressure and density gradients to be present in 2.5D and therein induce motions inline with the RTI but on scales of the order of a flux tube. Evolution of the plasma in this way may also be characterised using baroclinicity, a component of the evolving vorticity ω that arises in a flow with anisotropic temperature, This baroclinicity considers that a misalignment between the pressure and density gradients gives rise to the rotation of plasma as it slides down a constant-pressure gradient. In general, plasma unstable to the RTI in a direction other than vertical may be identified using this approach. The fourth row of Fig. 3 details the evolution of the baroclinicity within the condensing material. At t = 7264.6 s, the signature of baroclinicity covers a broad region but one that is focussed at the periphery of the condensation region and forms a ‘U’ shape. As the evolution progresses, this ‘U’ shape is amplified in magnitude, but also becomes flat and increasingly localised to the bottom of the forming monolithic structure. In the density panels, at t = 7436.2 s we see that the two early condensations develop a curvature as they move towards the concave-up portions of the field topology. This is consistent with the baroclinicity-induced rotation. It is important to emphasise here that vorticity maps quantify, generally, the rotation of the velocity field (induced by some physical process), whereas the baroclinicity maps show the source of vorticity and represent a physical process in itself. Hence, maps of the vorticity would likely also show this rotation but would not offer information on the driving process. 3.1.3. Evolution of the monolithic prominence Figure 5 details the evolution of the now-formed prominence after the initial condensation period. Initially, between 7607.8 t 8294.2 s, it can be seen that the condensation region significantly deforms the field topology of the host flux rope as the concentration of highest density falls from an altitude of ≈9–6 Mm with an average velocity of 5 km s −1 . The largest magnetic field strength is recorded within the condensation at this time, with a value of ≈4.2 G. The falling material drags the associated field lines down and those field lines that lie directly below the condensation are compressed. Shortly after t = 8294.2 s, reconnection is triggered just above the densest portion of the falling monolith, creating a magnetically isolated region of high density that continues to fall, now with a velocity of ≈10 km s −1 . Behind this falling ‘plasmoid’ 3 , another high-density region forms in its wake, and at t ≈ 8500 s reconnection is again triggered behind this new region. Once the new density enhancement is isolated
Paper: 2019ApJ...878...24G (fulltext)
( 1996 ) and find thatThis implies that the disk is not very thin. From the vales in Equations ( 7 )–( 9 ) we can calculate the typical neutrino cooling timescale in the disk. Taking the neutrino cooling rate as (Brown Weingartner 1994 ) , we calculate the neutrino cooling timescaleWe calculate the radial velocity in the disk from mass accretion rate and find it to be . The inflow time of the gas in the disk from is . Namely, neutrino cooling in the disk is important. From the above results we can safely conclude that our derivations are self-consistent and that neutrino cooling plays a major role in the accretion disk of our scenario. Neutrino cooling lowers the temperature of the accreted gas, reducing the pressure and ensuring a mildly electron degenerate state, as in the scenario of Siegel et al. ( 2019 ). From Beloborodov ( 2003 ) the electrons become degenerate below the characteristic degeneracy temperature ofThis is somewhat higher than the typical temperature we find in Equation ( 9 ), implying that we have a mildly degenerate electron gas as Siegel et al. ( 2019 ) find in their scenario. Moreover, according to Figure 1 of Beloborodov ( 2003 ), the baryonic matter in the accretion disk of our scenario is in nuclear statistical equilibrium and is dominated by free nucleons with an electron-to-baryon ratio of Y e 0.3, i.e., a large fraction of neutron to protons. Overall, we conclude that the accretion disk conditions in the CEJSN r -process scenario resemble the conditions from the studies of Kohri et al. ( 2005 ) and Siegel et al. ( 2019 ). We expect then the outflow properties to be quite similar. 5.3. Nucleosynthesis in Jets As we mention above, at the stage of high mass accretion rate the core mass inner to the orbit of the NS is ≈1 M ⊙ . A large fraction of this mass is expected to be accreted at this high rate. As described in Section 2 , Papish et al. ( 2015 ) estimated that the jets in the CEJSN r -process scenario are expected to synthesize a mass of ≈0.001–0.01 M ⊙ of heavy r -process elements per event. This is based on the assumption that a fraction of ≈10% of the accreted mass is launched in the jets and ≈10% of the mass in the jets is transferred to heavy r -process elements. If we had used the recent results of Siegel et al. ( 2019 ), the mass of the r -process elements would have been even higher. Siegel et al. ( 2019 ) find the ejected mass to be ≈30% of the accreted mass. Under this assumption, we can take a more optimistic yield than what Papish et al. ( 2015 ) used, namely, we can take the yield per CEJSN r -process event to be ≈0.01–0.03 M ⊙ of heavy r -process elements. As we mentioned in Section 2 , Chevalier ( 2012 ) estimated the rate of events in which an NS enters the envelope of a giant star to be ≈1% of the total rate of CCSNe. Papish et al. ( 2015 ) found that if this is also the rate of CEJSN r -process events, then combining it with the yield of r -process elements that they derived, we obtain the
Paper: 2017A&A...599A.108M (fulltext)
) shows that these photodissociation products are likely to dominate the gas mass, since molecules including CO are photodestroyed very rapidly after release (in less than 100 years) due to the strong UV radiation environment found around an A0V star. The same radiation field is able to ionise these atomic species, explaining the detection of CII around several debris discs (e.g. Riviere-Marichalar et al. 2014 ). HR 4796 A was also searched for atomic oxygen (OI) and ionised carbon (CII) emission by Herschel ( Meeus et al. 2012 ), which set upper limits of 6.3 and Wm, respectively. Due to the low gas densities expected in this scenario, species with high critical densities such as CO and OI are expected to be out of LTE, meaning that fluxes are dependent on the density of collider species and on the radiation field as well as on the gas temperature and OI mass (Eq. (7) in Matrà et al. 2015 ). This means that the derivation of OI mass upper limits from the observed fluxes is highly degenerate; the Herschel observations therefore cannot tightly constrain the oxygen mass in these low-density discs. On the other hand, taking electrons as the dominant collider species (as shown to be likely in secondary gas discs, Matrà et al. 2015 ), CII has a much lower critical density of order 5–10 electrons cm ( Goldsmith et al. 2012 ). The electron density predicted by this exocometary model in HR 4796 A is much higher (of order at the location of the belt, Kral et al. 2017 ). This means that LTE is a good approximation for CII, yielding a mass upper limit between for the same 20100 K temperature range considered above for CO. However, our secondary gas model predicts an average ionised fraction in the disc that is very low (), meaning that we expect a much higher total carbon mass in HR 4796 A ring of 0.19 . Then, assuming the dominant molecule released by the comets to be CO, yielding a C/O abundance of one, we can estimate the total gas mass in the secondary origin scenario to be 0.44 . However, we note that the Kral et al. (2016) model shows that the bulk of the gas will rapidly expand viscously to form an accretion disc. This means that only 6% of this total gas mass (0.03 ) will be co-located within the AU wide dust ring. In summary, the secondary model predicts a gas mass co-located with the ring of 0.03 , whereas in the primordial scenario we expect a total gas mass of at most 0.11 . On the other hand, SED modelling of the ring’s infrared excess ( Milli et al. 2015 ) yields a total dust mass in the ring of 0.5 distributed among particles between 1 m and 10 mm. Regardless of the gas origin scenario, we therefore derive an upper limit on the gas to dust ratio at the ring’s location of 0.2, which rules out the presence of gas in sufficient amounts to explain the observed ring morphology in the HR 4796 A system. A similar conclusion was recently achieved for the narrow HD 181327 debris ring ( Marino et al. 2016 ), where very low levels of
Paper: 2020MNRAS.494...50C (fulltext)
Via the introduction of a physical model of gravitational structure growth, the inference of the non-linear dark matter distribution today can be posed as a statistical initial condition problem. To do so, the borg algorithm seeks to infer the cosmic initial conditions from which present 3D structures in the distribution of galaxies have formed via non-linear gravitational mass aggregation. The borg algorithm explores the posterior distribution of large-scale structures, consisting of a Gaussian prior for the initial density field at an initial scale factor of a = 10 −3 and a choice of bias model and likelihood metric at scale factor a = 1. The evolution of the initial density fields can be related to the present galaxy (or halo) distribution via a first- or second-order Lagrangian perturbation theory (LPT or 2LPT) or a full particle mesh model of gravitational structure formation (for details, see Jasche Wandelt 2013 ; Jasche Lavaux 2019 ). By exploiting non-linear structure growth models, the borg algorithm naturally accounts for the filamentary structure of the cosmic web typically associated with higher order statistics induced by non-linear gravitational processes. Furthermore, the posterior distribution accounts for systematic and stochastic uncertainties, such as survey geometries, selection effects, unknown noise, and galaxy biases as well as foreground contamination (see e.g. Jasche Wandelt 2013 ; Jasche et al. 2015 ; Lavaux Jasche 2016 ; Jasche Lavaux 2017 ). In this work, we use the borg algorithm to sample the initial conditions of the dark matter density field using LPT to evolve the field to the dark matter conditions today and then rely on the neural bias model to sample the parameters of the NPE to provide a field that is most informative about the halo mass distribution function inferred from a halo catalogue. A detailed schematic of the interconnection between parts of the model is shown in Fig. 1 . The stochastic uncertainties are assumed to be Poissonian using the likelihood in equation ( 16 ), and further study into direct learning of the deviations from Poissonity will be conducted in an upcoming work. Figure 1. Schematic of the borg algorithm with the neural bias model. Initial conditions for the density field in Fourier space, , are drawn from a prior given a cosmology , . These are then evolved forward using a deterministic prescription, in this example using LPT. The evolved field is then transformed further using the NPE that requires parameters that are drawn from a prior . This provides a field from which the halo mass distribution function can be described using the MDN with parameters drawn from a prior . This halo mass distribution function is then compared to the masses of haloes M obs from the observed halo catalogue. 5 RESULTS To examine the techniques developed in this paper, we will consider a relatively simple mock run using a simulated dark matter distribution and minimal working neural bias model.
Paper: 2019ApJ...870...87B (fulltext)
for density ρ , velocity , the specific entropy S , and the magnetic vector potential are given by where P is the pressure with , which is defined up to some additive constant, c p and c v are the specific heats at constant pressure and density, respectively, T is the temperature with being the ideal gas equation of state, K is the thermal diffusivity, ν is the kinematic viscosity, η is the magnetic diffusivity, is the magnetic field with b 0 being the imposed field, and is the current density that was already defined in the introduction. We adopt a polytropic stratification with background temperature , so T = 0 at z = 0. We fix d and choose to set the degree of stratification. The smaller the , the stronger the stratification, i.e., the stronger the temperature contrast. In the following we choose , so the temperature changes by a factor of 10; see Hurlburt et al. ( 1984 ) for a similar setup. We choose to be either negative or positive, corresponding to the northern or southern hemispheres, respectively. A vertical magnetic field is imposed and tangled by this velocity field. The simulation setup is similar to that of Hurlburt Toomre ( 1988 ), except they did not include rotation, which makes our present simulations therefore closer to those of Brandenburg et al. ( 1990 ), which did include rotation. In the following, we denote by the density at . Some of the parameters are listed in Table 3 . The imposed magnetic field points in the z direction and is given by , where is the thermal equipartition field strength. We use in all cases. The Rayleigh number is defined as , where and are density and the specific entropy of the hydrostatic solution in the middle of the domain. Table 3 Parameters for Convection Simulations Cross sections of , , and near the surface are shown in Figure 10 for the results of such a simulation. One sees cyclonic convection in the northern hemisphere as viewed from the top, so all converging inflows attain a counterclockwise swirl, and all diverging outflows have a clockwise swirl. A similar appearance is also attained by the magnetic field. If we viewed this pattern from beneath, we would see a mirror image of the original pattern and therefore the opposite sign of the B polarization. The consequence of this can be seen in Figure 11 , where we plot and for north (red) and south (blue) for Runs A and B, whose parameters are summarized in Table 3 . There is now a systematic EB correlation, so is positive in the north and negative in the south; see Table 4 . This is very promising and agrees with our intuition. Figure 10. Magnetic field vectors and the line-of-sight component (color-coded; left) as well as E -mode (middle) and B -mode polarization (right) in rotating convection viewed from the top onto the convecting layer. Figure 11. (top) and (bottom) for convection simulations corresponding to the northern (red) and southern (blue) hemispheres for Runs A (thin lines) and B (thick lines). The gray shades indicate