adsabs/WIESP2022-NER huggingface dataset:
Paper: 2016MNRAS.459..624B (fulltext)
the hyperbolic method initially derived for the diffusion equation (Nishikawa 2007 ) to a 2D set of incompressible dissipative MHD equations. This completes the application to advection-diffusion and Navier–Stokes systems. The hyperbolic formulation of the resistive and viscous terms allowed us to construct a simple finite-volume scheme discretization on a rectangular grid, using an approximate Riemann solver to handle the dissipative and advective flux in the same way. For the sake of clarity, a simple second-order upwind flux formulation is chosen in this work. It is shown that our hyperbolic scheme is able to produce stationary solutions for two non-viscous reconnection problems, namely the magnetic annihilation and reconnective diffusion for which exact analytical expressions exist (Watson et al. 1998 ). The first advantage of this approach over traditional schemes is demonstrated through its consistent accuracy over a wide range of magnetic Reynolds numbers. Indeed, the spatial second order of convergence is closely obtained for the main and gradient variables as well. Secondly, the convergence towards the steady state scales only linearly with the cell width h , giving thus a O (1/ h ) acceleration over conventional schemes. Our hyperbolic method has a clear analogy with the hyperbolic divergence cleaning technique used in MHD codes to handle the divergence-free condition of the magnetic field (Dedner et al. 2002 ). Indeed, an additional numerical scalar variable is introduced in order to advect and diffuse the divergence errors to the domain boundaries. A corresponding scalar equation is also added and coupled to the magnetic field induction equation, leading thus to an hyperbolic system. As a major difference, this latter hyperbolic technique is introduced to handle a numerical term and not a physical dissipation term. It is also implemented in the true time evolution of the system contrary to our pseudo-time implementation. The next step in the development of the hyperbolic method for MHD is the extension to time-dependent solutions. Accurate time-dependent computations are possible by using implicit time integration. Indeed, efficient high-order hyperbolic schemes have been proposed for the advection-diffusion equation, with the idea to converge to successive pseudo-steady states that are solutions at successive physical time steps of the time-dependent equations (Mazaheri Nishikawa 2014 ). In order to speed up the convergence procedure iteration at each time step, the scheme presented in this work would probably require some improvements like higher order spatial reconstruction and/or an implicit pseudo-time solver (Nakashima, Watanabe Nishikawa 2016 ). Such time-dependent solutions also require some spatial or/and temporal dependences of the resistivity profile, which are essential in studies related to solar flares. We have checked that our scheme also works when a spatially localized profile is prescribed on the 1D model
Paper: 2019A&A...622A..39A (fulltext)
objects. Therefore, in the rest of this work, we consider the A-, B-, D-, Q-, and V-type objects as possible taxonomical interlopers and eliminate them from our family. Fig. 2 Top panel : projection in the ( a , e ) domain of 5523 AstDyS asteroids in the region of the Gefion family (orange) and of 2547 members of the Gefion HCM cluster (black). Bottom panel : projection in the ( a , sin i ) domain of the same asteroids. Other symbols are the same as in Fig. 1 . Fig. 3 An ( a , sin i ) projection of the 606 asteroids in the Gefion region with taxonomic information from the SDSS-MOC4 database. The inset figure legend indicates the meaning of the symbols. Open circles indicate asteroids in the background, while filled circles correspond to asteroids of the Gefion family. The Wide-field Infrared Survey Explorer (WISE) and near-Earth object WISE 4 ( Masiero et al. 2011 ; Mainzer et al. 2016 ) were also used in this work to revise asteroid diameters and albedosin the Gefion region. We identified 896 asteroids with geometric albedo ranging between 0.0278 ± 0.0104 and 0.6200 ± 0.1510. We found 658 of them without taxonomic information. About 57.8% of those last albedos are possibly associated with an S-type (they vary between 0.12 and 0.30 with a standard deviation of 0.05). The mean albedo found in the HCM group is 0.2609 and the median one is 0.2598 with an associated standard deviation of 0.17 ( Fig. 4 ). Fig. 4 An ( a , sin i ) projection of the 658 bodies in the Gefion region with albedo information from the WISE dataset. The inset figure legend provides the meaning of the symbols. Considering a simple model with a spherical shape and a uniform density varying according to each class ( DeMeo Carry 2013 ), we find that the effective diameters ( D ) of the members in the HCM group vary between 0.82 and 71.46 km. The total mass of the family is estimated to be 0.791± 0.54 × 10 18 kg. Assuming that the objects on the left-hand side of the family are lost in the 5J:-2A MMR, our family probably originates from the break-up of an S-type asteroid with a diameter D ≈ 128 km, which is inside the allowed diameter range of 100–150 km mentioned in Nesvorný et al. (2009) . 3 Dynamical map We started our numerical exploration on the Gefion family by constructing dynamical maps of synthetic proper elements for 19 000 massless particles in the ( a , sin i ) plane and for 18 360 particles in the ( a , e ) plane. These numbers are chosen to cover an equally spaced grid in each plan. We vary the initial semi-major axis in the interval [2.69463, 2.832] with a step size of 0.0017 au. In the ( a , e ) plane, we increase the initial values of e from 0 to 0.18 with a step of 0.0015, while the initial values of the other elements are fixed to those of (1272) Gefion at the modified Julian date of 57200, which corresponds to June 27, 2015, and are available in AstDyS database. We use the Swift package ( Levison Duncan 1994 ) to integrate the particles over 20 Myr with a
Paper: 2020A&A...639A..70L (fulltext)
b shows the components with frequencies lower than seven cycles per pupil. This cutoff frequency has been chosen to identify the peak at around five cycles per pupil visible in the PSD of Fig. 10 (5c). It has been shown in Sect. 5 that with a FPM, the COFFEE algorithm reconstructs low-order aberrations that should be absent, this phenomenon is also visible on the PSD of Fig. 10 (5b). Fig. 11. Reconstruction error of Fig. 10 (4c). The unit are in radians. ( a ) Same reconstruction error than Fig. 10 (4c), at an optimized scale. ( b ) Orders lower than seven cycles per pupil of the reconstruction error (a), obtained with a cut on the PSD and an inverse Fourier Transform. ( c ) Difference between the phase maps (a) and (b). Patterns of a few cycles per pupil are visible and can be easily identified using a spatial filter. They correspond to the peak at around five cycles per pupil of the PSD of Fig. 10 (5c). It has been shown in Sect. 5 that with a coronagraph, the COFFEE algorithm generates extra low-order aberrations. Conversely, the reconstruction is not efficient for low-order aberrations such as the two cycles per pupil case. The reconstructed phase is very different than the theoretical one, in terms of phase pattern and RMS value (0.29 radians RMS), most of the error coming from low-frequency aberrations (see Table 1 ). This test indicates that there exist some modes that are not or badly visible by COFFEE, mainly when they are blocked by the FPM. The FPM limits COFFEE’s ability to sense and consequently reconstruct low-order modes with spatial frequencies smaller than the spatial extent of the FPM, that is, smaller than 3.5 cy/pup in our case (as a reminder, the FPM has a diameter of 7.2 ± 0.9 λ / D ). On the other hand, COFFEE shows its full capability for modes with higher spatial frequencies than the size of the FPM. 5.3. Impact of segment-level aberrations on the phase reconstruction From the previous study, we can deduce that phase patterns containing low-order variations are not properly reconstructed. Given that segment-level aberrations contain low-frequency components, COFFEE cannot fully reconstruct phasing errors, and can even add low-order background phase aberrations. To theoretically investigate this effect, we reconstruct a segment-level piston error with COFFEE. To do so, we simulate in- and out-of-focus PSFs with a segment-level phase error (piston of 1 rad PV on the central segment) in the same configuration as the HiCAT Iris-AO. These PSFs are used as inputs of COFFEE to reconstruct the phase error. The results are shown in Fig. 12 . Fig. 12. Test of reconstruction of a segment-level phase error with COFFEE in simulation only: ( a ) theoretical phase to reconstruct: a 1 rad piston is applied on the central segment, ( b ) phase reconstructed with COFFEE, ( c ) difference between the two previous phases, ( d ) cross-sections of the three phases of Fig. 12 along the vertical axis. We can notice in particular the
Paper: 2020A&A...644A.138T (fulltext)
modes first increases as the frequency is higher. It becomes maximum at 12.3 μ Hz and decreases rapidly beyond this frequency. Group C-II is composed of four frequencies above 12.5 μ Hz, which have non-consecutive radial orders. Group C-II shows an isolated high peak of about 1 mmag at 12.85 μ Hz, which is indicated by an arrow in Fig. 6 , while the peaks at the other frequencies are only about 0.2 mmag high. It might appear that the two subgroups originate from different types of modes because the amplitude distribution is clearly different between them. With the assumption of m = −1 and the rotation frequency of 14.9 μ Hz (cf. Table 2 ), we may estimate the spin parameters of the frequencies in group C-II to be 12.9 and above, which is consistent with those of Rossby modes with k = −3 (cf. Fig. 1 ). However, we reject this mode identification of group C-II because we fail to construct the diagram with k = −3 that shows clear alignment of points on a straight line (or a smooth curve), in sharp contrast to Fig. 5 . We thus regard all of the frequencies in group C as those of Rossby modes with m = −1 and k = −2. Still, we do not understand why the amplitude structure of these modes changes suddenly around 12.5 μ Hz. If the amplitude is determined by the mode visibility alone, the envelope of the amplitude in group C-I should extend as far as about the rotation frequency of 14.9 μ Hz (cf. Saio et al. 2018 ). The reason for the isolated peak at 12.85 μ Hz also needs to be explained. While we assume that the amplitude is controlled by the physical mechanism of mode excitation and damping, the problem needs to be discussed in more detail in a separate study. We finally note that the similar amplitude distribution of Rossby modes can also be found in other stars, including KIC 3448365 and KIC 9480469 , the spectra of which are shown in Fig. A2 of Saio et al. (2018) . Fig. 6. Lomb-Scargle periodogram of KIC 3240967 around frequencies of group C. The vertical dotted lines indicate the extracted frequencies given in Table A.1 . 4.2. KIC 12066947 We analysed another γ Dor star, KIC 12066947 , based on the diagram. The amplitude spectrum of the star is shown in Fig. 7 , and the extracted frequencies are listed in Table A.2 . These frequencies were obtained by the method described in Antoci et al. (2019) , and are found in five groups, which we refer to as groups U-0, U-1, A, B, and C in the order of frequency. Group B between 26 and 38 μ Hz consists of Kelvin g -modes with m = 1, and has been analysed in Christophe et al. (2018) and Paper I. On the other hand, group A between 19 and 25 μ Hz can be identified as Rossby modes with m = −1 and k = −2, as in Van Reeth et al. (2016) . We note that in contrast to Christophe et al. (2018) , we find the peaks between 56 and 69 μ Hz (group C) to be statistically significant. This is because after more detailed analyses, we discovered that using only data from the quarters Q10–Q17, hence removing Q0 and Q1 due to
Paper: 2018ApJ...865L..14S (fulltext)
of Δ s as a function of time and Mach number. Using the driving routines from Federrath et al. ( 2010 ), we ran an additional M = 3.3 hydro simulation with 50% of the energy in compressible modes, and again found a good fit to the PDF and Δ s evolution with s 0 = 0.7 and Equation ( 7 ) unchanged. Thus τ ev is independent of M and the same for mixed and solenoidal driving, and for all cases it is significantly less than τ eddy . In fact, over the s ≈ s * ± 2 range in which we have made our measurements τ ev ( s ) varies from ≈ τ eddy /4 to ≈ τ eddy /12. Meanwhile, the effect of the Mach number is to increase the width of the PDF and shift its peak as required by mass conservation. 4. Gravity and Star Formation Calculating the rate at which stars form requires extending the model to include gravitational collapse. Current analytic models do this by assigning a critical overdensity, s crit , above which gravity overcomes turbulent motions. In this case, the normalized star formation rate per free fall time becomeswhere φ t is a “replenishment factor” that accounts for the timescale over which the log-normal is assumed to be replenished, and the efficiency factor collapse accounts for the fraction of the mass with density larger than s crit that actually collapses. Various models in the literature make different assumptions about s crit and take s ff = 0 (Krumholz McKee 2005 ), s ff = s crit Padoan Nordlund ( 2011 ) or s ff = s (Hennebelle Chabrier 2011 ; Federrath Klessen 2012 ), calibrating collapse / φ t to direct numerical simulations. Recently, Burkhart ( 2018 ) developed a model in which the log-normal PDF was combined with a high-density power-law tail in a continuous and differentiable way, which has the effect of leading to higher star formation rates for lower power-law slopes, as seen in observations and simulations. This behavior also arises naturally through a simple extension of our Markov model. If we approximate each perturbation as a constant density sphere collapsing from rest from a large initial radius, conservation of energy gives ds/dt = (24 πGρ ) 1/2 . Adding this term to Equation ( 6 ), we arrive at a Langevin equation that describes supersonic, isothermal turbulence including self-gravity:where , and are dimensionless such that and are purely a functions of is the virial parameter with R and M the cloud radius and mass, and is the turbulent driving scale in units of the cloud radius. In the left panel of Figure 3 , we compare the results of this modified Langevin equation with full three-dimensional gravoturbulent simulations (Kritsuk et al. 2010 ). These simulations drive turbulence to a steady state with and a corresponding log-normal distribution with σ s = 1.73, and then “turn-on” the force of gravity abruptly with t ff,0 = 0.7 τ eddy . Figure 3. Left panel: comparison of the Markov results from Equation ( 9 ) with numerical results for the evolution of the PDF of isothermal supersonic turbulence plus gravity. Each
Paper: 2020MNRAS.495..943H (fulltext)
at ∼0.1 arcsec, as illustrated in Fig. 14(d) . Below R ∼ 0.05 arcsec, the effect of absorption becomes very important and complicates the picture, however with no significant impact on the present argument. We checked that this result remains true when considering only one quadrant (north-west, south-west, north-east, or south-east; see Fig. A2 of the appendix) of the sky plane. A natural interpretation of these results is to make the line width, rather than the wind velocity, responsible for the production of large Doppler velocity wings. As thermal broadening cannot produce such large line widths, turbulence must be invoked. The picture would then be of a shell surrounding the zone where stellar pulsation dominates and extending up to ∼10 au, hosting a very turbulent regime. Indeed, such a possibility was mentioned earlier by Kamínski et al. ( 2016 ) in relation with the possible presence of very broad AlO line profiles. According to the standard picture of acceleration by transfer of momentum from dust grains to molecules of gas (Höfner Olofsson 2018 ), this is a region that hosts important shocks. At 10 au, the escape velocity from one (two) solar masses, values commonly accepted for Mira A, is ∼13 (19) km s −1 and the density is such that the velocity acquired by a gas molecule after its interaction with a dust grain is immediately thermalized. Such important turbulence has been observed in a different context by Falgarone Phillips ( 1990 ) and discussed by Gillet et al. ( 1998 ) in the case of pulsating stars. If such a scenario were confirmed, it would require revisiting in its context the cases of the other mentioned AGB stars, L 2 Pup, EP Aqr, and R Dor, which is, however, beyond the scope of this work. In the same spirit of seeking a description of high Doppler velocity wings in terms others than a simple acceleration of the wind, Homan et al. ( 2018a ) have suggested a possible contribution of inelastic collisions in a mixed granular particle fluid. They were first, to our knowledge, to underline explicitly the need for a reliable description of such high-velocity wings and for an explanation of their relative importance in SiO emission with respect to CO emission Finally, we note that the models used by Wong et al. ( 2016 ) and Khouri et al. ( 2018 ) fail to reproduce the large Doppler velocities displayed in Fig. 14 . Indeed, these models do not account for important turbulence. If important turbulence is present between 5 and 10 au from the star, causing a broadening of the SiO line profile at the level of 20 km s −1 full width, this raises the question of the validity of models that ignore it at shorter distances and may significantly impact the conclusions reached by Wong et al. ( 2016 ) and Khouri et al. ( 2018 ) in the close neighbourhood of the star. 9 SUMMARY AND CONCLUSIONS The above study of the morpho-kinematics of the circumbinary envelope of the Mira AB pair at distances between ∼100 and ∼350 au from the stars
Paper: 2018ApJ...858L..19G (fulltext)
ρ instead. aswith B ∥ being the LOS component of the magnetic field. Beck et al. ( 2003 ) derived how an (anti)correlation between ρ and B changes this measurement of the mean magnetic field strength, which is exact for uncorrelated fields. For anticorrelated fields Equation ( 4 ) underestimates the true LOS B . This effect can be observed in Figure 4 (c), where the mean PDFs of the LOS B x (over the 512 2 available LOSs) are shown. With increasing anticorrelation from , , , , to the derived values of 0.1640(8), 0.1589(12), 0.1584(12), 0.1558(8), and 0.1533(11), respectively, deviate further from the real value 0.1667. More strikingly, the PDF of is much more peaked with a standard deviation of 0.058(3) compared to , , , and with 0.103(5), 0.104(4), 0.125(12), and 0.116(5), respectively. 3.4. Supersonic Simulations The supersonic simulations , , and exhibit a very similar behavior to their subsonic counterparts. All of the simulations reach a stationary regime after ≈2.5 T with the same , but different of 2.84(13), 1.96(19), and 1.62(7), respectively. Again, a higher for similar in the saturated regime implies less-effective magnetic field amplification in the case. Similarly, the ratio of compressive versus rotational power in the kinetic energy spectrum decreases from to to . However, the differences are less pronounced compared to the subsonic regime given that there is overall more power in compressive modes as expected from the supersonic regime. The dynamical alignment between velocity and acceleration field in the supersonic regime is virtually identical to the corresponding subsonic simulations shown in Figure 3 (b). Quantitative differences between both regimes are first observed in the ρ – B correlation, as illustrated in Figure 4 (d). and exhibit virtually no correlation with correlation coefficients of 0.04(5) and −0.09(7) whereas still shows a weak anticorrelation with a coefficient of −0.23(3). The logarithmic density PDFs of the supersonic simulations in Figure 4 (e) follow the same trend observed in the subsonic simulations. With decreasing power in the acceleration field the PDFs get a more pronounced negative skew. Finally, derived LOS magnetic field strength measurements are again systematically affected as shown in Figure 4 (f). The derived value in the simulation of 0.630(12) is closest the real value of 2/3 and the PDF is most peaked with a standard deviation of 0.229(17) whereas and underestimate the magnetic field strength with 0.562(19) and 0.544(15), respectively, and generally broader PDFs with deviations of 0.39(2) and 0.46(4), respectively. 4. Discussion 4.1. Unrealistic Large-scale δ-in-time Forcing While the idea of a δ -in-time forcing is appealing on first sight due to its random, uncorrelated nature, we argue that it is unrealistic for two reasons. First, no large-scale process (on some length scale L p ) in nature evolves instantaneously. 8 8 Small-scale processes, such as energy injection from supernovae
Paper: 2019A&A...630A..76G (fulltext)
to estimate the host-galaxy reddening and preferred dust extinction law. First, following Goldhaber et al. (2001) , we measured the stretch factor s that multiplies the temporal axis of the light curve providing the best match of the Hsiao et al. (2007) SN Ia template to the observations. In our case, we restricted the fit to the first 15 days, to avoid the dependence of the light curve on secondary parameters and the light excess detected for SN 2016hnk at later times, and obtained an s = 0.59 ± 0.04. The modified decline rate Δ m 15, s ( B ) is then defined as the magnitude difference between the peak and the reference epoch, 15 days × s (measured in the template), divided by the s factor [Δ m 15, s = , which in our case is = = 1.803 ± 0.200 mag. The measured value of Δ m 15, s ( B ) determines the theoretical model from Höflich et al. (2017) that is used as a reference to correct the B - and V -band light curves, the B versus ( B − V ) and V versus ( B − V ) diagrams, and the ( B − V ) evolution of SN 2016hnk. In the CDMagic fit we fixed the to the value previously obtained with SN OO P Y and the distance modulus measured from the assumed cosmology ( μ = 34.195 mag), and only left as free parameters E ( B − V ) and R V . While the color excess is estimated from the shift between the uncorrected (as observed and Milky Way dust-corrected) and the corrected points that best match the template, the R V is measured from the direction of this shift in the color-magnitude diagrams. In Fig. 9 we show, as an example, the absolute V and vs. B − V diagram, including a number of SNe Ia from the Carnegie Supernova Project I (CSP-I; Krisciunas et al. 2017 ) and the 1991bg-like SN Ia 2005ke (empty triangles) for reference, models from Höflich et al. (2017) with different stretch factor (green lines), and SN 2016hnk data before (blue) and after corrections (red). We obtained an E ( B − V ) of 0.45 ± 0.08 mag, consistent with previous methods, and an R V = 2.1 ± 0.6, which we use hereafter; however, we note that our results are not significantly dependent on the particular choice of R V . Fig. 9. Representative panel for the CDMagic method, where a simultaneous fit in the B - and V -band light curves, the B vs. ( B − V ) and V vs. ( B − V ) diagrams, and the ( B − V ) evolution, is performed. More details are given in, for example, Fig. 13 in Höflich et al. (2017) . We show the absolute V -band vs. ( B − V ) diagram populated with eight normal SNe Ia from the CSP-I ( Krisciunas et al. 2017 ; SNe 2004ef, 2005eo, 2005el, 2005iq, 2005ki, 2005M, 2006bh, 2006D; in differnet symbols), the SN Ia 1991bg-like 2005ke (empty triangles), and the uncorrected (blue) and corrected (red) SN 2016hnk data points. Models from Höflich et al. (2017) are shown in green for reference; the two arrows are the reddening vectors representing the direction to which the data points are shifted when affected by R V = 3.2 and R V = 1.0. SN 2016hnk corrected data points lie on top of the