adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018ApJ...856...41R (fulltext)
of the system is maintained. When a dust grain is placed on a highly eccentric orbit by radiation pressure, it may create a local pressure maximum but not linger in a nearby circular orbit where it can stabilize the gas through drag backreaction. This may provide a second possible explanation for the difference between runs D and F discussed above, especially given the higher mass of dust used in run F, which may lead to drag backforces that are strong enough to destabilize gas from a circular orbit. In Figure 12 , we show the dust surface density every 50 orbits for 400 orbits. We find that the photoelectric instability sets in quickly, but has no obvious secular effect on the global structure of the disk. The behavior is dominated by transient clumps and arcs that appear and disappear on timescales of orbits to dozens of orbits. Figure 12. Dust surface density over time for run F. The photoelectric instability arises quickly and is continuously redistributing dust into nonaxisymmetric structures throughout the disk over the full course of the simulation. Though the radial distribution of the dust varies nonsecularly over time, the 2D dust structure is consistently dominated by clumps and arcs that form and dissipate on short timescales of ≈1–10 orbits. In order to confirm that the dust structures seen in run F (Figure 8 ) are conclusively attributable to the photoelectric instability, we run two additional models: one without photoelectric heating, and one with an increased dust resolution to ensure model convergence (run I). For run I, we double the number of dust superparticles while reducing the mass of each superparticle by half in order to maintain the same overall dust mass. The run without photoelectric heating shows no clumping behavior, and instead yields a smooth, structureless dust distribution, as one would expect. Run I, on the other hand, shows the same clumping behavior as seen in run F, suggesting that the resolution we use in runs A–F is sufficient. Gas and dust surface densities after 400 orbits are shown in Figure 13 . Figure 13. Gas (left panel) and dust (right panel) surface densities after 400 orbits for run I, which is a high-dust resolution (2× superparticles) duplicate of run F (). The dashed circle indicates (100 au). Doubling the number of superparticles yields substantially similar gas and dust spirals and clumps, suggesting that 800,000 superparticles are sufficient for capturing the effect of the PeI in the presence of radiation pressure (for the chosen grain size distribution). In run H, we duplicate run F (Figure 8 ) but double the birth ring width to verify that the dust clumping behavior of run F does not require a narrow birth ring. Dust and gas surface densities after 400 orbits for run H are shown in Figure 10 . We find that the enhancement in the dust-to-gas ratio produced by the photoelectric instability is weaker in run H compared with run F (see Table 1 ), but the qualitative results—nonaxisymmetric,
Paper: 2019A&A...622A.164G (fulltext)
in Appendix B . The final result is plotted in Fig. 5 and the derived parameters are listed in Table 5 . The final rms is small with only 90 m s −1 . Our revised orbital parameters are in rather good agreement with the literature, within 1.5 σ ( Groenewegen 2013 ; Gorynya et al. 1995 ; Evans et al. 1990 ; Abt 1959 ). Fig. 5. Result of our combined fit of the orbital and radial pulsation velocity for FF Aql. Left panel : fitted (solid lines) and measured primary (blue dots) orbital velocity. Right panel : fitted (solid line) and measured (blue dots) radial pulsation velocity. Table 5. Pulsation and orbital parameters of the FF Aql system. Notes. Parameters are the same as in Table 4 . a 1 : semi-major axis of the Cepheid’s orbit relative to the centre of mass. f ( M ): the mass function. (a) Not fitted. (b) Uncertainty not given by the authors. (c) Gorynya et al. (1995) . We noticed a slight difference in the systemic velocity between our estimate and that from Gorynya et al. (1995) . This might be linked to the wide companion, however, this might also be from other non-astrophysical effects. We should be cautious when studying long-term variations of v γ , unless a clear pattern is observed. Differences in v γ of the order of 0.5–1 km s −1 might be caused, for instance, to the way RVs are determined (cross-correlation, bisector, ...), the mask used, and the instrument zero points. In this paper we did not perform such a long-term study as this is out of the scope of this paper and would require a complete analysis of all available literature data. U Car. The companion orbiting this Cepheid is below our detection level. Our datasets enable us however to exclude any companion with a flux ratio higher than 0.4%, which would correspond to a spectral type earlier than B2V (see Table 3 ). Y Car. Our possibly detected companion has a flux ratio of ∼0.94% and would correspond to a ∼A0V spectral type, which is in rather good agreement with the ∼B9V derived by Evans (1992c) . Although additional data are still necessary to confirm, this possible detection seems in a very close orbit as we measured a projected separation of ∼2.5 mas (∼3.5 au), as for S Mus. We first estimated the 3 σ detection limit using all observables and removing the possible companion (because we have a detection), and second using only the CPs but without removing it (because there is no detection). We noticed that the closure phases alone provide a contrast limit lower than 4 mag (see Table 3 ), which explains the non-detection with only this observable. With all observables we add more constraints in this case, which provide contrast limit of 5 mag (5.1 mag within 25 mas). This enables us to exclude at 3 σ any companion with a spectral type earlier than A0V. YZ Car. The faint companion orbiting this Cepheid is not detected from our observations. Our 3 σ detection limits (see Table 3 ) show that any companion with a spectral type later than B3V would not have been detected with
Paper: 2018ApJ...852...63M (fulltext)
tailed and, more importantly, (b) that the chosen statistical sample represents the global statistics well, at least, in terms of statistical power. Therefore, if we randomly select a pixel inside our sampling area from one observation, the probability to observe a value above the background mean is or . 10 10 is the probability to observe values outside the limits. Half of this should account for the probability to observe values that exceed only the upper limit of . Therefore, a is adopted as a realistic occurrence probability of a stochastic observation in a single experiment. Considering also that the signal at the position of the A HST star is the only consistently present signal in all our NIR images (i.e., three independent experiments), then the probability that it results from random background noise fluctuations should be or . Since this is negligible, we can safely consider that the presence of an underlying point source, which systematically “drives” the flux within the green circle above the limit in repeated observations, is the origin of the observed NIR signal of Figure 5 . However, are the photometric properties of the NIR detections consistent with the visual photometric properties of the A HST star? 4.2. Photometric Calibration of the NIR Detections The V and I magnitudes of the A HST star are measured with aperture photometry on the and HST images (Section 2.2 ). They are and mag, respectively. On the contrary, due to the low S/N of the Subaru data, aperture photometry is not an option for estimating the NIR magnitudes of the detections. Alternatively, their , and K S magnitudes are estimated by taking the ratio of the sum of the peak fluxes of G.S.1 and G.S.2 to the peak fluxes of the NIR detections, 11 11 Point sources of different brightness observed simultaneously with the same telescope are convolved with the same PSF as long as their separation is smaller than ∼30 arcsec (i.e., isoplanatic angle). Consequently, the ratio of the fluxes at any point across their PSFs and the ratio of their integrated fluxes will (ideally) be approximately equal and, therefore, any of these measurements should be representative of their true brightness difference. usingwhere is the peak flux of each guide star, is the peak flux of each detection, and is the Subaru magnitude of the G.S. that we derived in Section 2.1.1 . This yields , and mag for the detections in each NIR band. The uncertainties are calculated according to the relationwhere we assume a 10% uncertainty in the measurement of and a 30% uncertainty in the measurement of the, more uncertain, . Having magnitude estimations, we can attempt to constrain the V − I , visual–NIR, 12 12 I.e., , and . and NIR 13 13 I.e., , and . colors of the NIR detections/A HST star and, therefore, its spectral type and luminosity class. 4.3. An Extragalactic Supergiant or a Galactic Dwarf? There are two possibilities for the nature of the A HST star. It can be either a distant supergiant
Paper: 2016MNRAS.457.3724B (fulltext)
the course of the observations (typically 33 d for Campaign 0). Eclipsing binaries and ellipsoidal variables of high amplitude are avoided. If we consider only stars where the harmonic is visible, then 86 out of 265 (32 per cent) 09–B9 stars are rotational variables. In addition, if we add stars with just a single isolated peak (no harmonic), then 142 out of 265 stars (54 per cent) are rotational variables. Balona et al. ( 2015 ) has already shown that rotational modulation is probably present in many late B stars in the Kepler field. This study shows that this effect is likely to be present in early B stars as well. Indeed, the large fraction of B-type rotational variables in the K2 data is probably an underestimate because these data are less precise and of much shorter duration than the Kepler data. The only test that is available for rotational modulation is to compare the rotational velocity obtained from the photometric period (using an appropriate stellar radius) with the rotational velocity derived from spectroscopic line broadening measurements. This could be done successfully for the A stars (Balona 2013 ) because of the large number (875) of stars with known periods and radii derived from multicolour CCD photometry. Unfortunately, there are too few B stars in our sample to use this statistical test. Moreover, stellar radii cannot be estimated with sufficient precision. There are only seven stars in Table 2 with known values of the projected rotational velocity. Rotational modulation implies some kind of surface brightness inhomogeneity. The nature of such a ‘spot’ cannot be determined at present. However, it is difficult to conceive of starspot formation without the presence of a magnetic field. We therefore suggest that magnetic fields are probably present in stars with radiative envelopes. According to Cantiello Braithwaite ( 2011 ), dynamo action in a subsurface convection zone in such stars might generate a magnetic field. In this case magnetic spots of size comparable to the local pressure scaleheight can manifest themselves as hot, bright spots. A theory of dynamo action in radiative envelopes which involves differential rotation has been developed by Spruit ( 1999 , 2002 ) and Maeder Meynet ( 2004 ). In this theory, a magnetic instability in the toroidal field wound up by differential rotation replaces the role of convection in closing the field amplification loop in conventional dynamo theory. Zahn, Brun Mathis ( 2007 ) has verified that an instability is generated, but concludes that a dynamo cannot be sustained by this effect. Arlt Rüdiger ( 2011 ) found that feedback by the Lorentz force diminishes differential rotation and brings field growth to a halt in a few thousand years for intermediate mass stars. It seems that we still do not have an acceptable explanation for the generation of a magnetic field in radiative envelopes. One way to circumvent the generation of magnetic fields in radiative envelopes is to suppose
Paper: 2017ApJ...850..195E (fulltext)
NW part of RCW 57A. This could be the plausible reason behind the low-density material observed in the NW region. The observed large-scale loops in the NE possibly formed due to the newly formed material as a result of the expanding I-fronts driven by the embedded massive protostars of RCW 57A. A high-velocity outflow signature is discernible close to eastward from the center, while the same feature is absent westward as shown in Figure 11 (b). This could be attributed to the confined ionized gas flow toward the west from the center, possibly due to the presence of a dense clump (thin black contours in Figure 7 ). The high-velocity component is only seen toward the east, which implies a dearth of dense material there, and hence, the ionized gas expanded with relatively more velocity. de Pree et al. ( 1999 ) and Purcell et al. ( 2009 ) have also shown that the H ii region expands more freely toward the east and is bounded on the west. 5.2. Column Density Profiles: Filament Width and <italic>B</italic>-field Support We fitted a Gaussian to the mean column density profiles spread over ∼5 pc radii, and the resultant FWHM, corresponding to the filament characteristic width, is found to be 1.55 ± 0.09 pc. However, a constant filament width of ∼0.1 pc was determined for the central dense sections of the filaments of nearby star-forming regions in the Herschel Gould Belt survey by fitting a Gaussian to the inner sections (radii up to 0.3–0.4 pc) of the filaments (Arzoumanian et al. 2011 ). Therefore, we fit a Gaussian to the inner 1 pc width of the clubbed mean column density profiles of the NW and SE regions, and the resultant σ and FWHM are found to be 0.21 ± 0.08 pc and 0.50 ± 0.18 pc, respectively. Within the error, the filament width (0.50 pc; green lines in Figures 12 (a) and (b)) of RCW 57A is higher than the constant filament width of ∼0.1 pc (Arzoumanian et al. 2011 ). Filament widths larger than 0.1 pc (spanning over 0.1 –1 pc with typical widths of ∼0.2–0.3 pc) have also been witnessed in a number of studies (e.g., Hennemann et al. 2012 ; Juvela et al. 2012 ). However, the filament width of RCW 57A has not been resolved because of the limited resolution (364 corresponds to 0.42 pc as shown by the cyan lines) of the column density map. The mean power-law index p = 2.02 ± 0.05, derived from the Plummer-like profile fitting for RCW 57A, is consistent with other studies (Arzoumanian et al. 2011 ; Juvela et al. 2012 ; Palmeirim et al. 2013 ; Hoq et al. 2017 ). A similar value of this index ( p = 2) is expected if the filament is supported by B -fields (Hennebelle 2003 ; Tilley Pudritz 2003 ), in accordance with our observational results supporting the active role of B -fields in the formation and evolution of filaments, as described in Section 5.3 below. 5.3. Evolutionary Scenario of the Filament and Bipolar Bubble Based on the morphological correlations among the (i) dense filamentary cloud structure seen in the submillimeter (in the 0.87 mm
Paper: 2021MNRAS.503.3596A (fulltext)
the matter power spectrum (left-hand panel) and reduced bispectrum (right-hand panel), defined as S ( k ) ≡ T ( k ) BCM / T ( k ) GrO for T ( k ) = { P ( k ), Q ( k )}, at z = 0. Solid, dashed, dash–dotted and dotted lines are computed with simulations of box side and 768 3 , 512 3 , 384 3 , and 256 3 particles, respectively. Colours are referred to different halo mass bins, expressed in decimal logarithm of , with which the baryon corrections have been computed, according to the legend. APPENDIX B: FOLDING OF THE PARTICLE DISTRIBUTION Measuring the three-point clustering with the classical Fourier estimators can be very expensive in terms of memory and CPU, especially when using covering larger dynamical ranges. In fact, it is easy to see that, being k Ny = π N g / L box the Nyquist frequency of the grid, increasingly large number of grid points N g are required to get a given accuracy at a fixed wavenumber, when using progressively larger simulation boxes L box . Additionally, when using “interlacing” to suppress aliasing, the number of grids used must be doubled (Sefusatti et al. 2016 ). However, since our measurements are limited by discreteness noise (and not cosmic variance), we can obtain accurate estimates of Fourier statistics on small scales by folding the density field (Jenkins et al. 1998 ; Colombi et al. 2009 ). The idea is to fold the particle distribution by re-applying the periodic boundary conditions assuming a new boxsize L ′ = L / f , where we call f the number of foldings. If L ′ is large enough to assure that the modes inside the new box are uncorrelated, we can measure in principle the clustering from a new effective fundamental wavenumber up to a new effective Nyquist frequency, given by . For instance, by folding the box four times, we will get to Nyquist frequency four times higher. In Fig. A3 , we apply this technique to our simulation, folding the particles up to six times, and reaching a with a 64 3 and a 128 3 grid for the power spectrum and the bispectrum, respectively. Even using a TSC scheme on interlaced grids, we note that it is safer to use the measurements up to in the bispectrum. Also, the measurement of the largest modes of the folded box are noisy because they are sparsely sampled; for this reason, it is convenient to discard these modes, taking for instance wavenumbers . Figure A3. Left-hand panels: Matter power spectrum measured in a 64 3 (interlaced) mesh, folding the box up to six times, following the technique explained in the text (coloured dots). For comparison, the matter power spectrum measured with a 696 3 mesh and not folding the box is plotted as a black solid lines. The equivalent Nyquist frequencies for each folded box is plotted as a solid line. In the bottom panel, we display the ratio between the power spectrum measure with a 64 3 and a 696 3 mesh. Right-hand panels: Similarly to the left-hand panels, we display the measured bispectra using a 128 3 mesh and the folding of the box, and
Paper: 2018ApJ...857...65L (fulltext)
governed by the equilibrium with SI clathrate hydrates of CO 2 (Levi et al. 2017 ). For convenience, we have cast into a cubic polynomial form the first and second dissociation constants of carbonic acid, as derived from the revised-HKF equation of state:where P is in GPa and K is in molal. For each isotherm, the polynomial is valid for the pressure range spanning from the surface of the ocean to the ocean floor. The coefficients for the polynomials are tabulated in Table 4 . Table 4 Polynomial Coefficients to Be Used with Equation ( 55 ) In Figure 14 , we give the speciation of the carbonate system as a function of depth, for the 280 K isotherm. We find that freely dissolved CO 2 is the most abundant form of DIC, in water-planet oceans, at any depth. This is opposite to the case of Earth, where freely dissolved CO 2 is the least abundant DIC component in the ocean (Zeebe Wolf-Gladrow 2001 ). Also, we find no substantial difference between the concentrations of hydrogen complexes, H + , and bicarbonate (both are represented by the red curve). Carbonate and hydroxide ions are found to exist in the ocean only in trace amounts. We note that the errors we have suggested above for the values of K 1 and K 2 do not have any considerable effect on our results. Figure 14. Speciation of the carbonate system as a function of depth in the ocean. Here we consider the ocean thermal profile to be an isotherm, set at 280 K. The black curve is the concentration of freely dissolved CO 2 . The red curve is the concentration of H + and . The green curve is the concentration of . The cyan curve is the concentration of OH − . The curves terminate at the pressure representing the bottom of the ocean, for the given isotherm. From Figure 14 we see that the different chemical species making up the carbonate system have a concentration gradient with depth. We argue that these concentration gradients with depth are stable against vertical mixing in an ocean world. First, we wish to point out that the concentration of freely dissolved CO 2 , the most abundant component of the DIC, is constant to a good approximation throughout the ocean. This is a consequence of the nature of solubility when in equilibrium with the clathrate phase, rather than due to efficient vertical mixing in the ocean (Levi et al. 2017 ). Thus, the concentration gradients with depth are most pronounced for the chemical species that are least abundant. If a parcel of ocean water migrates from the ocean floor upward, this parcel would be supersaturated with carbonate with respect to the shallower depth. The value for K 2 decreases with decreasing pressure. Consequently, carbonate would transform to bicarbonate, and then to freely dissolved CO 2 . However, this newly formed CO 2 is added to an ocean likely saturated with CO 2 , at equilibrium levels with respect to its clathrate hydrate phase (Levi et al. 2017 ). Therefore, the additional dissolved CO 2 forms grains of clathrate hydrates, which tend to
Paper: 2019ApJ...880..100L (fulltext)
the importance of the presented ion acceleration in these systems. In terms of the long-term evolution of the acceleration process, in the turbulent environment of accretion disks, we expect many successive δB / B ∼ 1 amplifications and decreases of the field (see also Verscharen et al. 2016 ). So a more realistic picture of this process should consider the acceleration presented in this work occurring many times as the plasma is gradually accreted. We will study these aspects of the acceleration process in future investigations. Appendix A 1D Shear Setup The goal of our 1D shear runs is to simulate a shearing plasma, assuming that its properties depend only on the direction parallel to the mean background magnetic field . This is equivalent to assuming that the wavevectors captured in the simulations satisfy . Since, in a shearing plasma, the direction of evolves with time, the orientation of the wavevectors that can be consistently resolved needs to evolve accordingly. Our 1D setup is built upon the “shearing coordinates” setup presented by Riquelme et al. ( 2012 ). This setup was designed so that the simulation domain follows the shearing flow of the plasma, which is given by the shear velocity (). Figures 1 (a) and (b) illustrate the way the shape of the domain evolves as seen by an inertial observer at t = 0 and t > 0, respectively. Formally, the shearing coordinates ( x ′, y ′, z ′, t ′) are defined in terms of the regular, inertial coordinates ( x , y , z , t ) aswhere , , 19 19 Notice that, in the limit , the expression is equivalent to v = − sx , as we assume in Figures 1 (a) and (b). Since ensures that , this expression was adopted in Riquelme et al. ( 2012 ) in order to deduce the equations that describe the dynamics of the plasma in the shearing coordinate system. However, both in Riquelme et al. ( 2012 ) and in this paper, we are interested in the plasma dynamics in the regime . c is the speed of light, and s is the shear rate of the plasma. Our 1D domain is defined by y ′ = 0, and it is shown by the blue lines in Figures 1 (a) and (b). These figures show how the length and orientation of the 1D domain change over time. This in turn changes the orientation of the wavevectors that can be captured within the domain. In addition, there is an initially homogeneous magnetic field, , pointing parallel to the 1D domain at t = 0. Magnetic flux conservation ensures that will always be parallel to the 1D domain at t > 0, as also depicted in Figures 1 (a) and (b). Therefore, if the problem of interest is dominated by waves that propagate parallel to , our 1D simulations will be able to capture the essence of the phenomenon. In order for our 1D simulations to consistently satisfy , we need to replace x ′ with a new coordinate, , which we define asIndeed, if we assume and sy ′ ≪ c , the partial derivatives with respect to the spatial coordinates in the inertial frame areThus, if we impose the 1D condition, namely, that the fields depend only