adsabs/WIESP2022-NER huggingface dataset:
Paper: 2016MNRAS.463.4210N (fulltext)
their faintness ( V > 13.5, J > 11.5). This is somewhat expected since the two catalogues are based on different methodologies and input catalogues reflecting different scientific priorities. While K2-TESS is more focused on cool SpTs (late K and M dwarfs) and mainly based on the 2MASS infrared photometry, UCAC4-RPM is focused on solar-type FGK targets, implying different selection algorithms and thresholds. In other words, the two approaches will be highly synergic in selecting the optimal targets for TESS (Stassun et al. 2014 ). 5.2 PLATO The unique optical design of PLATO (Rauer et al. 2014 ) is driven by the requirement to cover a significant fraction of the sky with a good image quality in order to achieve an unprecedented photometric precision: 34 ppm h −1 (parts per million measured over a 1 h time-scale) for the main scientific sample limited at V 11, and even better for the brightest stars of the sample at V ≃ 6 (dominated by systematic errors). The present design of PLATO implies that the array of 12 cm telescopes is subdivided into four groups, each group pointing towards a direction which is displaced by an angle θ = 9.2 deg from the centre of the overall FOV. Such strategy provides a non-uniform instantaneous coverage of a 2124 deg 2 field. The observing strategy of PLATO consists of a two-step approach in order to maximize the scientific return of the mission. PLATO will image two ‘long-duration’ (LD) fields for more than two years each, and some supplementary fields for a few months each, during the ‘step and stare’ (SS) phase. The LD phase will fulfil the main scientific objective of the mission, being sensitive to planets down to the Earth's size orbiting within the habitable zone of solar-type (FGK) stars. The SS fields will expand the accessible sky area by about one order of magnitude, giving us access to a larger number of bright nearby stars at the expense of probing a smaller range of orbital periods. A simple calculation shows that, at the end of a nominal 6.5 year mission with 2 LD and 10 SS fields, PLATO will have surveyed about two thirds of the whole sky. The PLATO Science Requirements Document 10 defines five complementary stellar samples to be surveyed, listed in decreasing priority and summarized below. P1: DSLF5 stars brighter than V = 11, monitored with a photometric noise level σ ≤ 34 ppm h −1 , to be observed during the LD phase. P2, P3: DSLF5 stars brighter than V = 8, monitored at σ ≤ 34 ppm h −1 , to be observed during the LD and SS phase, respectively. P4: cool M dwarfs (M0 or later), to be monitored during the LD phase of the mission (at V 16), or during the SS phase (at V 15). The photometric noise level for both subsamples must be below 800 ppm h −1 . P5: DSLF5 stars brighter than V = 13, to be observed during the LD phase. Samples P1, P2, P3, P5 are therefore all made of ‘solar-like’ stars brighter than V = 13. Due to telemetry limitations, PLATO will not be able to download the full images, collected
Paper: 2019ApJ...872L..24K (fulltext)
would be one of the possible reasons for the difference between 2014 and 2016. Because the energetic electrons drift slower than the corotation and they spent more time in the inflow/outflow sectors, the drift orbit of higher-energy electron displaces more than that of the lower energy electron. However, statistical analysis indicated that the IPT and JSR did not have a strong positive correlation as expected from the hypothesis. Figure 3 indicates the scatter plot of the dusk-to-dawn ratio of JSR and IPT. Spearman’s rank correlation coefficient is −0.22 for 2014 and 0.048 for 2016. A correlation t-test at 10% significance level shows that JSR and IPT are regarded as having no correlation. The correlation between JSR and the solar wind dynamic pressure is not significant either. The energetic electrons drift slower than the corotation, which means that they may take longer to organize over all longitudes when a change in the dawn-dusk electric field occurs. The rotation period (corotation + magnetic gradient drift) of 10 MeV and 16 MeV electron are ∼10.5 hr and ∼11.0 hr at 1.5 Rj , respectively. In this study, response delays can be negligible comparing to the observation cadence. We do not consider any time lag between IPT and JSR, because the dawn-dusk electric field affects JSR and IPT at the same time. Hence, the short-term variation in the dawn-dusk asymmetry of JSR cannot be explained only by the dawn-dusk electric field. Figure 3. Scatter plot of the dusk-dawn ratio of IPT and JSR for 2014 and 2016. indicates Spearman’s correlation coefficient. Although these results did not support our hypothesis, we cannot rule out the possibility that the dawn-dusk electric field was masked by some other processes that could control the short-term variations in the dawn-to-dusk ratio of JSR. Here, we discuss the possible causalities of why the correlation is weak. Because the ionospheric potential difference induced by the field-aligned current depends on the ionospheric conductivities, the solar UV/EUV effect might appear in the dawn–dusk asymmetries of JSR. The stronger the solar UV/EUV, the weaker the dawn-dusk electric field because of higher conductivity. The third panel of Figure 2 indicates the solar UV/EUV. We cannot find a strong correlation between both dusk-to-dawn ratios and the solar UV/EUV; however, the conductivity variation should be taken into account. Because the ionospheric potential difference is the source of the electric field, the ionospheric potential map should also be taken into consideration. Dawn-dusk electric field is mapped from the ionospheric potential difference induced by the field-aligned current. As the potential difference becomes smaller at the lower latitude, the effect of the dawn-dusk electric field would be smaller in the innermost magnetosphere. The study with an ionospheric potential solver (e.g., Nakamizo et al. 2012 ) for Jovian ionosphere would give us a clue to estimate these effects quantitatively.
Paper: 2021A&A...648A..99T (fulltext)
in the strength of the BLR–H β component, in addition to that observed in the Fe II strength. However, a remarkable simultaneous variation in both H β and Fe II strength is what we would expect in the case when the two emissions are strictly co-spatial, whereas we know that the BLR is stratified and that microlensing magnifies the emission from the most compact regions more strongly. Therefore, we do not exclude the microlensing hypothesis. A detailed analysis of the different BLR spectra from the two images is beyond the scope of this work and will be presented in a forthcoming paper. Therefore, in this work, we focus on how we accounted for this effect during the spectral analysis. Consequently, in SDSS J1353+1138, we extracted two distinct nuclear spectra, namely, spectrum A and B that are shown in Fig. 3 , using a 03 radius aperture centred on the emission peak of each lensed image. We proceeded to fit them separately. In both spectra we used a first-degree polynomial to model the AGN continuum that is still dominant over the stellar continuum. Unlike the BLR modelling of HS 0810+2554, the multiple Gaussian fit was not sufficient to reproduce the broader and complex profile of the broad Balmer emission lines, especially the H β line in spectrum B. In fact, even though both H β prominent wings are likely due to the Fe II emission, as discussed above, the Fe II templates employed by the code were not able to reproduce such observed emission. Therefore, we modelled both wings as part of the broad H β line profile without any focus on their physical interpretation, as we were simply interested in identifying the overall BLR spectrum in order to remove it. For the modelling of the broad Balmer emission lines observed in SDSS J1353+1138 (i.e., H β and H γ ), we used a broken power law distribution convolved with a Gaussian profile ( Nagao et al. 2006 ): where the free parameters of the fit, for each line, are the central wavelength λ 0 , the two power-law indices α and β , the normalisation F 0 , and the width σ of the Gaussian kernel. In spectrum A (upper panel), the H β and H γ were modelled separately through the line profile described in Eq. ( 1 ). Spectrum B (lower panel) required instead an additional broad Gaussian component to suitably reproduce both the more extended red wing and the broad peak ( σ ∼ 800 km s −1 ) in the H β profile. Moreover, we had to constrain the H γ line profile through that of H β . Different Fe II templates have been selected in the BLR best-models for the two nuclear spectra. To model the narrow emission line profiles, we used three Gaussian components in spectrum A, while a single Gaussian component in spectrum B, since we did not actually observe any narrow component. Then we proceeded to fit the whole data cube following the procedure described in Sect. 3.1.2 , with the main difference that in each spatial pixel, pPXF considered a linear combination of the two BLR models, weighing their relative contribution
Paper: 2016MNRAS.458..306H (fulltext)
of a given amount of external irradiation. Fig. 4 illustrates this. It is a disc with an accretion rate of M ⊙ yr −1 , approximately a third lower than in Fig. 3 . In this case (Fig. 4 ), external irradiation has added additional mass to the outer parts of the disc, as the equilibrium disc structure is now more massive for a given accretion rate and radius, although in this case it does not meet the criteria required for it to fragment (which requires that the local α ≳ 0.1). In this disc, the external irradiation is maintaining Q ∼ 2 with little dissipation of the GI. In reality, we should expect Q to increase beyond the marginal limit of self-gravity in the outer parts of such discs. Figure 4. Disc with M ⊙ yr −1 , 33 per cent lower than the maximum it can sustain without truncating. It is irradiated at 10 K, and this small amount of external irradiation has altered the surface density structure of the outer parts of the disc, as the equilibrium disc structure is now more massive for a given accretion rate and radius. The ‘spiky’ features on the boundary between the outer and inner parts of the disc are a numerical artefact due to a small change in surface density from the resolution limit of the grid, and are more pronounced in log space. We should bear in mind that this is a simple model in which no infall is considered. Since, in general, adding mass to the outer regions of a self-gravitating disc encourages fragmentation (Kratter et al. 2010 ; Vorobyov Basu 2010 ; Kratter Murray-Clay 2011 ; Forgan Rice 2012 ), it seems that the Jeans criterion for fragmentation may be satisfied at relatively high accretion rates (of the order of ∼10 −7 M ⊙ yr −1 ) in the presence of sufficiently small (∼10 K) irradiation. Fig. 5 shows a contour plot of the amount of external irradiation required to prevent fragmentation of the disc as a function of accretion rate and radius. Below ∼3.2 × 10 −7 M ⊙ yr −1 , in our local viscous approximation, any disc will be able to regulate itself against collapse. Above this, differing amounts of irradiation can either prevent fragmentation and totally remove spiral features from the disc, or prevent fragmentation whilst allowing spiral features to exist. As accretion rate increases, higher temperatures are required to prevent the disc from fragmenting; additionally, beyond 60 au the determining factor in whether spirals are present or not is temperature, rather than a combination of temperature and accretion rate. Figure 5. Contour plot showing the minimum temperature in (K) of external irradiation required to halt fragmentation as a function of accretion rate and radius. The shaded region with dashed contours shows where both fragmentation is suppressed and spiral features are erased. The unshaded region with dotted contours shows where added irradiation prevents fragmentation but preserves spiral structure. 3.2 Comparison with imposed constant β In our model, unlike in SPH simulations by Dipierro et al. ( 2014 )
Paper: 2020ApJ...897...58T (fulltext)
∼10% at z = 18 to 23% at z = 12. While this is not an observable, it is an additional independent cross-check for our approach. 4.3. When Is <italic>dZ</italic> Important? In Figure 10 , we show the distribution of dZ as a function of physical parameters. Since dZ is most important in external enrichment, these panels indicate under what conditions external enrichment becomes important. For some stars with low [Fe/H] (≲−4), dZ is largely negative. This means that the metallicities of these stars would have been much higher if we assumed homogeneity. The clear cut at [Fe/H] ∼ −5.1 comes from our Population II star formation criterion. For stars with [Fe/H] > −5.1, we do not require carbon enhancement to form stars, whereas if [Fe/H] −5.1, we do require carbon enhancement (see Equation ( 2 )). Highly negative dZ means high (naively calculated) [Fe/H], which is therefore less likely to be carbon-enhanced and allowed to form stars. Figure 10. Distribution of dZ as a function of various physical properties of stars. (Top) For stars with [Fe/H] −4.5, external enrichment is the dominant channel. Therefore, inhomogeneity is important for such ultra-metal-poor stars. (Middle) For large, matured halos (), star formation has already begun; therefore, they are internally enriched. External enrichment is important when we consider star formation in small galaxies (). (Bottom) For star formation events at a very high redshift, external enrichment plays a role. The middle panel shows that in small halos, dZ can be largely negative. In massive galaxies (), star formation has already begun; therefore, they are internally enriched and well mixed. Combining with our former analysis of the dZ distribution, we find that the metallicity inhomogeneity is less significant in massive galaxies. The bottom panel shows that dZ is only important at high (although not the highest) redshifts. Low-redshift galaxies are homogeneous for the same reason as the massive galaxies. The highest-redshift galaxies () are homogeneous because they do not have enough time after the first star formation events to experience external enrichment. In summary, inhomogeneous metal mixing is important in low-mass halos at high redshift that are about to form EMP stars. 5. Discussion We have performed the first cosmologically representative analysis of metal mixing in high-redshift galaxies. We derived a physically motivated estimate of dZ , the metallicity difference between star-forming and all gas, and find that the distribution of dZ is very different between halos with stars and halos without stars. This bimodality can be understood by assuming that two processes are at work: internal enrichment and external enrichment. Halos without stars have not experienced any star formation events, so they can be identified as externally enriched halos. In external enrichment, the momentum of metal-rich wind is not strong. If a galaxy already has a dense gas cloud when the external enrichment takes
Paper: 2019MNRAS.483..223T (fulltext)
the halo contribution We estimate the average contribution from single LRG haloes as follows. Since we have selected central LRGs for our pair catalogue, we assume that the average single-halo contribution is circularly symmetric about each LRG in a pair. To determine the radial profile of each single halo, we analytially solve for the map (indexed by pixel p ) to a model of the form where y L,i is the single-halo signal in the ith radial bin centred on the ‘left’ LRG at (−1, 0), y R,j is the single-halo signal in the jth radial bin centred on the ‘right’ LRG at (+1, 0), and p = p ( X, Y ) is a pixel on the map as shown in Fig. 3 . When solving it, we choose radial bins of size Δ r = 0.02, and we weight the map pixels uniformly. To avoid biasing the profiles with non-circular filament signal, we exclude the central region −1 X +1 and −2 Y +2. Fig. 4 shows the resulting profiles for the LRG haloes. Figure 3. Illustration to estimate the radial profile of each single halo. The y value at a pixel p, y h ( p ), is the sum of ith radial bin centred on the left LRG at (−1, 0), y L, i ( p ), and jth radial bin centred on the right LRG, y R, j ( p ) at (+1, 0). To avoid biasing the profiles with non-circular filament signal, the central region −1 X +1 and −2 Y +2 is excluded. Figure 4. Top : The circular halo profiles estimated to the map in Fig. 2 . Bottom : The radial profile of the left and right haloes shown above. Fig. 5 shows the residual y map after subtracting the circular halo profiles shown in Fig. 4 (note the change in colour scale from Figs 4 and 5 ). The bright halo signals appear to be cleanly subtracted, while a residual signal between the LRGs is clearly visible. The lower panels of Fig. 5 show the residual signal in horizontal ( Y = 0) and vertical ( X = 0) slices through the map. The shape of the signal is consistent with an elongated filamentary structure connecting average pairs of central LRGs. The mean residual signal in the central region, −0.8 X +0.8 and −0.2 Y +0.2, is found to be Δ y = 1.31 × 10 −8 . Figure 5. Top : The residual y -map after the radial halo signals are subtracted. Bottom : The residual tSZ signal along the x- and y -axes. 3.3 Null tests and error estimates To assess the reality of the residual signal and estimate its uncertainty, we perform two types of Monte Carlo-based null tests. In the first test, we rotate the centre of each LRG pair by a random angle in galactic longitude, for example, the centre of one LRG pair is rotated from [ l, b ] = [10°, 60°] to [ l, b ] = [150°, 60°]. (Keeping the latitude fixed keeps the galactic foreground level in the y map approximately fixed.) We then stack the y map against the set of rotated LRG pairs, and we repeat this stacking of the full catalogue 1000 times to determine the rms fluctuations in the background (and foreground) sky. Fig. 6 shows one of the 1000 rotated, stacked y maps: the map has no discernible structure. We can use this ensemble of maps to
Paper: 2020A&A...635A.112L (fulltext)
two or more consecutive CMEs originated in the same source region. It is worth to note that the maximum fraction of free energy exhausted by one CME may be 25% ( Gopalswamy et al. 2005 ; Emslie et al. 2012 ; Feng et al. 2013 ). Finally, the speed distributions of all leading/trailing CMEs show a small but systematic tendency to have higher mean and median velocities for shorter WTs. The mean and median speed of the trailing CME are a few km s −1 higher than that of the leading CMEs, for WTs shorter than 7 h, after a WT of 10 h the differences remain constant and negligible. Nevertheless, these differences are higher (tens of km s −1 ) when we only take into account spatially-related CMEs (see Fig. 8 ). As the mean is not the best location indicator for highly skewed distributions, we also analyzed the quartiles (which are better suited to characterizing these distributions), the results are plotted in Fig. 9 . To assess the accuracy of the analysis, we applied the sign test to show that the differences between the medians of trailing and leading CMEs are statistically significant. The phases of the solar cycle where the sign-test probability (with the null hypothesis H 0 : ) is less than 0.1 are shown in Figs. 10 and 11 . These observations may be explained by the drag force models. It is clear that the magnetic field provides the necessary energy to launch and first acceleration of CMEs. Although this energy does not play an important role in the CME dynamics after few solar radii, at least for fast CMEs (such as those that form the tail of the distributions). Recently, Sachdeva et al. (2017) investigated CME dynamics using coronographic observations, and they concluded that for fast CMEs, the Lorentz force has its maximum effect at heights lower than 2.5 R ⊙ , and then, at heights of 3.5–4 R ⊙ becomes negligible compared with the drag force. In the case of slow CMEs, the Lorentz force may be significant at larger distances (up to 50 R ⊙ , but slow CMEs do not contribute to the tail of the distributions). The drag force is proportional to the drag coefficient ( C D ), the transverse area of the CME ( A ), the speed difference between the CME ( V CME ) and the solar wind ( V SW ), and the ambient density ( ρ ) ( Cargill 2004 ; Borgazzi et al. 2009 ; Vršnak et al. 2010 ). In a statistical analysis of a large number of events, the main values of A and V SW are similar for both leading and trailing CMEs. On the other hand, the leading CME may sweep the ambient medium, and the trailing CME encounters a less dense medium and therefore the drag force decreases, resulting in a faster CME. In this case, the shorter the WT, the higher the CME speed. This effect is clearly seen in Figs. 10 and 11 . 10. Conclusions We performed a statistical study of 27 761 CMEs observed by SOHO/LASCO from the ascending phase of cycle 23 to the declining phase of cycle 24, looking for long-range or memory signatures in the genesis and the first stages of evolution of
Paper: 2017ApJ...841....9R (fulltext)
therefore becomes is the change of phase angle. In other words, the wave propagates forradians before producing a shock, where the bracket means a time average along the wave front. The total propagation time is proportional to , so stronger and higher-frequency waves shock earlier. 3.3. Maturation of the Shockwave To estimate the point of intersection between the wave peak and shock front, we derive their respective trajectories. First, suppose a monochromatic wave with luminosity and frequency ω is led by a compressive edge (i.e., ). The wave peak initially lags behind the wave front by a distance . The peak propagates with a speed , where is the compressed local sound speed. Assuming properties of the gas do not vary significantly under compression (i.e., constant and ), we can write the compressed sound speed in terms of the peak pressure p w . The thermodynamic expression of the mean wave energy density allows to be expressed in terms of conserved wave properties, With the kinematic expression ( 3 ), the speed of the wave peak becomes Before shock formation (), the wave front simply travels at the local sound speed c s0 . For , the exact shock velocity requires numerical calculations to describe the arrival of the remaining wave pulse. We circumvent this calculation by approximating the shock front strength with the wave peak properties (). With the following shock jump condition,we approximate the shock Mach number to be Thus, the wave peak and shock front converge at the same location , onceis satisfied. 4. Hydrodynamic Simulations To test our analytical predictions of shock formation and provide examples of shock evolution and strengthening, we turn to numerical simulations. We construct 1D planar and spherical simulations in FLASH (Fryxell et al. 2000 ), a hydrodynamic adaptive mesh refinement (AMR) code. All quiescent structures begin in hydrostatic equilibrium with a gravitational acceleration g ( r ) that is independent of fluid perturbations (Cowling’s approximation). All simulations have inner reflecting and outer diode (outflow) boundary conditions. Two structures are considered: a planar isothermal atmosphere and a n = 3 stellar polytrope. The adiabatic index is held fixed in all of our simulations. 4.1. Planar Earth Atmosphere For planar shock formation, we consider a vertically stratified, initially isothermal atmosphere of gas (FLASH’s default Earth atmosphere) with in constant gravity. Lengths in this section should be compared to the 8.8 km scale height of the model, although the results can be scaled to any similar atmosphere. Upward-travelling waves are initialized by setting the isothermal atmosphere out of equilibrium; an example of the initial waveform and its evolution is shown in Figure 1 . The initially sinusoidal wave steepens (Phase 1) and forms a shock at the wave node, after which the wave peak approaches (Phase 2) and merges with the shock front. Figure 1. Deformation of a single sinusoidal impulse in a planar,