adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019ApJ...885L..13Z (fulltext)
(b) Group 1 (red line) and Group 2 (blue line) events; (c) Group 3 (red line) and Group 4 (blue line) events. To reveal the reason for this difference, the intermittent structures are grouped according to the intensity and direction changes of magnetic field. The intensity changes are determined by a normalized parameter , where and B 0 is the average of the magnetic field strength of the whole sheath. The directional changes are also divided into two groups: Δ θ > 45° and Δ θ ≤ 45°, as current sheets with Δ θ > 45° are categorized to strong currents in Borovsky Denton ( 2011 ). Thus, the intermittent structures are grouped into the following four categories: (1) and Δ θ > 45°; (2) and Δ θ ≤ 45°; (3) and Δ θ > 45°; (4) and Δ θ ≤ 45°. Figure 2 (b) shows the associated proton temperature of intermittent structures with significant magnetic field intensity changes (). A substantial enhancement (10%) of the normalized proton temperature can be found regardless of the value of Δ θ , revealing that local heating is prominent near these coherent structures. Near the intermittent structures without large changes in magnetic field magnitude (), local enhancement is still prominent (nearly 8%) near current sheets with large shear angles (Δ θ > 45°), but is not significant near current sheets with small shear angles (Δ θ ≤ 45°), as shown in Figure 2 (c). However, 10,831 of the total 12,426 intermittent structures identified fall into Group 4, so the average enhancement of normalized proton temperature is only 3%, as shown in Figure 2 (a). As shown in Figures 2 (b) and (c), the normalized proton temperature suffers a significant enhancement near current sheets with Δ θ > 45° (red lines). We then analyze the association between the temperature enhancement and the scale of such current sheets. The superposed-epoch analysis is performed for strong current sheets identified under four different time lags. Figure 3 shows that the temperature enhancement becomes more prominent as the spatial scale (≈ τ · V sh ) of the current sheets decreases. Meanwhile, the heating effect is more localized near thinner current sheets. When the time lag is 3 s, the normalized proton temperature has already dropped 7% at a distance of 10 5 km, but it almost remains the same at this distance when the time lag increases to 120 s. This result suggests that the intermittent heating mainly occurs at thin current sheets instead of large-scale ones. Figure 3. Averages of normalized proton temperature conditioned on the distance from strong current sheets (Δ θ > 45°) at different timescales. 4. Summary and Discussion A total of 12,426 PVI events have been identified in the sheaths of 71 MC events observed by the WIND spacecraft, with the criterion of PVI ≥ 3. Superposed-epoch analysis of normalized proton temperature associated with the PVI peaks identified shows that the averaged temperature enhancement is not prominent near the current sheets in the MC sheath, which is different
Paper: 2021A&A...649A.121B (fulltext)
et al. (2011) . These swirls are obviously caused by the turbulent plasma motions in the surface layers of the convection zone. However, the Hydro simulation shows a higher density of swirls than the MHD simulations. This is because the magnetic fields hamper the formation of vortices in the convective layers of the simulation. The photosphere of the MHD 200 G time instant harbors an almost homogeneous structure of vertically oriented swirls and only a few swirl arches are present near the τ 500 = 1 surface. In the MHD 50 G case, the vertical swirls are clustered in funnel-shaped structures and the arches are more prominent than in MHD 200 G. Different from these two cases, vertical swirls are essentially absent in the Hydro simulation, while swirl arches are omnipresent in the photosphere. Again, these results well agree with and confirm earlier results of Moll et al. (2011 , 2012) . A similar behavior is observed in the chromosphere: the stronger the initial magnetic field, the more abundant the vertical swirls are. However, we observe that the strongest swirls appear at the top of the MHD 50 G simulation. This suggests that too strong magnetic fields can possibly inhibit the formation of strong vertical vortices in the chromosphere. In the Hydro simulation, we see a quasi-isotropic distribution of swirls at chromospheric levels, which are most probably related to hydrodynamic shocks taking place in this region of the field-free solar atmosphere (see, e.g., Moll et al. 2012 ). Therefore, not all swirls found at chromospheric levels are necessarily related to the magnetic field, even though the magnetism clearly plays a cardinal role in the generation of vertical vortices. The above more qualitative assessment is confirmed by the quantitative analysis of Fig. A.2 , which shows for each simulation a bi-dimensional histogram of the inclination angle cos( θ ) of the swirling strength vector at each height z . The angle θ is defined through being the angle between the swirling strength vector λ and the unit vector in the vertical direction . Therefore, horizontally oriented swirls are binned close to cos( θ ) = 0, while vertically oriented swirls are close to cos( θ ) = 1. We choose to plot the histogram of cos( θ ) instead of θ in order to easily recognize isotropy. In this way, an isotropic angle distribution at a certain height z is characterized by a flat distribution in the histograms of Fig. A.2 . In the convection zone, that is, for z ≲ 0 km, all three simulations show a flat distribution, which confirms the nearly isotropic distribution of swirls in that region. Also, as estimated before, there are on average more swirls in the Hydro simulation (panel c) than in the MHD 50 G (panel b) and the MHD 200 G (panel a) simulations. Near the optical surface, that is, around z = 0 km, there is a sudden break of isotropy. Two families of vortices can be spotted. First, a cluster of horizontally oriented swirls (cos( θ )≈0), visible in all three
Paper: 2019MNRAS.490.2467T (fulltext)
to the total flux. Thus we believe the signal to belong to the visual companion star, which has a temperature of 5430 K. A rotation period of 4.6 d is fairly rapid for a star of T eff = 5430 K, which may imply a young age for the system, consistent with our bagemass analysis in Section 6 . 5 COMBINED MCMC ANALYSIS We use a Markov chain Monte Carlo (MCMC) approach to fit the combined photometric and radial velocity data, as well as investigate the RM effect. We follow methods very similar to Temple et al. ( 2018 , 2019 ), whereby we conduct both an RM analysis and a tomographic analysis and adopt the better constrained solution. The RM analysis involves detecting the line profile distortions as an apparent overall shift in radial velocity measurements (e.g. Triaud 2017 ), whereas the tomographic analysis requires one to directly map the motion of the distortion caused by the occulting body across the line profiles as a function of phase (e.g. Brown et al. 2017 ; Temple et al. 2017 ). The code we use is described by Collier Cameron et al. ( 2007 , 2010 ) and Pollacco et al. ( 2008 ). The combined photometric and RV fitting determines the orbital period P , the epoch of mid-transit T c , the planet-to-star area ratio ( R p / R ⋆ ) 2 , the transit duration T 14 , the impact parameter b , the stellar reflex velocity semi-amplitude K 1 , and the barycentric system velocity γ. We use the value of T eff obtained in the dilution correction as input, and interpolate four-parameter limb darkening coefficients from the Claret ( 2000 , 2004 ) tables in each step using the current value of T eff . We use the stellar radius obtained in Section 4.2 (1.17 ± 0.08 ) as a prior to constrain stellar parameters. In the fit we present, we have assumed that the orbit is circular, as one would expect a hot Jupiter to circularize on a time-scale shorter than its lifetime (Pont et al. 2011 ). However, a further fit was carried out to test this assumption, leading to an upper limit of e 0.27 (95 per cent confidence). We display the photometry and best-fitting transit model in Fig. 2 . Figure 2. The WASP discovery photometry (top) and follow-up transit light curves (bottom) with the best-fitting model shown in blue (see Section 5 ). The data for the three follow-up light curves, prior to the dilution correction, are available online as supporting material . The RM fit and Doppler tomography give values for v sin i ⋆ , λ, and the system γ-velocity. We use the calibrations of Hirano et al. ( 2011 ) to fit the RM effect. For Doppler tomography, we assume a Gaussian profile for the perturbation to the stellar-line profiles and fit the intrinsic full width at half maximum (FWHM) of the perturbation, v FWHM . Both methods also provide an additional constraint on the impact parameter b , although the tomographic method fits this quantity more directly. We estimate the start value for γ by fitting a Gaussian profile to the CCFs. We also apply the spectral v sin i ⋆ as a
Paper: 2021MNRAS.504.1183S (fulltext)
can differ from a standard average value, depending on stochastically sampled explosions that have enriched a given galaxy. In addition, inhomogeneous mixing of gas results in different element abundance ratios in the next generation of stars. The same is true for other element abundance ratios, such as [Mg/Ca] or [Na/Mg]. Since there are model predictions of explosions of Pop iii stars, producing low [Na/Mg], we suggest them to be responsible for the low [Na/Mg] detected in MP stars. We failed to fit abundance patterns of any of our sample stars with a single nucleosynthesis event, however, we suggest that a small number of SN ii explosions including those with low [Na/Mg] contributed to the abundances found in the investigated stars. A comparison of observed relative [El/Mg] abundance ratios with nucleosynthesis predictions should be performed with a caution. We note that our sample stars are too metal rich to be fitted with yields from only one explosion. For each selected model, its metallicity is about [Fe/H]≃ −3, which is lower compared to that of the sample stars (−2.5 [Fe/H] −2.1). Magg et al. ( 2020 ) estimated analytically a limit for the dilution of metals produced by a single SN and recommended to account for not only element-to-element abundance ratios, but also the absolute abundance values when fitting stellar abundance patterns with nucleosynthesis yields. In their calculations, the following assumptions were adopted: the SN being alone and isolated, the explosions being spherical and well-mixed, and the surrounding medium being homogeneous. The latter assumption is not always fulfilled (see for example Chiaki Tominaga 2020 ). We also note that to date there is no a metal-poor star, whose extensive abundance pattern is reasonably well fitted with the yields from a single nucleosynthesis episode, despite many attempts that were made (see for example, Tominaga et al. 2007 ; Placco et al. 2015 ; Hansen et al. 2020a ). 5.3 Neutron-capture elements in classical and ultrafaint dwarf galaxies In the majority of EMP stars, n-capture elements are represented by Sr and Ba. A number of the Galactic studies reported on a large scatter of [Sr/Ba], of about 2 dex, which decreases with increasing [Ba/H] (Honda et al. 2004 ; François et al. 2007 ; Andrievsky et al. 2011 ). In classical dSphs, abundances of Sr and Ba at the lowest metallicity are at or below the lowest values in the MW halo, however, the [Sr/Fe] and [Ba/Fe] ratios increase toward the solar ratio as [Fe/H] increases, similarly to that for the MW halo (Cohen Huang 2009 , 2010 ; Jablonka et al. 2015 ; Mashonkina et al. 2017b ). Very low abundances of Sr and Ba were reported for the majority of UFDs (Frebel et al. 2010 ; Simon et al. 2010 ; Gilmore et al. 2013 ; Koch et al. 2013 ; Koch Rich 2014 ; Ji et al. 2016c , a ; Nagasawa et al. 2018 ). In the UFDs, the [Sr/Fe] and [Ba/Fe] ratios do not rise with metallicity, they scatter around a mean floor level of −1 and −0.7,
Paper: 2019ApJ...873...65S (fulltext)
directions of Sgr A* were 0.056 and 0.060 mas yr −1 respectively. Figure 5. Comparison of the stability of the reference frame as measured by the average differences between the positions and velocities in the radio and IR. The agreements of the maser positions (left) and velocities (right) in the IR are compared to those in the radio. The ellipses represent 1 σ , 2 σ , and 3 σ uncertainties. For the Keck/NIRC2 data, the uncertainty in the distortion correction of 1 mas that dominates the IR error was subtracted so as to show the improvement in the methods used in this paper to construct the reference frame. Using the NACO imager on VLT, Plewa et al. ( 2015 ) reports a stability of ∼0.17 mas in position and ∼0.07 mas yr −1 in velocity. Their analysis used eight masers because their mosaicked field covered 42 arcsec × 42 arcsec. However, their maser sample did not include IRS 7. IRS 7 is a supergiant and its SiO maser features originate from a much larger maser emission region than the other masers used (Reid et al. 2003 ) by a factor of ∼2, which is reflected in larger uncertainties in the star’s radio positions and proper motions. To see how much effect this one star has on the IR reference frame, we construct the reference frame without IRS 7. Excluding IRS 7, we obtain combined positional and velocity uncertainties of 0.17 mas and 0.031 mas yr −1 respectively. The velocity stability of our sample of six masers, without IRS 7, is still a factor of 2 better than the one reported by Plewa et al. ( 2015 ). This is because IRS 7 has less influence on the overall reference frame due to the very large errors assigned to its radio velocities. The maser sample used by Plewa et al. ( 2015 ) also had one less epoch of the radio observations. Furthermore, their IR observations included data through 2013, while our IR data extend four additional years. As explained in Section 5 , the IR data and the method used to create the reference frame presented in this paper were modified significantly compared to those used in Yelda et al. ( 2010 ). The comparison of the reference frame of Boehle et al. ( 2016 ) with that of Plewa et al. ( 2015 ) is likely more appropriate, because both use the maser data through 2013. 5. Discussion We have presented an improved astrometric reference frame that utilizes 13 years of IR observations of radio-emitting SiO masers in the vicinity of the Galactic Center. Since our previous work in Yelda et al. ( 2010 ), several modifications have been made in the construction of the reference frame including: (i) using 13 epochs of maser data (2005–2017) instead of six epochs (2005–2010) used by Yelda et al. ( 2010 ); (ii) using an improved PSF-fitting package, AIROPA, instead of StarFinder v1.6 (Diolaiti et al. 2000 ) used in Yelda et al. ( 2010 ); (iii) using a new method to mosaic 9 or 13 dither positions for each epoch of observations. Yelda et al. ( 2010 ) stitched together one field at a time, building up the starlist by one
Paper: 2016A&A...590A..23K (fulltext)
bias evolution scheme of Basilakos Plionis (2001 , 2003) , which is based on linear perturbation theory and given by ( Basilakos et al. 2011 ) with The constants (the present day bias factor) and depend on the host dark matter halo mass and for the CDM model are given by where ( Basilakos et al. 2012 ). This bias evolution model has been thoroughly tested and found to rate very well in reproducing -body simulation data ( Basilakos et al. 2008 ) as well as fitting observational data ( Papageorgiou et al. 2012 ). Fig. 1 Redshift evolution of the comoving clustering length for the two different parametrizations of clustering evolution. For the case of our model (Eq. ( 13 )), shown as black and blue curves, the different evolution behaviours shown correspond to the indicated parameters ). The specific values of these parameters have been chosen such as to resemble the and models of the traditional parametrization of Eq. ( 7 ), shown in red, which correspond to the constant in comoving coordinates clustering (for ) and to the stable clustering models (for details see the text). We therefore see that through the dependence of the bias factor on the dark matter halo mass, one expects a different clustering evolution for dark matter halos of different mass for the same values of and . In order to appreciate our parametrization of the clustering evolution we present in Fig. 1 the evolution of the comoving clustering length for models that have been purposely selected to resemble some based models. As can be seen the model with is equivalent with the constant in comoving coordinates clustering model () up to very large redshifts (). No other ( combination can provide such an equivalence up to such high redshifts. However, as we can again see in Fig. 1 , one can find () combinations that correspond to the comoving clustering model, but up to , as the example shown with . In fact one can derive the degeneracy between the and parameters such that the clustering evolution is constant in comoving coordinates (), but up to , since beyond this redshift the equivalence is unattainable. In Fig. 2 we present the corresponding 1, 2, and 3 contours in the ) parameter space of the solution that provides the above equivalence with the model. The range is dictated by the uncertainty that we have imposed on , which is taken to be of , typical of current accurate measurements. The degeneracy is clear and it can only be broken if we either impose a value of , for example from the expected slope of the power spectrum on these scales, or from an independent estimate of the mass of the dark matter halos in which the tracers (in our case X-ray selected AGN) reside. Fig. 2 Contour ranges of 1, 2 and 3 in the parameter plane for the case where our clustering evolution scheme corresponds to the constant in comoving coordinates model (imposed up to in this example). The strong degeneracy of the parameters is evident. Similarly, larger values of provide clustering evolution
Paper: 2017ApJ...839...57S (fulltext)
2.1. Observations 2.1.1. Spectroscopic Data This study makes use of data obtained with the Multi-Object Spectrometer for InfraRed Exploration (MOSFIRE; McLean et al. 2010 ) on Keck I on Maunakea in Hawaii. The observations over COSMOS were carried out in six pointings with a field of view. The observations were conducted on 2013 December 24–25 and 2014 February 10–13. Galaxies were observed in eight masks in the K band, which covers , and can be used to measure H α and [N ii ] emission lines for galaxies at . Two H -band masks were also included in the observations. The H -band coverage is , covering and [O iii ]. For this work, we limit the analysis to the H α emission line data in the K band. Further details on the H -band masks can be found in Nanayakkara et al. ( 2016 ). The total exposure time was 2 hr for each K -band mask. A slit width was used, yielding a spectral resolution of R = 3610. At z = 2.2, the median redshift of the sample of galaxies in this study, this corresponds to ∼26 km s −1 per pixel. The seeing conditions were , with a median of . We used a standard two-position dither pattern (ABBA). Before and after science target exposures, we measured the spectrum of an A0V-type standard star in 07 slits to be used for telluric corrections and standard stars to be used for flux calibration in a slit of width 3′′ to minimize slit loss. Each individual mask also contained a star for monitoring purposes, such as measuring the seeing conditions. The raw data were reduced using a slightly modified version of the publicly available 2015A data reduction pipeline 8 8 The modified version is available at https://github.com/themiyan/MosfireDRP_Themiyan . developed by the MOSFIRE instrument team, resulting in 2D spectra that were background subtracted, rectified, and wavelength-calibrated to vacuum wavelengths, with a typical residual of (Nanayakkara et al. 2016 ). To make up for the lack of sky lines at the red end of the K band, we used both night-sky lines and a neon arc lamp for wavelength calibration. Based on the standard star, we applied a telluric correction and flux calibration to the 2D spectra, similar to the procedure used by Steidel et al. ( 2014 ), and using our own custom IDL routines. We subsequently scaled the flux values to agree with the photometric K s -band magnitudes from the FourStar (Persson et al. 2013 ) Galaxy Evolution Survey (ZFOURGE; Straatman et al. 2016 ), resulting in a final median uncertainty of 0.08 mag (see also Nanayakkara et al. 2016 ). In Figure 1 we show two example spectra at z = 2.175 and z = 2.063, with strong H α emission at observed frame and , respectively. Other lines are visible in the spectrum as well, most notably [N ii ] and [S ii ] . Figure 1. Two example Keck MOSFIRE spectra (inverted gray scale) at z = 2.175 and z = 2.063, with H α clearly visible at (top) and (bottom). Other lines are visible as well, most notably [N ii ] and [S ii ] . 2.1.2. Continuum Subtraction From each 2D
Paper: 2019A&A...632A..98C (fulltext)
in the whole COSMOS field) to a control sample of 109 normal star-forming main sequence galaxies. We have performed an additional visual merger identification among the main sequence population and applied the classical Gini- M 20 classification to select galaxies with merger-like shapes. Our main results are summarized as follows: First, starburst and main sequence galaxies have different clumpiness histogram distributions: the former dominate in the high clumpiness regime, while the latter are mostly found at lower clumpiness. Given the merger nature of intermediate- z starbursts, this suggests that mergers are likely responsible for the formation of bright clumps and for the increase of the clumpiness with respect to normal star-forming isolated disks. Secondly, the majority (76%) of morphological mergers (according to Gini- M 20 based criterion by Lotz et al. 2008 ) are starburst galaxies. However, 57% of starbursts are not classified as mergers from their i -band morphology. This suggests that the Gini- M 20 merger selection is highly incomplete, likely due to multiple effects, including the galaxy inclination, the impact parameter, the dust attenuation, and the rapid fading of merger signatures after the coalescence. Furthermore, a larger difference among the clumpiness distributions is obtained both when comparing morphological mergers to not merger systems. In particular, Gini- M 20 mergers, regardless of their level of SFR, have a median clumpiness a factor of three higher compared to the rest of the population, and are almost entirely responsible for the high clumpiness tail observed among our sample. We also find a positive correlation between the clumpiness and the distance from the main sequence, for morphologically selected mergers. From hydrodynamical simulations of merger galaxies with initial conditions typical for our redshift range, we also confirm that mergers can significantly enhance the clumpiness compared to isolated main sequence systems, by a similar amount to that observed in real images. Different spin-orbit couplings of colliding galaxies can fully explain the scatter of the clumpiness observed in our sample (from 0 to 20%). Afterward, for a sample of 21 SBs with previous Magellan-FIRE spectra, there is a mild correlation between the clumpiness and the equivalent width of Balmer and Paschen lines, suggesting a possible clumpiness evolution during the merger, decreasing from early-intermediate to later, post-coalescence stages. X-ray detected AGNs are preferentially found in low-clumpiness systems, suggesting a possible evaporation or destruction of clumps induced by AGN feedback. However, other effects (including galaxy inclination, rotation, attenuation, and impact parameter) are likely responsible for the low correlation strength (≃2 σ ). Finally, using four high-resolution images in different optical or near-IR bands, available for three clumpy starbursts identified in the COSMOS-CANDELS field, we suggest that