adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019MNRAS.490.3112J (fulltext)
for a cold flow such that (equation B1 in Appendix B , with ω = 1). This expression provides the two solid blue curves shown in Fig. 4 , computed in two extreme cases for the location of the Alfvén surface: near the SM surface with x SM = 2, namely (lower curve) and much further out with for the upper one (as in typical near-equipartition cold flows). For a given magnetic lever arm λ (mostly determined by ξ), κ must be large enough. This rules out all super-SM solutions located at the left-hand side of these curves. It can be seen right away that the parameter space of super-A solutions for near-equipartition fields ( n = 0) will be very small, with mass loads κ smaller than 0.1, disc ejection efficiencies ξ smaller than 0.08, and magnetic lever arms larger than ∼7, in agreement with Ferreira ( 1997 ). On the other hand, MRI-like driven flows at small μ seem to allow mass loads larger than unity with small λ. These isothermal flows could be of great interest for reproducing dense outflows (ξ > 0.1) with low asymptotic speeds ( u p ∞ ∼ Ω K o r o ). In other words, a weakly magnetized disc is better suited for a massive ejection than a near-equipartition disc, not only by providing the necessary bending at the disc surface through the MRI-like mechanism, but also by bringing the Alfvén surface closer to the disc (see the discussion on the role of Ψ A in Appendix B ). Nevertheless, not all super-SM solutions located at the right-hand side of the solid curves in Fig. 4 can become super-A. For a given ξ, λ is roughly determined and so is the cylindrical radius r A = r o (λ/ω) 1/2 of the Alfvén point. But equation ( 21 ) shows that κ also depends on p and μ. For a given toroidal current density p , the SM constraint provides μ so that κ is fixed. On the other hand, p also determines the radial magnetic field component at the disc surface and thereby the initial jet bending (see Fig. B3 ). As a consequence, playing with p not only affects the disc vertical equilibrium (SM point) but also this initial jet angle. Not all couples (κ, λ) fulfil the Grad–Shafranov equation ( 30 ). If a solution is not possible, this means that there is no altitude A of the Alfvén point that can be found starting from the conditions provided at the base of the jet (SM point). Changing the value of p leads to a slight modification of κ, λ as well as the jet angle at the SM point, allowing thereby to (possibly) meet the A condition. This translates into an adaptation of the altitude A of the Alfvén point (thus the angle Ψ A ) according to the disc conditions. The necessary condition highlights this aspect. When λ is large, r A / r o is large and gravity plays a negligible role so that is satisfied whenever λ > 3 (dashed line in Fig. 4 ). But gravity cannot be neglected anymore at lower values of λ, as can be seen in equation ( 31 ). In that case, providing requires to increase sin Ψ A , namely to bring the Alfvén surface closer to the disc surface. The closer it is to the
Paper: 2018ApJS..238...21F (fulltext)
to be aligned with its projected morphological major axis. The coincidence in PAs from the kinematics and morphology is thus generally a criterion in determining the nature of a galaxy. In practice, complications arise for a variety of physical reasons (e.g., presence of nonaxisymmetric structures, nearby companions with overlapping outer isophotes, spatially nonuniform extinction), compounded by surface brightness sensitivity and spatial resolution limitations for high-redshift galaxies. We examined the agreement between the derived kinematic position angle PA kin (Section 4.3 ) and the morphological position angle PA morph , quantified via the “kinematic misalignment” . We exclude GMASS-2540 here because it is not fully covered by the SINFONI AO data and because of its low H α surface brightness emission hampering a reliable PA kin determination. Following the same motivation as in Section 6.1 , we adopted as PA morph the PA from the H -band morphologies, when available, and from the H α maps otherwise. As noted in Section 5.4 , the PA H α and PA H are similar within 1.5 σ , and we verified that our conclusions would not be affected by instead adopting PA H α for all objects. For our sample, the mean and median ΔPA are 23 and 13 , respectively. For 73% of the galaxies, the alignment is better than 30 . Figure 8 shows the PA offsets as a function of the adopted axis ratio q and effective radius R e . Not unexpectedly, and as seen in other high-redshift samples (e.g., Wisnioski et al. 2015 ; Harrison et al. 2017 ), our galaxies broadly follow a trend of increasing ΔPA for more face-on and smaller sources. Of the 10 objects with ΔPA > 30°, eight are compact sources with distortions/extensions in their outer isophotes, asymmetric light distributions in their inner brighter regions, or little or irregular velocity structure (Q1623-BX502, Q1623-BX513, GMASS-2303, SA12-6339, ZC400528, ZC401925, ZC404221, and ZC411737). The other two sources are large, massive, and fairly face-on disks for which the misalignment can be attributed to morphological features (a noncircular ring with a clump on one side for Deep3a-6004 and clumps in a tail-like structure for ZC400569N; see Section 7.2 for more details). Figure 8. Kinematic misalignment ΔPA as a function of morphological axis ratio q for the SINS/zC-SINF AO sample. The symbols are color-coded and sized according to the effective radius R e of the galaxies. The five compact objects with ill-defined axis ratios and to which we assigned q = 0.64, corresponding to the for randomly oriented disks (Section 6.1 ), are plotted as squares connected with red dotted lines to their nominal best-fit q , shown as circles with a red arrow pointing toward the adopted value. Most of the well-resolved objects show very good agreement between their kinematic and morphological PAs, especially for the more edge-on systems. A larger proportion of the compact sources have ΔPA > 30°, reflecting ill-defined PAs due to
Paper: 2017A&A...607A.133W (fulltext)
the galactic relationship (i.e., Friedman et al. 2011 ) can be applied and it provides the estimate of corresponding to based on the DIB measurement alone, which is in very good agreement with our direct measurement of . Fitting the emission with multiple Gaussians allows an estimate of for the small component at km s as also listed in the table. Figure 6 relates these measurements to those by Welty et al. (2006) , Welty Hobbs (2001) and Friedman et al. (2011) in the Milky Way. The MUSE data allow for excellent measurements of the DIB equivalent widths. The comparison to other DIB data in Fig. A.4 further verifies the method utilized here to measure the equivalent widths in composite fitting residuals of cluster stars as the derived absolute equivalent widths for different DIBs lie within the expected correlation to the strong DIB 5780. Fig. 6 (5780) vs. ( ). The data for the composite spectrum as listed in Table 1 (red star) overlaid to corresponding data for the Milky Way as published in Welty et al. (2006) , Friedman et al. (2011) and Welty Hobbs ( 2001 ; as scatter plot in black). Measurement uncertainties of those data vary between 3 mÅ and 20 mÅ for DIB 5780 and are omitted here for clarity. For our measurement the statistical error is about 12 mÅ and lies within a fraction of the star symbol. 4.2. Spatially resolved Na<inline-formula specific-use="simple-math"><italic>I</italic></inline-formula>, K <inline-formula specific-use="simple-math"><italic>I</italic></inline-formula>, and DIBs 4.2.1. Maps In the previous section we saw that the measurements on the composite spectrum of almost 10 000 cluster stars are in very good agreement with other measurements of individual sight lines in the Milky Way. Rather than finding universal correlations between species, we are in the position to be able to probe correlations between DIBs on very small scales and thus likely in a physically connected absorber. Figure 7 shows a spatial map of and of the DIB ratio 5780/5797 (this ratio is thought to be sensitive to the UV radiation field: Ensor et al. 2017 ). The implications are discussed in Sect. 4.3 . The measured total equivalent width of shows no correlation with the brightness of the GC which would otherwise indicate some underlying systematics in the analysis. Fig. 7 Equivalent width map for the doublet ( top ) and the DIB ratio 5780/5797 ( bottom ). All selected cluster stars are colored in 31 groups according to their Voronoi-bin. The color reflects the measured total equivalent width of the doublet for each composite spectrum per bin with an average of about 960 mÅ. The center of the globular cluster is at coordinate origin and marked with a red star. Brightness contours of the globular cluster are shown as white lines. For the contour plot, the white light image was smoothed to a resolution of 20 arcsec. Further spatial maps are shown in Fig. 8 . The large spatial variations up to a factor of four ( ) or two (DIB 5797) stand out. DIB 6283
Paper: 2016A&A...585A..35P (fulltext)
maximum grain size is determined by the fragmentation barrier (Eq. ( 3 )) and particles can reach centimetre sizes, but small particles are continuously reproduced. For a more massive planet, the location of the pressure maximum (and therefore the peak of mm dust concentration) occurs further away at 30, 44, and 54 AU for 1, 5, and 15 , respectively, as previously shown by Pinilla et al. (2012) . The radial range in which mm-sized grains are concentrated becomes wider. For the case, the second peak is caused by a transient density feature that in reality would be short-lived and would not function as a pressure maximum that can effectively concentrate particles. This issue has no impact on our results since we focus on the inner disk. In the regions inside the planetary gap ( AU) where the gas surface density steeply decreases outwards (and therefore the pressure gradient is negative), the maximum grain size is determined by radial drift (Fig. 2 ), and only close to the snow line ( AU) does the fragmentation barrier drop below the drift barrier due to the change in at . With the decrease in at , the maximum grain size is a few centimetres and the Stokes number of the particles at that location is much lower than unity (), thus they are better coupled to the gas, their radial drift velocities are lower (Eq. ( 2 )), and they move with at a similar speed to the gas (see also Brauer et al. 2008 ). If does not change at , the maximum grain size is dominated by radial drift, reaching values of the Stokes number of around , which would lead to very high drift velocities () depleting the inner disk on very short time scales (100–1000 yr). Therefore, when the inner disk is constantly resupplied from the outer disk, the changes of at allows replenishment of warm, small, and optically thick dust near , for which the drift velocities are slow. In the case of a mass planet, the micron-sized particles (1–10 m), are not perfectly trapped at the outer edge of the planet-gap, and they move continuously through the gap via turbulent diffusion. This partial filtration of dust grains supports a constant replenishment of dust from the outer to the inner disk, which together with variations of at extends the lifetime of a detectable inner disk reaching the life of the disk (5 Myr). In the cases of and mass planets, there is more efficient trapping of particles at the outer edge of the planetary gap, filtering all particle sizes (from 1m, which is the minimum grain size in our setup). The dust inside the planetary gap ( AU) is the dust that was initially within the gap. These particles grow, drift and fragment when they reach the snow line. Although there is a considerable amount of dust in the inner disk for all cases (1, 5, and 15 ) after 1 Myr, after 5 Myr of evolution the inner disk is practically depleted of dust for a disk hosting a and a mass planet. The dust density distribution after 5 Myr of evolution for AU is almost identical for and because the
Paper: 2017ApJ...836..156C (fulltext)
et al. 2015b ). The contaminated regions of the SMICA map were further removed with the Galaxy, point-source, and SMICA -specific temperature and polarization masks, which leave ≈67% of the sky for the minimum-variance lensing reconstruction analysis. Given the sensitivity and angular resolutions of the Planck mission, the analysis thus results in the most significant measurement of the CMB lensing potential map to date. The online data provided by the Planck science team are in the standard HEALPix format (Górski et al. 2005 ). The spherical harmonics coefficients of lensing convergence, , are given for multipoles up to instead of the lensing potential, . We use the standard definition of lensing convergence to calculate fromWe then use the HEALPix synfast package to synthesize the lensing potential map, . The generated map has a resolution of (i.e., pixels). The projected lensing potential map is shown in Figure 1 . This reconstructed lensing potential map is used to cross-correlate with the cosmic void catalog, where the azimuthally averaged potential around each void is extracted, scaled, and then combined to constrain the void density profile. For our analysis, we shall use only those regions that pass the analysis mask (Galaxy + point-source + SMICA ). Figure 1. Lensing potential map, , constructed from Planck lensing convergence, . The gray shaded area marks the rejected pixels excluded by the analysis mask. 2.2. Cosmic Void Catalog In this work, we use the “Public Cosmic Void Catalog” (Sutter et al. 2012b ) constructed using a modified and extended version of the watershed algorithm ZOBOV , called “Void IDentification and Examination” (VIDE; Sutter et al. 2014b ). We applied the code to the SDSS Data Release 7 (Abazajian et al. 2009 ) main galaxy sample and SDSS-III Baryon Oscillation Spectroscopic Survey (BOSS) Data Release 10 (Ahn et al. 2014 ) LOWZ and CMASS samples. This results in individually detected voids. The samples represent volume-limited catalogs of voids at various redshift bins. To minimize any possible evolution of the profile parameters, we choose to work with voids in individual redshift bins and do not stack voids across the bins. In order to have as many voids in a single bin as possible, we chose a high-redshift bin to increase the volume. Here, we report on the analysis using the “dr10cmass1” (, , ) and “dr10cmass2” samples (, , ), hereafter called “cmass1” and “cmass2,” respectively. We therefore stacked the Planck lensing potential map (Section 2.1 ) around the central positions of cosmic voids taken from the Sutter et al. ( 2014b ) “dr10cmass1” and “dr10cmass2” samples as two separated measurements. However, during our analysis we found that considerable numbers (106 and 303 for cmass1 and cmass2, respectively) of the voids are located in the negative lensing potential regions, which could be due to a number of reasons and warrants further investigations. We therefore restrict our analysis to 123 and
Paper: 2018A&A...615A.151B (fulltext)
age uncertainties of these stars (that can be substantial as these solar twin stars are located on the upper main sequence). The diagonal line shows the relationship between Li and stellar age from Carlos et al. (2016) . 3.7. Li in stars with planets Since King et al. (1997) claimed a possibility that the low Li abundances in the Sun and 16 Cyg B relative to 16 Cyg A could be due to the presence of planetary companions in the former two, several studies have presented disparate results regarding whether or not stars with detected planets have different Li abundance patterns compared to those with no thus-far detected planets. Studies that support the idea that there is no difference include Ryan (2000) , Luck Heiter (2006) , Baumann et al. (2010) , Ghezzi et al. (2010) , Ramírez et al. (2012) , and Pavlenko et al. (2018) , while those that find that indeed there is a difference include Israelian et al. ( 2004 , 2009 ), Takeda Kawanomoto (2005) , Chen Zhao (2006) , Gonzalez ( 2008 , 2014 , 2015 ), Figueira et al. (2014) , Delgado Mena et al. (2014) , and Mishenina et al. (2016) . To see whether this study can add some further clues to this controversy the exoplanets website 1 ( Han et al. 2014 ) was queried on February 14, 2018. We find that 36 stars in our sample have detected exoplanets, that 18 of those have well-determined Li abundances, and that 6 have upper limit Li detections. These 18 + 6 stars are marked in red in Fig. 8 . They span a range of effective temperatures from about 5600 K to 6500 K. The bottom panel in Fig. 8 shows the differences between the Li abundance of the exoplanet stars and the mean Li abundance of other stars in the sample that have effective temperatures within ±75 K, log g within ±0.25 dex, and [Fe/H] within ±0.25 dex of each exoplanet star. Due to the low number statistics of stars with similar atmospheric parameters at higher T eff , the temperature range was expanded to ±200 K for stars with T eff > 6100 K. As stars at these temperatures have less Li depleted atmospheres, this expansion should not affect the results. The stars with detected exoplanets have on average 0.08 dex higher Li abundances than those without planet detections, with a dispersion of 0.36 dex. Using the median instead gives a Li-enhancement of 0.04 dex. These numbers are well within the uncertainties and our sample reveals no statistically significant difference in Li abundances between stars with and without exoplanet detections. Fig. 8. Upper panel : red circles mark the 18 stars in the sample that have exoplanets detected and good Li measurements. Bottom panel : difference between the A (Li) for individual stars to the mean A (Li) for stars that have no exoplanet detections and that have effective temperatures, surface gravities, and metallicities, within ±75 K, ±0.2 dex, and ±0.2 dex. For stars with T eff > 6100 K, the temperature range is expanded to ±200 K. 3.8. Li trends in the thin and thick disks It is not straightforward
Paper: 2019MNRAS.484.5400M (fulltext)
be needed to fully address this point. 4 DISCUSSION AND CONCLUSIONS WDM with a particle mass above still provides a valid alternative to the standard CDM scenario. In the previous papers of this series (Frings et al. 2017 ; Macciò et al. 2017 ) we have shown that the results of our simulations of field galaxies and satellite galaxies of the Milky Way in CDM are consistent with current observational data. In this work we investigated if the observations constrain the dark matter to be cold or if they allow for a WDM. For that we presented a sample 42 cosmological simulations of 21 haloes that were run both in CDM and WDM. As expected from the damping of the power spectrum the virial masses of the WDM haloes lie well below their CDM counterparts for masses below . Haloes at larger masses are much less affected. Like the CDM galaxies, WDM galaxies fulfil the extrapolated abundance matching relation from Moster et al. ( 2013 ) even with slightly less scatter. The critical mass for haloes to host luminous galaxies is much higher in WDM, where we find several dark haloes up to a halo mass of . Furthermore WDM seems to allow a larger range of masses where one finds both dark and luminous haloes. We found that WDM and CDM galaxies show no different behaviour in the observable velocity dispersion – size and metallicity – stellar mass relation. We have further investigated the average stellar formation times and found a possible way to distinguish the two populations: on average the WDM galaxies form slightly later, with a 〈 T age 〉 = 8.15 ± 0.70 Gyr ago while CDM galaxies have earlier formation times with 〈 T age 〉 = 10.00 ± 0.43. This result confirms the predictions from previous studies on the formation of dwarf galaxies in WDM (Governato et al. 2015 ; Chau et al. 2017 ), even though the current biases and systematic in observationally derived stellar ages (Guidi et al. 2015 ) prevent any firm comparison with observational data. Finally we found an indication that WDM barely reproduces galaxies at masses around with mean stellar ages of , i.e. that formed in the early universe before the UV background sets in. In agreement with previous work we find that the WDM haloes are systematically less concentrated than their CDM counterparts. On the other hand on small scales, about a few per cent of the virial radius, we confirm that the dark matter density slope is mainly fixed by star formation efficiency (e.g. Tollet et al. 2016 ) and not by the nature of dark matter. As a consequence at low stellar masses ( and below), our (few) data points suggest that the lower star formation efficiency in WDM galaxies might cause a slightly steeper central slope at those masses, but a larger sample of simulated galaxies is needed to firmly confirm this effect. We used four galaxies of the WDM sample and their CDM counterparts to simulate their evolution in a Milky Way potential as described above. We witnessed the lower concentrations of the WDM haloes causing the
Paper: 2017ApJ...845..111M (fulltext)
at best, lower limits to the the true integrated fraction of escaping ionizing photons, and generally offer poor representations of the total number of ionizing photons that escape. The tables in Figure 4 show that LBGs may emit significant amounts of LyC radiation, at the level of 5%–1%, even if the optical depth at the edge is ≈10 (). By way of example, we note that Izotov et al. ( 2016a ) found an edge escape fraction f e 900 = 0.08. Using Equation ( 8 ), we derived an integrated escape fraction of f e LyC = 0.35 or 0.25 for our two cases, respectively. This suggests that the “f-escape” problem might not be as bad as it seems. The only definitive way to rule this out is through deep UV observations in the redshifted rest frame of the EUV in an attempt to observe the LyC drop-in. In the next section we address the observability of the Lyman drop-ins in the face of the steadily increasing mean optical depth of the IGM with increasing redshift. 4. Redshifted Models, Including Mean IGM Transmission We have thus far not included attenuation of the escaping LyC due to resonant scattering and photoelectric absorption (photoionization) from discrete collections of intervening clouds in the IGM. Here we use the rest frame LyC transmission models described in Section 3.1 to compute the mean transmission of the IGM as a function of the fiducial redshifts in Table 1 . The mean transmission function is derived from a mean optical depth (Paresce et al. 1980 ; Madau 1995 ; Inoue et al. 2014 ), expressed here as a numeric integral:Here, the observed wavelength is related to the rest frame wavelength by . The optical depth is . The redshift range from is in fixed increments of . The logarithm of the column densities range from in variable increments of with 0 . The differential distribution of the number of discrete absorbers with respect to H i column density and redshift, , is typically characterized by a piecewise continuous function in redshift and column density (c.f. Madau 1995 ; Inoue et al. 2014 ). There are three basic H i absorption regimes: the Ly α forest regime (LAF), ; the Lyman limit (LL) regime, , and the damped Ly α (DLA) regime, . We employ the described by Inoue et al. ( 2014 ), which uses a “Schechter-like” distribution function for the column density multiplied by a piecewise-continuous multiple power law distribution to describe the evolution in redshift. The distribution function is broken down into the sum of two different products, which in combination reasonably agrees with the evolution of H i absorption systems from the LLA to DLA regimes as detailed in the literature (Weymann et al. 1998 ; Prochaska et al. 2005 , 2010 ; Ribaudo et al. 2011 ; Noterdaeme et al. 2012 ; Fumagalli et al. 2013 ; Kim et al. 2013 ; O’Meara et al. 2013 ; Prochaska et al. 2014 ). We note that the Inoue et al. ( 2014 ) distribution function produces a mean transmission that is generally less aggressive than that produced by the distribution function