adsabs/WIESP2022-NER huggingface dataset:
Paper: 2016A&A...586A..26M (fulltext)
separately for the stellar subset (H ipparcos and Tycho-2 stars) and the quasars. Table 1 , experiment 1, presents the median value of the parallax errors (estimated minus true), the uncertainty of the median calculated using the bootstrap method, and the RSE 4 dispersion of the parallax errors for each of the subsets. The different columns give statistics for selections based on the individual formal standard uncertainties of the parallaxes. The median obtained for the quasar subset agrees with the corresponding stellar value to within a few as, independent of the selection of sources. Table 1 Simulation results of three different experiments comparing the parallax median between the stellar subset and the quasars. Notes. “Stars” refers to the combined subset of H ipparcos and Tycho-2 sources. In each subset, statistics are given for the selection of 90% of the sources with the smallest individual formal uncertainties and for all sources together. The values given are the median (and its uncertainty from the bootstrap method) and the RSE dispersion of the parallax errors (estimated minus true). 3.2. Spurious proper motion from variable source structure Variation in the source structure of quasars can lead to shifts of their photocentres up to the milliarcsecond level (e.g. Popović et al. 2012 ; Porcas 2009 ; Taris et al. 2011 ). Linear trends of these shifts might lead to spurious proper motions measured for quasars and stable over years to decades. Titov et al. (2011) fitted long-term proper motions for 555 quasars from VLBI observations. The total proper motion in as yr in their catalogue can be described by a log-normal distribution with mean 1.9 dex and standard deviation 0.61 dex. It is impossible to say whether these measurements give an optimistic or conservative characterization of spurious quasar proper motions on the much shorter time baselines of our simulations. Additionally, the morphology of the host galaxy might lead to a statistical increase in the centroiding error, and photometric variability of the nucleus together with the stable photocentre of the host galaxy might lead to an effect similar to “variability-induced movers” in binaries ( Wielen 1996 ). Physically all of these effects are expected to be random and therefore should only increase the dispersion of the results but not the median values themselves. We use the statistical properties of the results by Titov et al. (2011) as the basis for simulations, but apply a factor of 10 to provide a conservative assumption on the total spurious motion. The individual components of the proper motion are computed as where is a random position angle and is taken from a normal distribution with mean value 2.9 dex and standard deviation 0.61 dex. The median value of the resulting is about 800 as yr. While this spurious proper motion increases the RSE of the solution for the quasar subset, the agreement of median parallax between the quasars and the stellar subset remains at the
Paper: 2021MNRAS.505.2983C (fulltext)
induced by turbulence (e.g. Raettig, Klahr Lyra 2015 ; Manger Klahr 2018 ) can concentrate dust particles (Barge Sommeria 1995 ; Klahr Henning 1997 ; Cuzzi, Hogan Shariff 2008 ) and seed planetesimal formation through streaming instability or gravitational instability (Youdin Goodman 2005 ; Johansen et al. 2007 ; Chiang Youdin 2010 ), whereas the growth of streaming instability can be substantially diminished by a moderate level of turbulent viscosity (Chen Lin 2020 ; Umurhan, Estrada Cuzzi 2020 ). Turbulence also influences the radial migration of planets, as well as the flow morphology and gap formation around them (Nelson Papaloizou 2004 ; Papaloizou, Nelson Snellgrove 2004 ). Therefore, understanding the origin and characteristics of turbulence in PPDs is essential to many aspects of planet formation and evolution. The lack of magnetohydrodynamic (MHD) turbulence in a PPD led to a surge in the interest of purely hydrodynamic instabilities (Lyra Umurhan 2019 ; Weiss, Bai Fu 2021 ). Among the most explored are the vertical shear instability (VSI; Nelson, Gressel Umurhan 2013 , hereafter N13 ; Lin Youdin 2015 ; Latter Papaloizou 2018 , hereafter LP18 ; Cui Bai 2020 ), the convective overstability in its linear (Klahr Hubbard 2014 ; Lyra 2014 ; Latter 2016 ) and non-linear (subcritical baroclinic instability; Klahr Bodenheimer 2003 ; Petersen, Julien Stewart 2007a ; Petersen, Stewart Julien 2007b ; Lesur Papaloizou 2010 ) phases, and the zombie vortex instability (Marcus et al. 2013 , 2015 ; Lesur Latter 2016 ; Umurhan, Shariff Cuzzi 2016b ). These instabilities set in under certain thermodynamic and structural conditions, thereby operating at distinct regions of PPDs (Malygin et al. 2017 ; Lyra Umurhan 2019 ; Pfeil Klahr 2019 ). The VSI is of particular interest as it extends a large portion of the disc (e.g. Lin Youdin 2015 ; Lyra Umurhan 2019 ). The VSI is inherited from the Goldreich–Schubert–Fricke instability (Goldreich Schubert 1967 ; Fricke 1968 ) and is initially discovered in the context of differentially rotating stars. Its importance to accretion discs was later explored by Urpin Brandenburg ( 1998 ), Urpin ( 2003 ), and Arlt Urpin ( 2004 ). The applicability of the VSI to PPDs has been demonstrated only recently in N13 . It quickly drew intensive interests (e.g. Stoll Kley 2014 ; Barker Latter 2015 ; Umurhan, Nelson Gressel 2016a ; LP18 ; Lin 2019 ; Cui Bai 2020 ; Flock et al. 2020 ; Schäfer, Johansen Banerjee 2020 ; Flores-Rivera et. al. 2020 ) and is considered to be a promising hydrodynamic mechanism in driving turbulence in PPDs. A differentially rotating disc with Keplerian profile is stable according to the Rayleigh criterion (Chandrasekhar 1961 ). The presence of vertical shear can destabilize inertial waves in a vertically global disc model (Barker Latter 2015 ). Nevertheless, a fluid element also experiences stabilizing effects from vertical buoyancy, which impedes the VSI growth. This can be overcome by
Paper: 2019ApJ...880...47M (fulltext)
to contextualize this nebula, we measured the SB Ly α profile through the standard NB images, similar to previous works ( B16 , A19 ). We computed the circularly averaged profile from the pseudo-NB image obtained by collapsing the MUSE data cube using the maximum spectral size of the 3D mask as the width, which in the case of our Ly α emission is 30 Å. We then performed an exponential fit and a power-law fit above the 2 σ noise limit, i.e., SB of ≈0.5 × 10 −18 erg s −1 cm −2 arcsec −2 , that returns a normalization parameter (at a radius of 10 pkpc) of log 10 ( C p , r 10 ) = −15.8 and a slope α of −2.4, compatible with other Ly α SB profiles obtained with MUSE. More details on the fitting process are provided in Appendix B of B16 . The red line in Figure 7 shows the circularly averaged values computed in units of SB × ((1 + z )/4) 4 with an extent of more than 100 pkpc from the QSO. We chose these units because we wanted to perform a consistent comparison of the J0952+0114 SB profile with the other Ly α systems in the literature and therefore avoid any redshift dependence and cosmological dimming effects. Figure 7 also shows the comparison with previous works as the median profile of the MUSE QSO nebulae we published in Borisova et al. 2016 (purple line, see also Appendix C ) and the best exponential fit to the QSO MUSEUM sample ( A19 , blue line). As discussed later in Section 5 , the outer parts (beyond 10 pkpc) of the J0952+0114 SB profile are very similar to what we found in other giant fluorescent nebulae around QSOs at z ≈ 3. The innermost regions are brighter due to the presence of the PDLA; however, this could reflect the difficulty of tracing the nebula into the center of typical objects (without PDLAs). Figure 7. Ly α surface brightness (SB) profile of the J0952+0114 nebula as a function of the projected physical distance from the QSO. The circularly averaged profile is plotted with the red solid line. Other Ly α SB profiles are also plotted for comparison. In particular, the median SB profile of the MUSE Ly α nebulae from B16 is shown in purple with the dashed lines marking the 10th and 90th percentiles. This median profile has been rescaled to z = 3. The best exponential fit (and its dispersion) of the SB profiles from the QSO MUSEUM is plotted with the solid blue line (light blue area, Arrigoni Battaia et al. 2019 ). 4.4. He <sc>ii</sc>/Ly<italic>α</italic> and C <sc>iv</sc>/Ly<italic>α</italic> Map Ratios Ly α λ 1216, C iv λ 1550, and He ii λ 1640 are some of the brightest rest-frame UV lines employed to better constrain the nature of ionized gas in emission. In particular, the ratios of these lines, i.e., He ii /Ly α and C iv /Ly α , are particularly useful for insights into the physical properties of the emitting gas, such as the ionization parameter, density, and metallicity (Villar-Martin et al. 1997 ; Arrigoni Battaia et al. 2015 ; Prescott et al. 2015 ; C19 and references therein). Differently from previous works, in
Paper: 2017ApJ...834..181O (fulltext)
optically thin, and uniformly expanding shells as the source of emission in the line wings, since they produce a top-hat line profile without individual emission peaks (e.g., Zuckerman 1987 , pp. 185–209; Cid Fernandes Terlevich 1994 ; Tenorio-Tagle et al. 1996 ; and also the Appendix ). However, the two-hump emission can be caused by a non-spherically symmetric shell or a shell having internal absorption (the other alternatives are analyzed and discarded in Section 5.2 ). Even though the observed amplitudes are similar, the blue component of each pair tends to be larger than the red component. Figure 9 shows the ratio between the areas of the red and blue components, and in all but one case, this ratio is one or smaller, considering error bars. Moreover, if one of the components is missing, it tends to be the red one. These two facts suggest a dusty expanding shell model, where the receding cap of the shell is obscured by the approaching cap (see the Appendix and Figure 17 ). These issues are discussed in detail in Section 5.2 . The color code employed in Figures 9 – 15 assigns a color to each galaxy, and then the different symbols of the same color correspond to the various pairs of secondary components in that galaxy. Figure 9. Area ratio between the blue and red emission of paired components vs. mean strength of the secondary components relative to the central component, i.e., area ratio vs. ε . Each point corresponds to one pair in one of the galaxies, as indicated in the inset—each galaxy has a color, and different symbols with the same color correspond to different weak components of the same galaxy. When only one component exists, it tends to be the blue one (they are marked with an asterisk in the inset). We show unpaired components as having an area ratio equal to zero. Except for ML32, the ratio is consistent with one or smaller. There is no obvious trend in the observed scatter plot. Using the equations in Section 3.3 , we have estimated the mass, mass loss rate, and kinetic energy associated with all the weak components in the H α wings (Section 3.2 , Figure 3 ). The mean values, as well as the errors, are given in Table 4 . The velocities and the ratio between the secondary components and the central component, ε , are provided directly by the multi-Gaussian fit described in Section 3.2 . In the case of paired components, the velocity is the mean of the absolute values of the two velocities, and ε is the total ε of the pair. The estimates also depend on the temperature and electron density of the emitting plasma, as well as on the relative thickness of the shell, , and the filling factor, f . Table 4 Parameters for the Weak Components in the H α Wings a Notes. a The errors in this table represent the standard deviation across the starburst (for velocity, ε , and area ratio) or errors for the value obtained from the mean H α line of the starburst (for the rest). b The number added to the galaxy name identifies each of the pairs of
Paper: 2021ApJ...911...21B (fulltext)
are fully available in the machine-readable version. We present one catalog for each band, containing the 1730 SDSS+ATLAS cluster galaxies for which the pipelines ProFound + ProFit work. 4. Results We aim to shed light on the formation of S0 galaxies in clusters. In particular, we focus on understanding the relative importance of environmental processes acting in the cluster core and preprocessing acting in the cluster outskirts. We study the g − i colors separately for bulges and disks up to ∼2.5 R 200 , including the infalling cluster regions where galaxies are mainly in groups or isolated. We explore the color–magnitude relations for the two galaxy components. To understand the impact of the environment, we investigate the dependence of the bulge and disk g − i colors on the projected cluster-centric distance R / R 200 and the local galaxy density. We disentangle backsplash and infalling galaxy populations in the PPS. Finally, we compare the results for double-component galaxies with single-component disk-dominated galaxies. 4.1. Color–Magnitude Relations We aim to understand whether the bulge, disk, or both are responsible for the global galaxy color–magnitude relation observed in clusters. The study of the bulge and disk color–magnitude trends helps us to assess the mechanisms that drove the evolution history of the two components. We explore the extinction- and k -corrected bulge and disk g − i colors for the 469 double-component galaxies within 2.5 R 200 and with reliable bulge and disk measurements as described in Section 3.3 . Figure 9 shows the g − i color distributions for the bulges and disks. The disks are mainly shifted toward bluer colors with respect to the bulges. The median g − i offset separating the two distributions is 0.11 ± 0.02 mag. We find that 67% ± 2% of the galaxies have a bulge that is redder than the disk, while for the remaining 33% ± 2%, the bulge is bluer than the disk. Figure 9. Histogram in g − i color for bulges and disks of the 469 double-component galaxies within 2.5 R 200 . The dashed lines represent the median values. Disks are shifted toward bluer colors with respect to bulges by 0.11 ± 0.02 mag. We investigate the g − i color–magnitude relations for double-component galaxies and for bulges and disks in the left and right panels of Figure 10 , respectively. The total absolute magnitude of the galaxy M g has been estimated using , where D L is the luminosity distance measured using the cluster redshift. The k -correction and Milky Way extinction corrections are applied. We fit the g − i color–magnitude values using the Hyper-fit software, which takes into account the random uncertainties associated with the data (Robotham Obreschkow 2015 ). Figure 10. Color–magnitude diagrams for the 469 double-component galaxies within 2.5 R 200 (left panel) and the bulges (red and magenta) and disks (blue and light blue; right panel). In both panels, M g is the total absolute magnitude of the galaxy. The
Paper: 2016ApJS..225...23S (fulltext)
initial selection of compact group candidates. They emphasize that the large angular size of the isolation region for low-redshift systems artificially removes them from a catalog. Figure 8 of Barton et al. ( 1996 ) shows that groups with larger isolation regions have smaller sizes for groups selected from the CfA redshift survey. Here, again the limiting apparent magnitude for the definition of the isolation radius is the limiting apparent magnitude of the catalog rather than a limit three magnitudes fainter than the brightest group member. Eliminating the isolation criterion includes these nearby groups at the expense of including compact group candidates that are substructures in massive systems. However, the compact group candidates within massive systems or in dense regions can be removed after the initial selection. In contrast, the low-redshift systems cannot be recovered. The probability of rejection as a result of the isolation criterion can increase with decreasing redshift depending on the details of the application. The lowest redshift compact group candidates have larger angular size and a correspondingly more extensive isolation region than more distant compact group candidates. Figure 7 (a) shows the difference between the redshift distribution of the MLCG systems and the SDSS DR6 compact groups. Because McConnachie et al. ( 2009 ) remove r 14.5 galaxies, they might miss some nearby groups, thus accounting for the difference. To test this conjecture, Figure 11 plots the redshift distribution of the MLCG systems containing only galaxies in analogy with the SDSS DR6 sample. Although we remove 407 MLCG systems containing bright members, we still identify plenty of compact groups in the local universe ( z 0.05) where SDSS DR6 compact systems are absent or rare. Figure 11. Redshift distribution of the MLCG systems containing only galaxies with (open histograms) compared with the SDSS DR6 compact groups (filled histograms, Sohn et al. 2015 ). Because we do not apply an isolation criterion in the selection algorithm, we also include compact group candidates in dense surroundings (see Figure 10 ). Indeed, the fraction of the MLCG systems in dense environments ( N C > 7) is larger than for the SDSS DR6 compact groups at all redshifts. Interlopers may occur more often in high-density regions, thus producing some of the MLCG systems with inflated line-of-sight velocity dispersions. Barton et al. ( 2007 ) emphasize that a study of some aspects of galaxy evolution in tight pairs and compact groups requires specialization to relatively low-density environments. As in pair studies, candidate systems in dense surroundings can be removed after the general group selection. 5.2. Environmental Effects The diverse local environments of compact groups have been discussed previously (e.g., Ramella et al. 1994 ; Ribeiro et al. 1998 ; Andernach Coziol 2005 ; Mendel et al. 2011 ; Díaz-Giménez et al. 2012 ; Pompei Iovino 2012 ; Díaz-Giménez Zandivarez
Paper: 2021ApJ...909..206B (fulltext)
With that purpose, we have selected seven regions where the dust-to-gas ratio is larger than the mean expected value for the diffuse ISM, and for consistency, we keep the same names as in Paper I : H1, H2, H4, L1, L2, L4, and M1. In regions H1–H4, the gas density value ρ is greater than 1 σ ; in regions L1–L4, it is lower than 1 σ ; and in region M1, the gas density reaches intermediate values. In all cases, conspicuous filaments of charged dust are formed aligned with the magnetic fields. We want to note that the computational domains for coagulation simulations are squared domains that contain the original regions, so their limits are slightly different from those in Paper I ; the properties of these regions are summarized in Table 1 . Table 1 Properties of the Selected Regions Note. Regions selected from the turbulence simulation (Paper I ) to study the performance of the dust coagulation algorithm. In all cases, they are taken to be squared regions of a fixed length as given in the table, and with a uniform resolution in x - and y -directions. Given a region, we extract the gas properties and particle positions and set them inside a new domain with normalized limits [0, 1] × [0, 1]. The boundary conditions are outflow to ensure that the divergence-free constraint holds, ∇ · B = 0. This implies that some mass of dust will be inevitably lost, but we will take this fact into account when discussing the performance of the algorithm. The initial dust population is homogeneous, with a common radius of 0.05 μ m, a charge of Z d = −17, and an internal solid density . As explained in Section 2 , the computational test particles have to be considered as the centroids of a squared cloud with linear size 2 b that depends on the dust content. At the initial stage, all the clouds have the same linear size, typically 1.4 times the pixel size; this means that the clouds span a region of 2 × 2 pixels. The system is then left to evolve an arbitrary amount of time that differs from simulation to simulation. The final time is subjectively set following the condition that dust particles have moved enough to produce substantial coagulation, but they have not been completely swept away from the domain. A typical value is (∼3 × 10 −3 Myr; see Table 1 ), which, when compared with the global simulation (), is ∼7% of the total time. Hence, we expect that the phenomena observed in the simulations presented here are representative of the evolution of the global system (Paper I ) at short timescales. Besides, if we compare this time with the typical lifetime of a molecular cloud (∼10 6 Myr; Hartmann et al. 2001 ), it is evident that the timescales treated in this work are so small that we can consider that the final size distributions are representative for a molecular cloud at any stage of its evolution, provided that the ambient conditions (temperature, ionization fraction, magnetic field strength, and density) are maintained. 3.1. Evolution of the Size
Paper: 2017ApJ...840L..20L (fulltext)
(95 au). Blackbody dust at ∼135 K, as found in the longest wavelength source peaking at ∼40 μ m of our HD 36546 SED (Figure 2 ) would have to be located at ∼20 au from a 20 primary. Allowing for the superheat ratio = 100 K/71 K = 1.41 found by NIRDS for dust in the HR 4796A (A0.5V) ring at ∼75 au from its primary (Lisse et al. 2017 ) moves this location out to ∼40 au, closer to the Curie et al. estimate. The remaining discrepancy between the spectral and imaging radial distance estimates might be due to our poor current characterization of the MIR–FIR SED peak; to our lack of understanding of the superheat behavior of the HD 36546 outer dust (values of out to 5–15 have been reported by Rodriguez Zuckerman 2012 and Vican et al. 2016 ); or to the different sensitivities of scattered light ( surface area*albedo) versus thermal emission ( surface area*emissivity* T 4 ) measurements of circumstellar dust in a thick disk, which will weight closer-in dust thermal emission most highly (Watson et al. 2009 and references therein). Using the T ∼ 570 K fit to the continuum underlying the warm SED excesses in Figure 2 , we find that the dust component creating the ∼8 μ m peaked emission would have to lie somewhere near 1.1 au for pure blackbody behavior, or near 2.2 au for superheated grains with . Either estimate is well within the Currie et al. coronographic masking region, so no direct comparison is possible. 5. Conclusions (1) The HD 36546 primary is a nearly unextincted early A star, not a late B star. Our modeling favors A1.5V +/−1 subclass, which is slightly later than the A0V classification found from optical spectra (Currie et al. 2017 ). Both optical and near-IR spectra rule out spectral types earlier than A0. The best-fit photospheric models require very low or no extinction, implying that HD 36546 is in front of the 1–2 Myr old Taurus–Auriga star-forming region as seen from Earth, and that there is little to no in-system extinction of the primary star. (2) There are no detected 2.1–2.5 μ m CO emission lines due to fluorescing circumstellar gas, no HBr γ lines due to accretion, and no He i 1.083 μ m, Fe ii 1.256/1.644 μ m, or S(1) 1-0 2.121 μ m emission lines due to strong wind outflow. No emission lines due to neutral PAHs are present at 3.29 μ m. (3) From 0.7 to 4.0 μ m, the SED is not “flat” or “filled-in” by inner-disk hot dust as in YSOs or transition disks. There are the beginnings of warm dust excess flux in the 4.5–5.0 μ m region, agreeing with archival JHKLM photometry of the system (triangles in Figure 2 ) showing a rise in M-band and WISE W2. (4) There are indications of an unusual local maximum in the SED at 6–9 μ m. A similar feature was seen in the A3V, ∼10 Myr old debris disk system HD 131488 and at lower SNR for the similarly bright HD 121191 system (A8V, ∼10 Myr) by Melis et al. ( 2013 ) using T-ReCS. The Mittal et al. ( 2015 ) Spitzer /IRS disk survey contains another good spectral match, the HD 148657 (A7V, 12 Myr) system. The