adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017ApJ...840...19S (fulltext)
an oscillatory process, in which two pulses with different directions are superimposed. In the first case, a random velocity with normal distribution is simulated, see upper panel of Figure 8 . In order to apply exactly the same method used with the real data, we filtered the simulated velocity signal employing the EMD (middle panel of Figure 8 ). The polarization of this filtered signal is then visualized through a compass diagram just like what was done in the case of the observed data. Besides, we simulated a simple oscillatory signal of the form:where A is the amplitude of the velocity signal, T 1 is period, and T 2 is long-term modulation to simulate a wave train (see lower panel of Figure 8 ). Figure 8. Top panel: simulated random velocity signal with normal distribution. Middle panel: same velocity signal filtered using the EMD. Bottom panel: simulated velocity signal made with two superimposed pulses in the x and y direction respectively. The two pulses are exactly the same, but slightly shifted and with different amplitudes. The compass diagrams of the two simulated velocity signals are shown in Figure 9 . In comparison with the oscillatory signal, the random process does not display polarization (top panel). This confirms the validity of our findings. In contrast, the same kind of polarization of the compass diagram is indeed obtained with a superposition of two pulses in different directions (bottom panel). Figure 9. Compass diagram of the simulated random velocity signal with normal distribution (top), and the one obtained from a superposition of two velocity pulses in two perpendicular directions (bottom). 4. Discussion and Conclusion The results presented above show low-frequency (5–6 mHz) kink-like horizontal oscillations of SSMEs in chromosphere with an elliptic polarization of the velocity vector (see Figure 1 for a schematic representation) observed in all of the 35 SSMEs analyzed in the quiet chromosphere. This frequency range is inconsistent with residual seeing aberrations. In this regard, it is worth noting that the orientation of the velocity vector in time is not random, but reflects a long-term coherency (several minutes). This timescale is by far longer than the typical timescale of atmospheric turbulence (a few ms). Indeed, Stangalini et al. ( 2016 ) have deeply analyzed the decorrelation timescales of adaptive optic residual aberrations using on-sky data, and found them to be of the order of 10–20 ms at visible wavelengths (∼630 nm). This is consistent with independent works in the literature (see, for example, Davis Tango 1996 ). Since the turbulence timescales at the Ca ii H wavelengths is even shorter (Roddier 1999 ), we expect a large reduction of the turbulence coherence time, resulting in a turbulence timescale that is even more inconsistent with that of the process observed here. In addition, since the seeing residual aberrations are a random process, one cannot expect any coherent helical motion of the
Paper: 2016ApJ...833...72B (fulltext)
et al. ( 2009 ). Similar to the conclusions from Bouwens et al. ( 2009 ), we find that most of the SFR density at z ≳ 3.5 appears to be directly observable in the rest-frame UV (see also Burgarella et al. 2013 ; Bourne et al. 2016 ; Dunlop et al. 2016 ). 5.3. Implications for the Lyman Continuum Photon Production Efficiencies Another consequence of our new ALMA results is for the interpretation of the prominent H α emission lines inferred in galaxies at z ∼ 4–5 based on the observed Spitzer /IRAC 3.6 μ m and 4.5 μ m fluxes (e.g., Schaerer de Barros 2009 ; Shim et al. 2011 ; Stark et al. 2013 ). As first noted by Shim et al. ( 2011 ), the total H α luminosities of galaxies at z ∼ 4–5 are in excess of what one might expect based on their luminosities in the rest-frame UV. The excess is conservatively as large as a factor of 2 using the general z ∼ 4–5 LBG selections (Smit et al. 2015 ) but was earlier reported to be a factor of 6 (Shim et al. 2011 ) using samples selected based on their emission line properties. One explanation for these high luminosities would be if the selected galaxies predominantly had young stellar ages (given that the Ly α emission was prominent in the spectroscopic sample considered by Shim et al. 2011 ) or if dust extinction preferentially had a larger impact on the observed UV-continuum fluxes than it did on the observed H α fluxes (Marmol-Queralto et al. 2016 ; Smit et al. 2015 ). Neither mechanism appears to provide a fully satisfactory explanation for the high H α fluxes in z ∼ 5 galaxies. Dust extinction falls off rapidly toward lower masses, but the H α EWs in these sources remain essentially unchanged (or possibly increase toward lower mass: see Smit et al. 2015 ). Similarly, young ages for the z ∼ 4–5 galaxy population cannot provide an explanation, because a high ratio of H α to UV continuuum is observed for typical star-forming galaxies at z ∼ 4–5 (Marmol-Queralto et al. 2016 ; Smit et al. 2015 ). 31 31 Shim et al. ( 2011 ) had speculated that young ages might explain the high ratios of H α to UV continuum observed in z ∼ 4–5 galaxies they studied that showed strong Ly α emission. However, it is now clear that such ratios apply both to sources that show Ly α in emission and to those that do not (Smit et al. 2015 ). This essentially forces us to conclude that the observed ratio of H α to UV-continuum luminosity must be intrinsic and that star-forming galaxies indeed have very high H α luminosities relative to their luminosities in the UV continuum, particularly in comparison with conventional stellar population models (i.e., Bruzual Charlot 2003 ). It has been speculated that such could be achieved due to binarity or rapid rotation in massive stars, allowing them to output large amounts of ionizing radiation tens of million of years after an initial burst of star formation (e.g., Yoon et al. 2006 ; Eldridge Stanway 2009 , 2012 ; Levesque et al. 2012 ; de Mink et al. 2013 ; Kewley et al. 2013 ; Leitherer et
Paper: 2020MNRAS.492..848A (fulltext)
however, and is, therefore, highly dependent on the host galaxy colour, with bluer galaxies having the highest Type Ia SNe rate per unit mass (e.g. Mannucci et al. 2005 ; Mannucci, Della Valle Panagia 2006 ). Type Ia SNe commonly occur in elliptical galaxies, including those with very little recent star formation (e.g. Oemler Tinsley 1979 ; Cappellaro et al. 1999 ; Mannucci et al. 2005 ; Barkhudaryan et al. 2019 ), however, showing at least some progenitors are from older stellar populations. The radial distribution of Type Ia SNe does not correlate with the UV flux distributions at a statistically significant level (see Fig. 4a : p = 0.01 for both NUV and FUV), in agreement with Anderson et al. ( 2015b ). As UV is a useful tracer of moderate- to high-mass stars and, therefore, also a tracer of moderate emission time-scales (e.g. Hygate et al. 2019 ), the divergence of the UV CDF from the dotted normal line in Fig. 4(a) suggests that Type Ia SNe have progenitor systems from different mass and time-scale ranges. 3.2 Type II SNe The radial distribution of Type II SNe shows strong agreement with both UV fluxes ( p = 0.73 and 0.71 for NUV and FUV, respectively) and differs from the R -band and H α light distributions at, or near, statistically significant levels: p = 0.05 and 0.02, respectively (see Fig. 5a and the Appendix). Type II SNe occur throughout the optical disc and examples can be found near or at (see also Figs 6e and f ). Figure 5. CDFs for (a) Type II SNe, (b) stripped-envelope supernova (SE SNe: Types Ib, Ic, Ib/c, and IIb), and (c) the combined Type II and SE SNe sample. See Fig. 4 for further description. Figure 6. Normalized radial flux profiles shown as separate differently coloured lines, with SN locations indicated with small red circle(s): Type Ia SNe (top row), Type II SNe (middle row), and SE SNe (bottom row). The average radial positions for Type Ia, Type II, and SE SNe are marked on each plot using a black cross, circle, and triangle, respectively, and these show the now well-known centralization of SE SNe compared to Type II SNe. To aid comparison, the mean positions of the other SN types are shown with grey symbols. The lack of SE SNe in the outer regions of the UV apertures is evident in panels k, l, with no SE SNe observed in the outer ∼30 per cent of the UV light distribution, compared to the absence of SE SNe in the outer ∼18 per cent of the H α light distribution (panel j). See Sections 3.3.1 and 4.2 for further discussion. The H α CDF for Type II SNe (see Fig. 5a ) has a central section with a reduced slope; just over 10 per cent of Type II SNe are found in 30 per cent of the flux (between 30 and 60 per cent of the total flux – see also Fig. A1b ). Strong bars are known to cause a notable suppression of massive star formation in early-type spirals (see Hakobyan et al. 2016a and references therein) and this could contribute to this CDF feature. No Type II SNe are found in the outer ∼30 per cent of the FUV
Paper: 2020MNRAS.495..402Q (fulltext)
of 3°–14°. Figure 10. Top panel: Wind simulation with parameter values set to match P04: f x = 0.1, R in = 60 R g , cm/s, T = 2.5 × 10 4 K, and n 0 = 2.5 × 10 9 cm −3 . We also set τ UV = 0, and σ X = σ T as it is done in P04. Bottom panel: Wind simulation with same parameters as the top panel but using the standard τ UV and τ X of Qwind . 4 CONCLUSIONS AND FUTURE WORK We have presented an updated version of the Qwind code ( Qwind2 ), aimed at modelling the acceleration phase of UV line-driven winds in AGNs. The consistency of our approach with other more sophisticated simulations shows that the non-hydrodynamical treatment is well justified, and that our model has the potential to mimic the results of more expensive hydrodynamical simulations. The main free parameters of the model are the initial density and velocity of each streamline, and the inner disc radius from which the fluid elements are first launched. Nomura et al. ( 2013 ) calibrate the initial wind mass loss using the relation from CAK that links the wind mass loss from O-stars to their gravity and Eddington ratio. However, it is not clear whether this relation holds for accretion discs, where the geometry and the radiation field and sources are quite different (Laor Davis 2014 ). To be able to derive these initial wind conditions from first principles, we require a physical model of the vertical structure of the accretion disc. Furthermore, we need to take into account the nature of the different components of the AGN and their impact on the line-driving mechanism. In that regard, we can use spectral models like Kubota Done ( 2018 ) to link the initial conditions and physical properties of the wind to spectral features. We aim to present in an upcoming paper a consistent physical model of the vertical structure of the disc, considering the full extent of radiative opacities involved, that will allow us to infer the initial conditions of the wind. Another point that needs to be improved is the treatment of the radiation transfer. Qwind and current hydrodynamical simulations compress all of the information about the SED down to two numbers L X and L UV; however, the wavelength-dependent opacity can vary substantially across the whole spectrum. This simplification is likely to underestimate the level of ionization of the wind (Higginbottom et al. 2014 ) and motivates the coupling of Qwind to a detailed treatment of radiation transfer. Higginbottom et al. ( 2013 ) construct a simple disc wind model with a Monte Carlo ionization/radiative transfer code to calculate the UV spectra as a function of viewing angle; however, properties of the wind such as its mass flow rate and the initial radius of the escaping trajectories need to be assumed. We will incorporate a full radiative transfer code like Cloudy or Xstar to compute the line driving and transmitted spectra together. This also opens the possibility of having a metallicity-dependent force multiplier and studying how the wind
Paper: 2019ApJ...874..117B (fulltext)
how the SMBH population evolves over cosmic time, we populate the halo tree according to our recipe. When a halo for the first time reaches a mass M c , with chance p it is marked as occupied by a black hole. The occupation is inherited by the merger tree descendant. As a consequence, in our seeding framework black holes are not only “born” in the high-redshift universe. To show this, the left panel of Figure 5 presents birth rates over cosmic time. Seeding is most frequent at early times ( z > 4). Depending on the model parameters, the median seeding time lies in the second, third, or forth billion years. At z 6, the model curve shapes are almost independent of the input parameters. Regarding the normalization, we note that a 100% seeding above mass M c creates a black hole population at a similar rate as 10% seeding at mass 10% × M c (left panel of Figure 5 ). Figure 5. Left panel: the birth rate of black holes over cosmic time. Curves show different seeding fractions p and mass thresholds M 0 . Center panel: merger rate of halos occupied by black holes. The kink at z ∼ 1 is due to the simulation’s cosmic variance, i.e., a large overdensity merging at that redshift. Right panel: the total number of halos with black holes. We compare with space densities of AGN with luminosities (AGN) and (QSOs) measured by X-ray surveys sensitive to unobscured and obscured accretion (Buchner et al. 2015 ). Comparing the model curves and observations, we see that only a very small fraction (1:10 to 1:10,000) of black holes accrete at these luminosities. As halos containing black holes merge, interactions of multiple SMBHs are possible. The middle panel of Figure 5 shows mergers of halos hosting black holes. The normalization again scales as just described for the birth rate. Changes in the critical mass shift the peak slightly. We explore the possible merging of the black holes themselves in the next section. The right panel of Figure 5 presents the total number of massive black holes over cosmic time. For p = 1, this is just the number of halos above the mass threshold. Overall, we find a present-day SMBH space density of >0.01 Mpc −3 (with models not ruled out in Figure 4 ). In the local universe, the fraction of occupied halos is mass-dependent in the way presented in Figure 3 . The comparison between the number of SMBHs to the observed number of AGN provides the fraction of actively accreting black holes in excess of that luminosity threshold. Modern surveys detect hard X-ray emission even in heavily obscured AGN, and recover the intrinsic accretion luminosity using X-ray spectra (e.g., Buchner et al. 2015 ). In Figure 5 we include the space density of AGN at L (2–10 keV) > 3 × 10 42 erg s −1 and >10 44 erg s −1 , which correspond approximately to accretion rates of 5 × 10 6 and 5 × 10 8 M ⊙ Gyr −1 (Marconi et al. 2004 ), respectively, assuming 10% radiative efficiency. The AGN density inferred from observations is 1–3 orders of magnitude below the
Paper: 2018A&A...618A.157D (fulltext)
from four Gaussian functions in the surface brightness − colour plane. Notes. The MP values quoted in the second column are the mixing proportions for each component of the mixture model. For the mean vectors and covariance matrices, the x variable corresponds to the galaxy colour g − r , while the y variable is the surface brightness μ r . Table C.2. Galaxies in the SDSS DR14 photometric catalogue around compact groups that are potential sources of contamination. We measured the surface brightnesses ( μ r ) and observer-frame colours ( g − r ) of each spectroscopic galaxy and of the potentially contaminating galaxies. The surface brightness of each galaxy was computed using the Petrosian radius provided by the SDSS pipeline. After analysing the surface brightness − colour relation, we used the Mclust package ( Scrucca et al. 2017 ) of R software to perform a density estimation based on finite Gaussian mixture modelling. From its application to our data in the surface brightness − colour plane, we observed that, according to the Bayesian Information Criterion, a proper density representation can be obtained using a mixture of four Gaussian functions for a model with ellipsoidal distribution that allows variable volume, shape, and orientation. The best fitted parameters for the obtained Gaussian mixture modelling are quoted in Table C.1 , while the perspective plot of the resulting surface density is shown in the upper panel of Fig. C.1 . Using this surface density, we defined the isodensity contour that enclosed 95% of the spectroscopic galaxies in the surface brightness − colour plane. This region is represented as the grey. shaded area in the lower panel of Fig. C.1 . We also show the scatter plot for the potential sources of contamination (red dots) Photometric galaxies lying outside the grey region are considered non-contaminating galaxies (empty red dots). This criterion allowed us to discard 104 galaxies. Finally, as described in Sect. 4.2 , after a visual inspection of the remaining 81 objects, we confirm that 13 were actually objects misclassified as galaxies by the SDSS pipeline. Therefore, only 68 galaxies are kept as potential sources of contamination. The SDSS object identification numbers of these 68 galaxies are quoted in Table C.2 . Appendix D: New CG catalogue in the SDSS In this appendix we present the tables of HMCGs identified in the SDSS. In Table D.1 we show the content for the table containing 462 HMCGs, while Table D.2 shows the content for the table that contains the information for the 2070 galaxy members. Table D.1. Compact groups identified in the SDSS DR12 using the modified algorithm. Notes. HMCGid: group ID, N : number of galaxy members, RA: group centre right ascension (J2000), Dec: group centre declination (J2000), Redshift: group CMB redshift, Θ G : angular diameter of the smallest circumscribed circle, μ r : r -band group surface brightness, σ v : radial velocity dispersion, r b : r -band observer-frame
Paper: 2017MNRAS.466L..24L (fulltext)
a new set of young solar-type stars. See etal. ( 2015 ) analysed the magnetic field topologies of 55 stars with masses in the range 0.11.5M and discovered that the toroidal field scales with the inverse Rossby number more steeply than the poloidal field does. The toroidal field fraction shows two different power-law dependences on the poloidal field for stars above and below 0.5M . Additionally, they found that strong toroidal fields are typically axisymmetric. To set highly resolved solar or simulated magnetic vector maps into the context of ZDI-observed cool stars, Vidotto ( 2016 ) presented a magnetic field decomposition method. The method is compatible to the description used in ZDI studies and decomposes the magnetic field into spherical harmonics of different -modes up to a maximum max . The small -modes describe the large-scale field ( 1 dipolar, 2 quadrupolar mode, etc.) and the higher -modes the small-scale field. This small-scale field can be removed from the simulations or solar observations by selecting low -modes to compare their magnetic field topology with the magnetic field topology of observed stars. In this Letter, we compare the global time-dependent non-potential simulations of Gibb etal. ( 2016 ) based on the surface flux transport model, with the sample of observed stars 1 analysed by See etal. ( 2015 ). In particular, we compare their magnetic field topologies, focusing on the poloidal, toroidal, and their axisymmetric components at different spatial scales. 2 SIMULATIONS AND METHODS The simulations of Gibb etal. ( 2016 ) used a magnetic flux transport model for the photospheric evolution, coupled with a non-potential coronal evolution model described by a magnetofrictional technique. The magnetic flux transport model solves the magnetohydrodynamic induction equation to evolve the surface magnetic flux, whilst a flux emergence pattern, e.g. bipolar starspot pairs, are advectively injected, and then sheared by surface DR, poleward meridional flow, and diffusion (Charbonneau 2014 ). The flux emergence pattern is based on Yeates ( 2014 ) who determined flux emergence properties using solar synoptic magnetograms observed by the US National Solar Observatory, Kitt Peak, between 2010 January and 2011 January. Gibb etal. ( 2016 ) then varied the flux ER using a statistical model, and the DR in the range of 1 ER/ER 5 and 1 DR/DR 5. The variation of the parameters leads us to 17 simulations ranging from solar-like stars up to stars with five times solar flux ER and DR. The immense difference in resolution prevents a direct comparison between the highly resolved simulated and the relatively poorly resolved observed stellar magnetic field vector maps. Vidotto ( 2016 ) presented a method to filter out the small-scale field from highly resolved solar magnetic field vector maps to ease the comparison with stellar magnetic field vector maps. The method decomposes the radial, azimuthal, and meridional magnetic field components into
Paper: 2020MNRAS.495..417C (fulltext)
in size. The spectra were reduced with NOSTAR 2 using the standard procedure. For the molecular analysis, we also used SEST CO(2-1) data that were kindly provided to us by A. P. Marston in 2008 (see Marston et al. 1999 ), and NANTEN CO(1-0) obtained in 2001 (see Paper I ). 3.2 H <sc>i</sc> and radio continuum data Current radio surveys are of little use to inspect the surroundings of WR16 with enough resolution and sensitivity. In Paper I , the radio continuum data at 4.8 GHz were obtained from the Parkes-MIT-NRAO (PMN) survey. At the location of WR16, the survey contains observations performed with the Parkes 64-m antenna. However, the beam size (∼5 arcmin × 5 arcmin) is comparable to the diameter of the ring nebula (≲8 arcmin), which hampers a detailed study. To render an acceptable resolution, observations must be performed using interferometeric techniques, and only two radio interferometers, the Australia Telescope Compact Array (ATCA) and the Molonglo Observatory Synthesis Telescope (MOST), offer public surveys covering an area compatible with the Galactic longitude of WR16. The Southern Galactic Plane Survey (SGPS; McClure-Griffiths et al. 2005 ), which includes both H i and continuum emission data from the ATCA, is limited to −1° ≤ b ≤ +1°, and WR16 lies more than 1° away south of this band. On the other hand, the radio continuum emission towards WR16 at 843 MHz from the Molonglo Galactic Plane Survey (MGPS-2; Murphy et al. 2007 ) is affected by strong sidelobes introduced by the bright nearby H ii region RCW 46. To obtain the best possible set of maps, we then processed from scratch archival radio observations performed during 2001 and 2002 towards this region with the Australia Telescope Compact Array (ATCA) in the configurations EW 352 and EW 367 under project C961. The region was observed simultaneously in the H i 21-cm line and in radio continuum. The continuum data consist of 32 channels spanning through a bandwidth of 128 MHz centred at 1384 MHz, while the H i line was recorded over 1024 channels covering a 4 MHz bandwidth (equivalent to 0.82 km s −1 ). Pointings were arranged in an hexagonal mosaic with a 23 arcmin grid separation and were observed as snapshots during 60 s over a wide range of hour angles. All observation parameters are summarized in Table 1 . Table 1. Parameters of ATCA observations. Standard data reduction was performed with the miriad software package (Sault, Teuben Wright 1995 ), using PKS B1934−638 for flux and bandpass calibration and PKS B0843−54 for phase calibration. For the H i data, the continuum component was subtracted by fitting a linear baseline through a set of line-free channels in the Fourier domain. All pointings in the mosaic were cleaned together with a joint approach using the miriad task MOSMEM. For the H i data, the solutions converged for all channels. Finally, the clean components were convolved with a 130 arcsec × 130 arcsec and a 100 arcsec × 100 arcsec beam for the line and