adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020MNRAS.494.5636W (fulltext)
larger than the PSF FWHM (orange dashed line). The 3D half-light radii agree with the two observational methods for the bright galaxies. The sizes of faint galaxies show a steeper decline than the observational data, indicating potential systematic errors in observations below the instrumental resolution limits. We examine how well the measured sizes using the three different methods recover the true 3D half-stellar mass radii. Fig. 10 illustrates the ratio of the F115W (rest-UV) sizes to the 3D half-stellar mass radii as a function of stellar mass, with the F115W sizes obtained by the three methods. We have combined sampled from simba -25 and simba -50. Solid green lines represent the median of the ratios, and the shaded regions show 1σ spread in the size ratios. For the growth curve method and 3D half-light radii, we also show results using the no-dust sizes with magenta lines. Figure 10. Similar to Fig. 9 , but showing comparisons of the measured F115W sizes (rest-UV) to the galaxy 3D half-stellar mass radii as a function of stellar mass. Shaded regions represent 1σ spread in the size ratios. The 3D half-light radii traces the half-mass radii very well at z = 6. Due to the finite pixel size and smoothing by the PSF, size measurements in real observations bias the ‘true’ sizes, which we find to be a mass-dependent effect. Focusing first on the right-hand panel of Fig. 10 , the no-dust 3D half-light sizes are smaller than the 3D half-stellar mass radii, but the ratios are roughly constant. This indicates that although more concentrated than mass, light traces mass well in these simulated z = 6 galaxies, so these galaxies can only have weak age gradients. When including dust extinction, the sizes could be a factor of 1.5–3 larger than sizes without dust for galaxies with M * ≳ 10 9 M ⊙ , and also overestimate the half-mass radii by up to a factor of ∼1.5. This suggests that the centres of the galaxies are more dust-attenuated. Indeed, we will show in Section 3.4.3 that the centres of these galaxies are younger but with more dust extinction. Results obtained with the two observational methods are generally similar, as shown in the left-hand and middle panels. The sizes of galaxies with M * 10 8 M ⊙ are overestimated by factors of 1.5–2, owing to the finite resolution of the mock images and smoothing of the PSF. Overall, simba produces galaxy sizes that are in good agreement with current HST observations at z = 6. We also find that the observed sizes are a biased tracer of the true half-stellar mass radii. Moving forward, we will explore predictions for JWST observations. 3.4.2 Rest-optical versus rest-UV sizes Since future JWST observations will be able to explore the z ∼ 6 galaxies in the rest-frame optical wavelengths, we explore how the rest-frame optical sizes compare with the rest-frame UV ones. Fig. 11 shows comparisons between the F444W (rest-optical) sizes and the F115W (rest-UV) sizes, measured using the growth curve method, the Sérsic
Paper: 2020ApJ...893...98M (fulltext)
a systemic velocity for the SNR. The broad range of ejecta space velocities and expansion indices (see Table 1 ) that we measure in our sample may be characteristic for an SNR transitioning from the free-expansion phase to the Sedov–Taylor phase. Measurements of the proper motion at various locations along Kepler’s forward shock by Katsuda et al. ( 2008 ) and Vink ( 2008 ) found expansion indices of 0.47–0.82 and 0.3–0.7, respectively. For remnants nearing the Sedov–Taylor phase, Chevalier ( 1982 ) estimated η = 0.4 for s = 0 and η = 0.67 for s = 2 ambient density power-law solutions. Hence, our ejecta velocity measurements and previous forward shock analyses apparently suggest that the kinematics for some regions in Kepler may be dominated by nearly free expansion, while others are better described by Sedov–Taylor dynamics. New 3D hydrodynamical simulations that focus on Kepler and these high η knots may give some insight into their origin. The knots N2, N5, N1, and N3 have the highest measured space velocities (6100–8700 d 6 km s −1 ), and are all located in the “steep arc” (DeLaney et al. 2002 ) of Kepler’s SNR, a “bar” of bright X-ray emission that runs from east to west, located about halfway between the center of the remnant and the outer edge of the main shell. They are projected close to each other within a small (50′′ × 20′′) area. This proximity, and similarities in their measured Si abundances, space velocity vectors, and expansion indices, suggest that these knots might have originated generally from a “common” layer of the exploding white dwarf. Sato Hughes ( 2017b ) measured properties of another knot (they label “N4”) projected within the steep arc, which exhibited similar properties to N2, N5, N1, and N3. This suggests that ejecta within the steep arc have generally homogeneous kinematic and spectroscopic properties. In the western Ear, we measure high space velocity in region Ear2, v s ∼ 5800 d 6 km s −1 . Such a high velocity may be expected considering the knot’s projected location in the western Ear feature, which protrudes out about 30% beyond the main shell. Interestingly, knot Ear1 has a significantly smaller space velocity, v s ∼ 3300 d 6 km s −1 , even though it is projected very close to the position of Ear2. Knot Ear1 may be interacting with a CSM-dominated feature identified by Burkey et al. ( 2013 ) projected adjacent to it, which could have caused it to significantly decelerate recently. Such an interaction between ejecta and CSM may produce H α emission. We searched for H α emission at the location of Ear1 in the archival Hubble Space Telescope images (with the F656N filter) of Kepler (Sankrit et al. 2016 ). We found a faint wisp centered at Ear1's position, possibly indicating the presence of shocked CSM gas, which would support our conclusion of an ejecta-CSM interaction there. Considering their spatial proximity and similarly high Si abundance, it seems likely that Ear1 and Ear2 were produced very near each
Paper: 2017A&A...606A..25A (fulltext)
at 10 to 20%. However, a much larger bias has been invoked to reconcile cosmological parameters derived from the cosmic microwave background and cluster counts (Planck Collaboration XX 2014 ), although the calibration of the bias by Planck team has been amply discussed (von der Linden 2014 ; Andreon 2014 ; Smith et al. 2016 ). Caustic masses can provide an alternative calibration of the bias of hydrostatic masses. They are almost unused for this purpose (we are only aware of Maughan et al. 2016 ) and we exploit a new method for using this type of masses: if there is a relative bias between caustic and hydrostatic masses then there should be an offset in gas mass derived using the two masses. Throughout this paper, we assume , , and km s Mpc. Results of stochastic computations are given in the form , where and are the posterior mean and standard deviation. The latter also corresponds to 68% intervals because we only summarize posteriors close to Gaussian in this way. Logarithms are in base 10. 2. Sample selection, cluster masses, and X-ray data Sample selection, halo mass derivation, and X-ray data are presented and discussed in Andreon et al. ( 2016 , Paper I), and gas masses are derived in Andreon et al. ( 2017 , Paper II), to which we refer for details. We summarize here the work done. We used a sample of 34 clusters in the very nearby universe (, XUCS, for X-ray Unbiased Cluster Sample) extracted from the C4 catalog (Miller et al. 2005 ) in regions of low Galactic absorption. There is no X-ray selection in our sample, meaning that 1) the probability of inclusion of the cluster in the sample is independent of its X-ray luminosity (or count rate), and 2) no cluster is kept or removed on the basis of its X-ray properties, except for two clusters for which we cannot derive gas masses. The impact of this selection is discussed in Sect. 3.3. We collected the few X-ray observations present in the XMM-Newton or Chandra archives and we observed the remaining clusters with Swift (individual exposure times between 9 and 31 ks), as detailed in Table 1 of Paper I. Swift observations have the advantage of a low X-ray background (Moretti et al. 2009 ), making it extremely useful for sampling a cluster population that includes low surface brightness clusters (Andreon Moretti 2011 ). Caustic masses within 1 , , have been derived following Diaferio Geller ( 1997 ), Diaferio ( 1999 ), and Serra et al. ( 2011 ), then converted into and assuming a Navarro et al. ( 1997 ) profile with concentration . Adopting would change mass estimates by a negligible amount; see Paper I. The median number of members within the caustics is and the interquartile range is . The median mass of the cluster sample, , is and the interquartile range is 0.4 dex. The average mass error is 0.14 dex. Gas masses are derived by projecting a flexible radial profile, fitting its projection to the unbinned X-ray data, and propagating all modeled sources of uncertainties (e.g., spectral
Paper: 2020ApJ...899L..20T (fulltext)
et al. 1993 ; Steiner et al. 2010 ), which is determined by black hole mass and spin. In the case of non-rotating black holes, ISCO is equal to three times the Schwarzschild radius. Thus, from the average innermost radius of the disk, km, the black hole mass M BH is estimated to beThe expected distance–mass relation in i = 0° and 60° are shown in Figure 5 . Figure 5. Observational constrains on the distance–mass diagram. The two solid colored lines represent Equation ( 3 ) when i = 0° (blue) and 60° (cyan). The dashed lines with the same colors denote the error region due to systematic error. The gray curves give the empirical relation that the luminosity after the soft-to-hard transition ( T = 96) is 0.01–0.04 L Edd . The shaded region represents the plausible mass and distance range. In addition, BHBs are known to show similar luminosity dependence of the spectral states. For instance, in the current case of MAXI J1348–630, the flux at the soft-to-hard transition ( T = 91) is ∼10% of the peak flux in the high/soft state ( T = 14); this value is consistent with those of other BHBs discovered by MAXI, such as MAXI J1820+070 (∼12%; Shidatsu et al. 2019 ) and MAXI J1910−057 (∼10%–15%; Nakahira et al. 2014 ). Furthermore, it was pointed out that the soft-to-hard transition typically occurs at 1%–4% of the Eddington luminosity ( L Edd ; Maccarone 2003 ). Namely, the bolometric luminosity soon after the soft-to-hard transition is expected to be 0.01–0.04 L Edd . The gray curves in Figure 5 give the distance–mass relation of L trans / L Edd = 0.01 and 0.04, where we employed the bolometric flux at the soft-to-hard transition ∼1.7 × 10 −8 erg s −1 cm −2 on T = 96 and L Edd = 1.3 × 10 38 ( M/M ⊙ ) erg s −1 . Next, we try to constrain the source distance. The galactic coordinate of the source is ( l, b ) = (309.3, −1.1), which is tangential to the Galactic Scutum–Centaurus arm. If MAXI J1348−630 locates in the Scutum–Centaurus arm, the distance is estimated to be ∼4–8 kpc (e.g., Xu et al. 2018 ). However, the value of N H = 8.6 × 10 21 cm −2 implies that the source is located in front of the arm at ∼3–4 kpc. If we adopt the most likely index L trans / L Edd ≈ 0.02 (Vahdat Motlagh et al. 2019 ), the distance becomes ∼3.8 kpc for the face-on disk. It is consistent with the estimates from optical observations (Charles et al. 2019 ; Russell et al. 2019b ). In order to estimate the black hole mass more precisely, taking account of the relativistic effects, we tried the kerrbb model instead of the diskbb model (Li et al. 2005 ). The kerrbb model gives relations among the spinning parameter a , inclination angle i , and black hole mass M under a given source distance, mass accretion rate, and spectral hardening factor ( κ in Equation ( 2 )). We carried out a joint fit to the MAXI/GSC and Swift/XRT spectra at T = 85 fixing D = 4 kpc and κ = 1.7 for several discrete a and i values, only allowing the black hole mass and mass accretion rate to be free.
Paper: 2020ApJ...896...10B (fulltext)
described in Section 3.2 , 417 out of 47,125 galaxies have AGN-like variability, for an overall variable AGN fraction of 0.9%. In Figure 3 , we show the light-curve standard deviation versus the PTF R -band magnitude for the full sample. In general, the scatter about the median magnitude for a given light curve increases as the magnitude increases, i.e., there is increased scatter in light curves of fainter objects. The sample of low-mass galaxies from the NSAv0 with AGN-like variability is presented in Table 3 . Three examples of low-mass galaxies with AGN-like variability are shown in Figure 4 . Figure 3. Light-curve standard deviation vs. PTF R -band nuclear magnitude. The shaded gray contours show the full population of galaxies from the NSAv0. The blue circles show galaxies we identify as having AGN-like variability. The orange points show the variable AGN from our ancillary samples (described in Section 2.2 ). The typical scatter in a light curve increases toward fainter galaxy nuclei. Figure 4. Three low-mass galaxies with star formation-dominated narrow emission lines and AGN-like variability. These galaxies span from 5 × 10 7 to 2 × 10 9 M ⊙ in stellar mass. The images cutouts (left) are from the DECaLS Legacy Survey browser and are 30′′ on each side. The black circles are the measured PTF R -band magnitude values measured within a 3′′ nuclear aperture. The light curves (right) give the PTF R -band nucleus magnitudes over 5.5 yr. In the light curves, the gray circles are data, the blue line represents the best-fit damped random walk model, and the shaded blue region shows the range of model uncertainties. Table 3 237 Low-mass Galaxies with AGN-like Variability Note. Variability-selected AGNs with M * 10 10 M ⊙ , in order of R.A. NSA ID refers to the ID given in the NSAv0. σ var , σ QSO , and σ notQSO refer to the significance that the given light curve is variable, an AGN, or a false alarm, respectively. Details on these parameters can be found in Section 3.2 . A full version of this table is available in the online version. (This table is available in its entirety in machine-readable form.) Only a portion of this table is shown here to demonstrate its form and content. A machine-readable version of the full table is available. We explore the fraction of variable AGNs as a function of galaxy and light-curve properties. Uncertainties are binomial limits computed following Gehrels ( 1986 ). In Figure 5 , we show the fraction of variability-selected AGNs as a function of host galaxy stellar mass; we find that the AGN fraction increases toward higher stellar masses. In Figure 6 , we show histograms of stellar mass for the full sample and for variable AGNs. The sample of variable AGNs skews toward higher stellar masses; the median stellar mass of the overall sample is , while the median stellar mass of the variability-selected AGN is 8.3 × 10 9 M ⊙ . The pink shaded region in Figures 5 and 6 denote the region , where our sample is
Paper: 2021MNRAS.500.2076G (fulltext)
(top) WC/WN versus metallicity. Blue lines correspond to BPASS binary models and red dashed lines the single models. The black star shows the averaged WC/WN estimation for the Antennae. For comparison, we also show the values for the Magellanic Clouds and the Milky Way taken from Rosslowe Crowther ( 2015 ), shown with open stars. (bottom) WR/O versus metallicity. The upside down arrow represents an upper limit for the Antennae, since we only computed the number of O-type stars in the SSCs with WR stars. Finally, the template-fitting method has been used in several works to classify WR stars (e.g. Hadfield Crowther 2006 , 2007 ; Kehrig et al. 2013 ; Gómez-González et al. 2020 ). However, it is still not clear how representative a template spectrum is for a given subtype. That is, what is the dispersion in the strengths of different lines in the individual spectra used for obtaining the template. An in-depth study of line strengths of WR stars in the Galaxy updated with new astrometric and photometric information from Gaia would be required to address this question. 5 SUMMARY AND CONCLUDING REMARKS We have used VLT MUSE data cubes from the ESO archive to search for WR stars in star-forming complexes of the Antennae galaxies. We reported their number, classification, and distribution. Our results can be summarized as follows. We detected 38 WR SSC complexes with 4053 ± 84 WR stars, out of which 2021 ± 60 are WNL, 2032 ± 59 WC-types. We cannot uncover a plausible presence of WO-type stars given the limited spectral range covered by the observations used for this study. Further observations that covering the VB could confirm or rule out this population, the rarest type of WR stars. However, thanks to the GTC spectrum of WR 1, we can rule out the presence of WO-type stars in this SSC complex, the one with the highest number of WR stars in the Antennae. Galactic WR templates of WNL, WCE, and WCL subtypes were appropriate to classify and quantify the WR stars of each SSC complex, given the metallicity of the Antennae, with an oxygen abundance 12 + log(O/H) ≈ 8.9. We analysed the observed WR blue and red bumps using multiple-component Gaussian fitting in order to recover the ionic transitions responsible for the bumps and report their main parameters. In all cases, the recovered ions are consistent with those expected for the inferred subtype using the templates. Avoiding any nebular contamination, we evaluated the contribution for each broad emission line to the entire luminosity of the BB and the RB of the entire WR sample. We do not find any evidence of the nebular He ii emission line. This is explained by theoretical models, as discussed, given the high metallicity of the Antennae. We estimated the main physical properties of the WR nebular environments, oxygen abundances, and presented information regarding their kinematics. We derive a global WC/WN ratio ∼1, which, according to predictions of the current BPASS models is consistent with the
Paper: 2016A&A...587A..32M (fulltext)
Larson core. We focus on niMHD calculations; iMHD calculations are discussed in Sect 5 . Fig. 8 Visualisation of the disk structures. Top row : panels a) and b) show side and top views, respectively, of the disk density (orange), with the velocity vectors superimposed for . Panels c) and d) are the same side and top views, but showing the plasma parameter (colour map), over which we plot the magnetic field direction for the side view c) and the velocity vectors for the top view d) . Bottom row : same as for the top row , but for the simulation. Each row has a different spatial scale. 4.1. Aligned case Conservation of angular momentum during the collapse yields a very high specific angular momentum close to the protostar, which eventually leads to the formation of rotationally dominated structures with a strong toroidal velocity component and in some cases to genuine disks with Keplerian velocity profiles. To define a “disk” in the simulations, we used the criteria defined in Joos et al. (2013) . Structures in which the magnetic pressure either dominates or is at least not negligible, however, may satisfy these criteria while being very different from the flat rotationally supported structures characteristic of Keplerian disks. It is important to clearly identify and distinguish these two types of structures, since the existence of flat disks around Class-0 objects remains a subject of heated debate, and this issue is addressed below. Following Joos et al. (2013) , a piece of fluid belongs to the disk if it fulfils all the following constraints (using ): it is close to hydrostatic equilibrium:; it has strong rotational support: ; its rotational support is stronger than the thermal support: ; it has a high density: . In our fiducial aligned case with , we do observe the formation of a disk according to the above criteria that strongly resembles a Keplerian disk. Its characteristic properties are given in Table 3 and the results of the simulations are shown in Fig. 8 (top row). The disk is well defined in density, beginning just at the edge of the core and exhibiting sharp edges at its periphery. The breaking of symmetry observed in the top view (Fig. 8 b) is due to the evolution over several orbital periods at the core radius, with small numerical errors leading to a bar-like instability that evolves into two spiral arms. The disk then grows to a radius of au and is flat. The velocity profile in the disk is displayed Fig. 9 and is very close to a Keplerian one, with an estimated mass for the central object of compared to (Table 3 ) (see Appendix C for an important remark about assuming a Keplerian profile to estimate the mass of the central object). Table 3 Properties of the disks that are formed in the non-ideal MHD simulations. The evolution of the disk for our two values of magnetisation is displayed in Fig. 10 (solid lines for , dashed lines for ). In niMHD, the pinching of the field lines the midplane is greatly reduced and the radial
Paper: 2018A&A...620A.189O (fulltext)
software based on the analytical equation applied to spot modeling by Budding (1997) , assuming circular spots. Fixed parameters were the effective wavelengths of the filters used, linear limb darkening coefficients ( Howarth 2011 ) taking into account the stellar parameters, and the unspotted brightness in all bandpasses. We note that there is no way to deduce the true unspotted brightness of an active star showing the usual rotational and long-term light variations. On the other hand, one would expect a higher brightness level and smaller rotational variation with fewer and fewer spots. Therefore, we took the observed maximum magnitude as the unspotted reference brightness level. The rest of the activity is supposed to be distributed evenly and/or remain on the poles. We note that we have observed only two rotations of the star in a standard photometric system, and the archival and K2 data were obtained in different, non-standard bandpasses (except ASAS which has a V magnitude bandpass), and therefore cannot be compared quantitatively. We modeled in parallel the B − V , V − R C , V − I C color observations, assuming that there are just two circular starspots. The parallel modeling of each of these colors results in spot coordinates (longitudes, latitudes), sizes, and a single spot temperature. For EPIC 211759736 we have a high rotational inclination angle (81.85°), and thus the two stellar hemispheres are nearly interchangeable (supposing that the rotational axis is perpendicular to the orbit, the star is seen nearly edge-on), that is, approximately invariant under inversion. Therefore, in the course of the modeling, the spot latitudes were kept fixed at the equator; generally, it is not possible to obtain reliable spot latitudes from photometric data. In this way we had five free parameters to fit for: two longitudes and sizes, and the spot temperature. The results show that in early 2018 there were two cool spots (active regions) on the stellar surface, one facing the secondary component and the other on the opposite side, covering altogether about 10% of the total stellar surface, with a temperature of 3960 ± 300 K, or about 800 K below the surface temperature of 4750 K. The individual results of the spot temperature modeling from the three different color indices are as follows: B − V : 3968 ± 305, V − R C : 3936 ± 364, and V − I C : 3978 ± 215. Although the spot temperatures from the three color indices have substantial uncertainties, their values are remarkably close to each other. The resulting spot longitudes and sizes from the three color indices are within their mutual 1- σ uncertainties. The four-color light curves and the fitted color indices are plotted in Fig. 6 . (We note that experiments allowing the spots’ latitudes to also be free parameters gave essentially the same results for the longitudes, sizes, and temperature, but with much higher uncertainty due to the error propagation.) By contrast, the average stellar