adsabs/WIESP2022-NER huggingface dataset:
Paper: 2016A&A...587A.115M (fulltext)
be expected at a distance of 5000 au or more. Sana et al. (2014) show that at a separation of 8, the cumulative multiplicity frequency reaches 91%. It would correspond to a distance of 16 000 au at a typical distance of 2 kpc. For deprojected distances above 10 000 au the neighbour star is probably not bound to the main star ( Sana et al. 2014 , actually indicate that at large separation only the brightest companions are bound to the main star). The maximum projected separation in arcsec corresponding to the linear separation of 10 000 au in the image is also provided (see Table 5 ). The results are indicated in Table 5 , Cols. 3 to 5. Stars P4; HD 168625b; HD 168607b; MWC314b,c; MWC930c; WR102e; HD 152234b; and HD 164794b,c,d are separated from the main component by less than 10 000 au. This test eliminates 50% of the potential companions visible in the images as bound in a binary system. According to the study by Sana et al. (2014) among massive stars, it is not rare to find binaries with a magnitude difference in K or Lp of 88.5. The limiting magnitude would ensure that almost all potential companions are caught. In addition, they also used NACO to find binaries in O stars, as this threshold would be adequate for LBV stars and corresponds to a late B-type star (using the models from Schaller et al. 1992 ). The results are provided in Table 5 , Col. 6, while we give the angular projected separation corresponding to 10 000 au as an indication in Col. 5. This test allows us to discard 30% of the potential companions visible in the images as bound in a binary system. The results are discussed in Sect. 3.4 . 3.4. Binarity assessment and discussion To summarize, a companion is bound to the main star if it complies with the following criteria: The deprojected/probable separation must be smaller than10 000 au. This value is given inTable 5 , Col. 4. The projectedseparation (from Col. 2) is corrected with the mostprobable angle of projection to provide a more realistic angularseparation (Col. 3) converted to au inCol. 4. The difference in magnitudes must be mag 8.5 (Table 5 , Col. 6). The chance projection probability (field) must be smaller than 10%, (field) smaller than the cut-off probability, or (field) smaller than 5%. In Col. 7 we indicate the chance projection probability for the potential companion considering the stars counted in the NACO images (except for WR102ka for which SINFONI image is used). In Col. 8, we provide the chance projection probability for the potential companion considering the stars counted in the results of simulations with the trilegal code ( Girardi et al. 2005 ). They differ by the magnitude ranges used: all trilegal stars, stars with a maximum difference of 2 and 1. In Col. 9, the chance projection probability following the formula of Correia et al. (2006) is provided and the scaled cut-off probability is given in Col. 10. In Col. 11, the chance projection probabilities (field), (field), and (field) respectively
Paper: 2016ApJ...824...86S (fulltext)
its multiple imaging cross section at the redshift of the LAE in our sample will be comparable to or larger than the diameter of a BOSS fiber. As a result, the multiply imaged source search region associated with each CMASS galaxy is essentially the size of the fiber. This is very different from general studies of lens statistics (e.g.,Kochanek 1993a , 1993b , 1996 ; Maoz Rix 1993 ; Jaunsen et al. 1995 ; King Browne 1996 ; Möller et al. 2007 ) which must account for variations in the multiple imaging cross section with lens redshift, lens mass, and source redshift. Given this fact, our estimate of the magnification bias can be checked observationally by comparing the incidence of LAE detections in BOSS galaxy spectroscopic fibers to their incidence within the blank-sky fibers of the survey. To explore this, we rerun the full candidate selection procedure on BOSS sky fibers and obtain 9 candidates with - σ detection significance. This number can be compared to the 187 candidates found to this same significance threshold in the galaxy-fiber sample. Since there are approximately six times more galaxy fibers than sky fibers in the BOSS survey, we find that our high- z LAE detections occur with a frequency times higher in the galaxy fibers than in the sky fibers. This is consistent with the boost factor expected based on our simple magnification-bias calculation above: the higher relative incidence of LAE detections in the galaxy fibers thus suggests that the galaxy-fiber detections are highly magnified by strong lensing. We stress, however, that the close quantitative agreement between the predicted and observed detection-rate boost of galaxy fibers relative to sky fibers must be regarded as coincidental considering the rough approximations involved. 5. DISCUSSION We first emphasize that our spectroscopic selection technique is powerful and generic, and can be easily applied to a variety of scientific considerations with appropriate modifications. For instance, by changing the single-line detection strategy to detections for at least two of the lines [O ii ], [O iii ] 4959, 5007, H α , and H β at a common redshift, one can pick out lens candidates with star-forming galaxies as the background sources. That is exactly the approach adopted in the successful SLACS, BELLS, and S4TM surveys (Bolton et al. 2006a ; Brownstein et al. 2012 ; Shu et al. 2015 ). One can also specifically search for lensed quasars by tuning the selection criteria to the presence of more than one prominent quasar emission lines such as Ly α , C iv , C iii ], and Mg ii . Interested readers are encouraged to adopt the spectroscopic selection technique according to their own research interests. Although the size of the sample of galaxy–LAE lenses from our initial HST program seems to be relatively modest when compared to other galaxy-scale strong-lens samples, there are another 166 galaxy–LAE lens candidates remaining in the parent sample. We applied very stringent
Paper: 2021MNRAS.503.1940H (fulltext)
which starts forming BHs in galaxies with . While modelling BH formation in low-mass galaxies is crucial to understand BH formation in the high-redshift Universe, as well as to understand the current populations of BHs in local dwarf galaxies, the regime of low-mass galaxies is barely resolved in such large-scale simulations of on a side (but see Habouzit et al. 2017 , and references therein for BH formation in low-mass galaxies from high redshift to low redshift). All the other simulations employ lower seeding masses than the TNG simulations. In Horizon-AGN fewer BHs of are present at z = 0 than at higher redshift. This is because BH formation stops at z = 1.5 by design in this simulation. In simulations, the initial mass of BHs was shown to affect the low-mass end of the M BH − M ⋆ diagram, and the overall normalization of the scaling relation (e.g. Bower et al. 2017 ). For higher stellar masses, the relations for different seeding masses converge to the same relation (whose normalization depends on the BH accretion efficiency parameter) as a result of self-regulation. The seed mass is important for the simulations using the Bondi accretion model (e.g. Illustris, TNG, Horizon-AGN, EAGLE), for which there is a degeneracy between the seeding mass and the boost factor to produce the same normalization of the M BH − M ⋆ mean relation. In simulations using a torque accretion model (SIMBA), the accretion rate on to the BHs does not strongly depend on their masses (, contrary to the Bondi model ). Therefore, the low-mass BHs can grow more efficiently than in the Bondi model, and they converge into the M BH − M ⋆ mean relation regardless of their seed mass (Anglés-Alcázar, Özel Davé 2013 ; Anglés-Alcázar et al. 2015 ). We point here again that the seeding in large-scale cosmological simulations is often not physically motivated (e.g. fixed BH mass, threshold in galaxy, or halo mass to form a BH), but instead parameters of the seeding models are chosen to reproduce the local scaling relation. 3.3.2 On the growth of BHs Getting the BHs on the main scaling relation depends both on the seeding BH mass and BH accretion rate. The parametrizations of these models are degenerate. Therefore, different choices allow the BH population to get on to the same empirical scaling relation. For example, the TNG model seeds BHs with a higher initial BH mass than the Illustris model (by one order of magnitude) but also does not include any boost factor in the accretion model (while Illustris does include a boost factor of 100). The colour code of Fig. 3 provides information on the accretion rates on to the BHs. At z ∼ 2, it is clear that the accretion properties are very different among the different simulations. For example, BHs of embedded in galaxies of have high bolometric luminosities of (limit commonly employed to define an AGN) in TNG, Horizon-AGN, and SIMBA, while a large fraction of these BHs in Illustris and EAGLE have much lower luminosities.
Paper: 2018ApJ...864..166D (fulltext)
In Figure 13 we also compare the number density of our red nondusty massive galaxies with the number density of passive galaxies selected in the literature for the same (or a very similar) stellar mass cut (Mancini et al. 2009 ; Muzzin et al. 2013 ; Straatman et al. 2014 ; Tomczak et al. 2014 ; Davidzon et al. 2017 ; Merlin et al. 2018 ). These passive galaxy selections are based on rest-frame colors, and their number densities are directly quoted by the authors, or we have obtained them by integrating their corresponding GSMF. 10 10 In the latter case we integrate the Schechter fits to the GSMF. The uncertainties on the number densities reflect the uncertainties on the Schechter parameters. This method was implemented by Muzzin et al. ( 2013 ), Tomczak et al. ( 2014 ), and Davidzon et al. ( 2017 ). Our comparison shows that the number density of our red nondusty galaxies is significantly higher than the number density of passive galaxies derived from Muzzin et al. ( 2013 ) and Davidzon et al. ( 2017 ) GSMFs. These differences are mainly produced by the shallower depths of the data sets used by these authors with respect to our own. In the case of the comparison with Davidzon et al. ( 2017 ), there is the effect of (a) their choice of dust extinction law and (b) their specific criterion to select passive galaxies, which is color based, but different from that applied by other authors. Davidzon et al. ( 2017 ) also correct for Eddington bias (Eddington 1913 ), which causes a drop in number densities at the massive end of the GSMF. Our red nondusty galaxy number densities are broadly consistent with the value derived by Merlin et al. ( 2018 ) and the upper limit from Mancini et al. ( 2009 ). They are also in excellent agreement with the number densities derived by Straatman et al. ( 2015 ) at z ∼ 2–3, although this might be somewhat fortuitous, given their very different methodology to select quiescent galaxies. At higher redshifts, instead, our results indicate a much faster decline in the number density of passive galaxies than that obtained by these authors. This difference does not seem to be the product of the different methodologies (note that we would select less rather than more passive galaxies using the quiescent wedge of the UVJ diagram, according to Figure 12 ). The observed differences could in part be the result of cosmic variance, as Straatman et al. ( 2014 ) analyzed images over an area ∼6.5 times smaller than that considered here. Figure 14 shows the fractions of our different populations (dusty, nondusty/blue, and nondusty/red, i.e., quiescent) among all massive () galaxies, versus redshift. We compare these fractions to those obtained in the literature. In these other works, quiescent galaxies have been selected using the UVJ color–color diagram wedge. In addition, Martis et al. ( 2016 ) have also determined regions to segregate dusty and nondusty star-forming galaxies on this color–color plane. Figure 14. Comparison of our
Paper: 2016MNRAS.463L.122H (fulltext)
that gravitational instabilities drive the formation of molecular clouds within the Galactic Centre (hereafter, GC) gas stream. 2 DATA This Letter makes use of data from the Mopra CMZ survey, first published by Jones et al. ( 2012 ). We focus on the J = 1 → 0 transition of N 2 H + (the rest frequency of the main J, F 1 , F = 1, 2, 3 → 0, 1, 2 hyperfine component is ∼93.174 GHz), the emission from which is both bright and contiguous over the region of interest (Henshaw et al. 2016 ). The presented data cubes have an effective spatial resolution of ∼60 arcsec (corresponding to ∼2.4 pc), a pixel size of 30 arcsec × 30 arcsec (1.2 pc × 1.2 pc), a spectral resolution of 2 km s −1 , and the typical rms noise level is ∼0.01–0.02 K. Fig. 1 is a map of the inner CMZ. The colour-scale represents the Herschel -derived column density (Battersby et al., in preparation). The present investigation focuses on the region −07 l 00, −01 b 01, which is indicated by the black dashed rectangle. The black contours highlight integrated emission of N 2 H + . The data enclosed within the black dashed rectangle were analysed using the Semi-automated multi-COmponent Universal Spectral-line fitting Engine ( scouse 1 ; Henshaw et al. 2016 ). 3 RESULTS Panel A of Fig. 2 provides a close-up view of the Herschel -derived column density map (Fig. 1 ). It focuses on the location of the upstream velocity field (depicted by the blue dots). To identify the main structures we use dendrograms (Rosolowsky et al. 2008 ). Each contour in panel A represents a leaf, the highest level in the dendrogram hierarchy. 2 Red contours depict structures that are spatially coincident with the velocity pattern. The column density map of Battersby et al. (in preparation) is used to derive physical properties of each leaf, including the peak column density ( N ), total mass ( M ), equivalent number density and corresponding equivalent free-fall time ( n eq and t ff,eq ). 3 These properties are presented in Table 1 . Panel B of Fig. 2 showcases the velocity pattern first identified by Henshaw et al. ( 2016 ), which appears as a corrugation in { l , v LSR } space. Each data point refers to the location ( l ) and centroid velocity ( v 0 ) of a Gaussian component extracted using scouse . We corroborated these values using the hyperfine structure (HFS) fitting functionality available in class . 4 This method accounts for the effects of opacity on the line-profiles (whereas Gaussian fitting does not), leading to a more accurate measure of the velocity dispersion and line centroid where the opacity is high. We find that the velocity dispersions derived using scouse are systematically larger than those obtained using class (). However, the derived centroid velocities are very similar, km s −1 . Both the scouse and class derived kinematic properties are included in Table 1 . Figure 2. Galactic longitude versus: (A) Galactic latitude. A column density map of the CMZ focusing on the region of interest
Paper: 2020MNRAS.491..708L (fulltext)
be measured, then this would provide constraints on the field strength and distribution. We also explored the possibility of non-WKBJ effects relevant to mixed modes of low degree. Our results demonstrate that the form of the quantization condition remains very similar to that in the case where WKBJ is applicable throughout the star. However, in our more general expression, an additional offset g appears, given in equation( 90 ). This is unrelated to magnetic effects and has its origins in the non-WKBJ nature of strongly coupled cavities. Measurements of this may provide information about the structure of the evanescent zone and base of the convective envelope. The effects of rotation have not yet been incorporated and would be the subject of future work. APPENDIX A: TREATMENT OF THE UPPER LAYERS In this appendix, we discuss the convective envelope and evanescent zone (regions P and E), for the purposes of connecting the solutions in these regions to those in the g-mode cavity (treated in Section 4 ), and thereby obtaining the mixed-mode quantization condition. A1 Region P Here we still shall assume N 2 0, making the region isentropic. Equations( 15 )( 16 ) then combine to give where . Making a change of variable , we obtain the DE where The first term in the expression for is expected to dominate, since this corresponds to the squared acoustic wavenumber, which greatly exceeds 1/ r for typical of stochastically driven oscillations in the outer parts of the convection zone. Retaining only the first term in when considering amplitude factors in the WKBJ approximation, the WKBJ solution to ( A2 ) is corresponding to where C 3 and are arbitrary constants. Here r t is the lower turning point where vanishes. We shall assume for simplicity that this exceeds r 0 , where N 2 drops to zero from a positive value further in. Otherwise, the lower limit of the integral should be replaced by r 0 , as ( A1 ) does not apply where N 2 0. The constants C 3 and are in the first instance determined by the requirement that the solution( A4 ) is a continuation of the one that is specified just interior to r e . Asserting the continuity of the solutions multiplying C 1 and C 2 in equation( 34 ), we write The amplitude factors A 1 and A 2 and the phases 1 and 2 in general have to be determined from numerical integrations if the WKBJ approach cannot be used throughout region E, as will be the case for small . A1.1 Incorporating a surface boundary condition The solution representing a mode of oscillation must also satisfy a boundary condition at the surface. Following the usual procedure when constructing WKBJ solutions, we assume that this condition can be met with a solution of the form ( A5 ) by specifying the phase . The value of 0 is determined by the surface boundary condition. Given the above continuations, it is readily found that the condition that the linear combination occurring in equation( 34 ) leads to the correct phase condition specified above in this
Paper: 2021MNRAS.500.4307F (fulltext)
plot the branching ratio as a function of the NSC density (, β = 2.3); in the bottom panel, we show as a function of the the slope of the BH mass function ( and ). The general trend with a hard discussed previously is in part smeared out by the fact that we sample the binary semimajor axis in the range ( a GW , a hard ), rather than having it fixed to a hard . Moreover, BHs do not have a fixed mass, as in Mod1 and Mod2 in equal-mass binaries, but their masses are sampled from a distribution. Additionally, we find that SBH–SBH binaries produce the largest branching ratio, while IMBH–IMBH lead to the lowest one. This can again be ascribed to the fact that hard binaries of two SBHs have typically smaller semimajor axes than binaries comprised of two IMBHs. As a consequence, stars that undergo a resonant encounter with an SBH–SBH binary are more likely to pass within the tidal radius of one of the two BHs and get tidally disrupted. Branching ratios of binary IMBH–SBHs are in between the SBH–SBH and IMBH–SBH cases. Figure 2. Top panel: branching ratio as a function of the NSC mass (, β = 2.3) for SBH–SBH (orange), IMBH–SBH (red), and IMBH–IMBH (green). Central panel: branching ratio as a function of the NSC density (, β = 2.3) for SBH–SBH (orange), IMBH–SBH (red), and IMBH–IMBH (green). Bottom panel: branching ratio as a function of the the slope of the BH mass function (, ) for SBH–SBH (orange), IMBH–SBH (red), and IMBH–IMBH (green). 3.3 Orbital properties of binaries that exhibit tidal disruption events The semimajor axis of the binaries that undergo a TDE depends on the host NSC mass and density, and on the slope of the BH mass function β. We illustrate in Fig. 3 the distribution of semimajor axes of the SBH–SBH binaries that undergo a TDE. In the top panel, we show the case for and different values of the NSC mass (Mod3), while in the bottom panel, we plot the case for and different values of the NSC density (Mod4). The slope of the SBH mass function is fixed at β = 2.3. We find that the NSC mass has only a modest effect in shaping the semimajor axis of the binaries that undergo a TDE, unlike the host NSC density. The reason for this trend can be explained considering that binaries have orbital semimajor axis in the range ( a GW , a hard ), where Larger densities correspond to smaller values of a GW , thus a wider range of possible binary semimajor axes, while the NSC mass does not have a significant effect. Figure 3. Distribution of semimajor axis ( a ) of the SBH–SBH binaries that undergo a TDE. Top panel: and different values of the NSC mass; and bottom panel: and different values of the NSC density. The slope of the SBH mass function is fixed to β = 2.3. Fig. 4 shows the distribution of semimajor axes ( a ) of the SBH–SBH binaries (top) and masses ( m SBH ) of the SBHs (bottom) that undergo a TDE, for different values of the slope of the SBH mass function. The mass and density of the NSC are fixed to and , respectively. We find that the
Paper: 2021MNRAS.501.3916G (fulltext)
best fit and subsequently adjusting the result. The best-fit is obtained for T eff = 10, 140 K, and log g = 8.10. Statistical uncertainties are negligible due to the high SNR of the optical spectrum. Subsequently, we performed a photometric fit of the available APASS and Gaia DR2 photometry (Table 2 ), in which we used the Gaia DR2 parallax and the white dwarf mass–radius relation (Fontaine, Brassard Bergeron 2001 ) as additional priors. We have not used VHS photometry to be sure that the IR excess has no effect in the fit. The photometric fit delivers a 0.1 dex-lower surface gravity. Hence, we iterated between spectroscopic (log g fixed) and photometric ( fixed) fits, until both methods converged to a final solution. The difference between the initial spectroscopic fit and the final result is adopted as the error estimate, resulting in T eff = 10, 090 ± 50 K and log g = 8.00 ± 0.13. The interstellar reddening obtained as a result of our fitting procedure is negligible, and of the order of 0.01 mag. We measured the radial velocities of the strongest metal lines via fitting Gaussian models and estimated the uncertainty as the scatter between the measurements of the different lines. For the 2011 spectrum, we obtained an average radial velocity of = 44 ± 5 km s −1 and for the 2019 spectrum, = 47 ± 8 km s −1 . The individual radial velocities for the different lines are presented in table 6. The atmospheric parameters of WDJ1814−7354 correspond to a white dwarf mass of M ⊙ and a cooling age of Gyr. Using the Cummings et al. ( 2018 ) initial-to-final-mass relation (IFMR), we infer a progenitor mass of M ⊙ . To estimate the progenitor age, we used the evolutionary tracks of Choi et al. ( 2016 ). For a progenitor mass of 1.3 M ⊙ (late F-type star), the progenitor age is τ MS = 5.2 Gyr, and the total age of the white dwarf would be τ MS + τ cool = 5.8 Gyr. The large systematic uncertainty implied by the empirical IFMR translates into a large uncertainty on the progenitor age. For the upper limit of the progenitor mass of 1.8 M ⊙ (a late A-type star), the progenitor age would be τ MS = 3.4 Gyr, so the lower limit on the total age of the white dwarf is 4 Gyr. The upper limit on the total age is unconstrained, because the lower limit of the progenitor mass is 0.9 M ⊙ (an early K dwarf star), whose lifetime could be comparable or longer than the age of the Universe. 4.2 Composition of the accreted material and diffusion time-scales The abundances of the three detected elements (Mg, Ca, and Fe) are given in Table 7 . The listed abundances and uncertainties are the averages and the standard deviations inferred from the modelling of the strongest lines for each detected element: lines at 3933.66, 3968.47, 8498.02, 8542.09, 8662.14 (Ca ii ), and 4226.73 Å (Ca i ) for Calcium; lines at 4481.13 (Mg ii ), 3838.29, 5167.32, 5172.68, and 5183.69 Å (Mg i ) for Magnesium; and lines at 3609, 3619, 3632, 3720, 3738, 3750, 3759, 3816, 3821, and 3826 Å for iron (Fe i ).