adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019MNRAS.484.4601S (fulltext)
runs with observational data. The Ly α LFs are obtained by convolving the L Ly α – M h relation measured in the simulations with the theoretical haloe mass function (MF) from Sheth Tormen ( 2002 ). The shaded region indicates the 1σ uncertainty due to the average scatter in the L Ly α – M h relation. The data points show recent estimates of the Ly α LF at = 6.6 from Ouchi et al. ( 2010 ), Matthee et al. ( 2015 ), and Konno et al. ( 2018 ). The Ly α LFs in the CW and VW models reproduce well the shape of the best Schechter fit, both the faint-end slope and the exponential cutoff at the bright-end, while the LF in the NW model appears to be steeper than the Schechter fit. This means that our radiative transfer calculations were able to properly capture the correct scaling between L Ly α and M h in the CW and VW models. However, the LFs in the VW and NW models deviate from the observed one over the entire luminosity range of the data. We speculate that this may be because we do not account for dust in our radiative transfer calculations. While we argue in Appendix A that dust is probably not important for the Ly α line transfer through the IGM in our simulations, it is likely to be an important factor in reducing the escape fraction, , of Lyα photons out of galaxies, especially massive ones. This is also supported by the fact that galaxies in the CW model are the most efficient at ejecting metals from their ISM into their surrounding IGM (as discussed in S16 ), which means that these galaxies should be the least affected by our omission of the dust. This simple explanation is consistent with the fact that the LF in the CW model has the closest match with the observed LF. A quick way to assess the effect of dust on ISM scales in our radiative transfer calculations is by estimating the value of the Ly α escape fraction, , required to match the Lyα LFs of our models with the observed one. For simplicity, here we assume that is independent of haloe mass. Comparing the Ly α LFs obtained in our models with the observed LF shown in Fig. 8 , we find that , and 0.39 for the CW, VW, and NW models, respectively. These values are broadly consistent with the ones measured in detailed Ly α radiative transfer calculations on sub-ISM scales with dust (see e.g. the recent study by Smith et al. 2019 ). 4.2 Comparison with an observed galaxy overdensity at <inline-formula><tex-math id="TM0145" notation="LaTeX"></tex-math></inline-formula> = 7 Recent deep spectroscopic follow-up observations of colour-selected galaxies in the Bremer Deep Field (BDF) have revealed LAEs associated with a galaxy overdensity at ∼ 7 (Castellano et al. 2016 , 2018 , hereafter C18 ). These observations are ideal to compare the results of our CR simulations with real data. The galaxy sample presented in C18 corresponds to 16 colour-selected galaxy candidates previously observed with HST and believed to be associated with a galaxy overdensity at ∼ 7. Out of these 16 objects, only 3 of
Paper: 2020MNRAS.493.1034N (fulltext)
expects stalled bubbles (and consequently, a size distribution with slope ≈−2.7) in the presence of cooling, or if this depends on the ambient pressure. In order to answer the question, we have run our simulations for two different pressures. Fig. 12 shows the scatter plot of bubbles (similar to Figs 6 and 8 for our fiducial ISM pressure) for two different values of ISM pressures, at 30 Myr. Essentially, we have decreased the non-thermal contribution in the ambient pressure, by reducing the equivalent temperature from 4 × 10 4 to 2 × 10 4 K (left-hand panel) and then to 10 4 K (right-hand panel). Comparing with Figs 6 (adiabatic case) and 8 , we find that the bubbles in these two cases are not dominated by stalled bubbles, as was the case for Fig. 8 . For the left-hand panel, we find that bubbles with L ∼ 5 × 10 36 erg s −1 stall after a time-scale of ∼7 Myr, and bubbles bigger than that at latter times. When the pressure is further lowered, in the right-hand panel, all bubbles keep growing until 30 Myr, and the circumstance is similar to Fig. 6 . We recall that, in these cases of domination by growing bubbles, the size distribution is likely to be steeper than 1 − 2β, as we confirm below. Figure 12. Scatter plot for bubbles for ambient pressure p 0 = 1.38 × 10 −12 dyne cm −2 (left), and 6.9 × 10 −13 dyne cm −2 (right), at 30 Myr. We show in Fig. 13 the size distribution of the bubbles for these values of pressure at two different times after the onset of star formation (10 and 30 Myr). In general, we find that lowering pressure steepens the size distribution, by allowing low -luminosity bubbles to grow to larger sizes. This trend is shown in Fig. 14 , for two different epochs, 10 and 30 Myr. At lower pressures, there is an evolution of the slope between these time-scales, since bubbles keep growing in low ambient pressure, compared to high-pressure environments. Figure 13. Size distribution of bubbles for ambient pressure p 0 = 2.76 × 10 −12 dyne cm −2 (blue), 1.38 × 10 −12 dyne cm −2 (red), and 6.9 × 10 −13 dynes cm −2 (green), at 10 and 30 Myr. Figure 14. The slope of size distribution as a function of ISM pressure, for two different epochs, 10 Myr (green) and 30 Myr (red). The distributions at lower (non-thermal) pressures also show a positive part at small sizes, in addition to the falling numbers at large sizes as we have seen earlier. The rising part comes from growing young bubbles whose age is smaller than the stalling time of the bubble with the lowest luminosity. This generates a peak in the distribution. The peak depends upon the radius of the stalled bubble with the lowest luminosity at the time being observed. Fig. 13 shows that at higher pressures the peak radius is independent of time but starts varying with time as soon as we start decreasing the pressure. This occurs due to the fact that at higher pressures (such as, at 2.76 × 10 −12 dyne cm −2 ), the bubbles with low luminosities stall much before 10 Myr hence the peak
Paper: 2016MNRAS.457.4507H (fulltext)
to a predicted final binding energy equal to E bin ∼ 2.4 E bin,0 in very good agreement with what shown in Fig. 7 . 3.3 Radial variation of the binary fraction Fig. 8 shows the radial profiles of the binary fraction (defined as the ratio of the number of binaries for each population to the total number of binaries and single stars in the cluster) for all binaries, for FG binaries, and for SG binaries. The low ionization rate of the compact binaries we are considering, facilitates the central accumulation of binaries segregating from the outer regions and results into an increasing binary fraction in the cluster inner regions. It is interesting to note that the evolved binary fraction radial profiles are in general qualitative agreement with those found in the recent observational study of Milone et al. ( 2016 ). Figure 8. Time evolution of the radial profiles of the binary fraction (defined as the ratio of the number of binaries to the total number of binaries and single stars) for the model MPr5f1x3-800. Each profile is calculated by combining three snapshots around the time indicated in the legend. Top, middle, and bottom panels show, respectively, the profiles for all the binaries, for FG binaries, and for SG binaries. The horizontal dashed line in the top panel shows the initial binary fraction. The three panels of Fig. 9 show the cumulative and the differential radial profiles of SG-to-FG binary ratio and the differential SG-to-FG ratio for single stars. The interplay among ionization, ejection and segregation towards the inner regions results into a progressive decrease of the SG-to-FG binary number ratio in the cluster inner regions. The global SG-to-FG binary number ratio decreases with time due to the dominant effect of the preferential ionization or ejection of SG binaries; it is interesting to note that this is opposite to what happens to the global SG-to-FG number ratio for single stars which increases with time because for single stars the preferential evaporation of FG stars (see also Vesperini et al. 2013 ) drives the evolution of this number ratio. Figure 9. Time evolution of the radial profile of the cumulative number ratio of SG to FG binaries (top panel), the differential SG-to-FG binary number ratio (middle panel), and the differential number ratio of SG to FG single stars (bottom panel) for the model MPr5f1x3-800. Lines with different colour and style correspond to the different times indicated in the legend, and the horizontal lines show the global SG-to-FG ratio for binaries (middle panel) and single stars (bottom panel). (Each profile has been calculated using three combined snapshots around the times indicated in the legend.) As shown in Fig. 9 , the SG-to-FG binary number ratio depends on the distance from the cluster centre. It is important to identify the radius R glob , where the local SG-to-FG ratio is equal to the global value of this ratio. In paper I , we have already shown that R glob depends on the initial
Paper: 2019A&A...625A..51L (fulltext)
in Sect. 3 are indicated as blue circles filled with gray, while values for the average stacks in Table A.1 are indicated as open blue circles. The correlation between EW(CIII]) and EW(Ly α ) appears to be very strong ( r P = 0.99, r S = 1) and hides the wide range of properties of CIII] vs. Ly α . As a comparison with the literature, we plot the observed values from the different Ly α samples of Shapley et al. (2003) (green stars, average for Ly α samples), Rigby et al. (2015) (purple diamonds), and Stark et al. (2014 , 2015) (orange squares). This behavior is somewhat expected as the radiative transfer of Ly α or CIII] photons is markedly different. As a striking example we point to object 520391642 (second from the top in Fig. 1 ), which shows strong CIII] (EW > 20), as well as CIV, HeII, or NV, but only weak Ly α emission. Our results compare well with the literature when the large spread of the relation between EW(CIII]) and EW(Ly α ), the selection, and the size of different samples is taken into account. The average properties from stacks show that our results agree well with those of Shapley et al. (2003) . VUDS galaxies span a broad range of properties, including galaxies with and without Ly α in emission, expanding from smaller and possibly biased samples for which a stronger correlation between Ly α and CIII] is reported ( Stark et al. 2014 , 2015 ; Rigby et al. 2015 ; Guaita et al. 2017 ). We find that the chance of emitting CIII] and CIV at an appreciable level (EW > 3 Å) for galaxies with EW(Ly α ) > 25 (the broadly used definition of a LAE) is 42% and 20%, respectively. For galaxies that emit both Ly α and CIII] at these levels, the median EW(CIII]) is 7.7 Å, compared to a median EW(CIV) of 3.4 Å for the average LAE emitting CIV. The overall weak correlation that is observed between CIII] and Ly α emission strength for individual galaxies is in line with findings from photoionization models (e.g., Jaskot Ravindranath 2016 ; Zackrisson et al. 2017 ; Nakajima et al. 2018 ) when a broad range of conditions such as ionizing fields, geometry, and dust opacity is taken into account. 5. Distribution of EW(CIII]) To answer the question of how frequently CIII] emission is observed in UV-selected galaxies, we seek to establish the fraction of CIII] emitters in the VUDS sample as a function of EW(CIII]). While it is straightforward to identify strong CIII] emitters, it is more difficult to separate weak emitters from noise. We therefore proceed with a statistical analysis of the distribution of CIII] emission for the 3899 VUDS galaxies with 2 z 3.8. If CIII] emission is absent and in the presence of noise, the line measurement resulting from the process presented in Sect. 2.2 could result in either a false-positive or negative EW. As CIII] is only expected in emission, we use the distribution of EW measurements that indicates absorption as an estimate of how many CIII] emission lines could be produced from noise spikes, under the
Paper: 2021MNRAS.505.5714A (fulltext)
the minimum comoving scale R = 8 h −1 Mpc. This cut is translated to an angular harmonic mode on each tomographic bin using the Limber relation l max = k max × χ(〈 z i 〉), where is the mean redshift for the i th tomographic bin of the analysis and χ( z ) the comoving distance computed on the fiducial cosmology of the analysis. Similar approaches were taken in Nicola et al. ( 2020 ) and Doux et al. ( 2020 ). We fix , which yields l max = 80, 127, 172, 215, and 246 for the five redshift bins for DES-Y1 cosmology. We checked these cuts do not change significantly when using a FLASK cosmology. We also keep l min = 43 for all bins based on the impact of binning for our modelling. In order to verify whether these scale cuts are effective to mitigate the effects of nonlinear bias we perform a simple χ 2 test comparing two data vectors: a fiducial data vector generated with a linear bias and a data vector contaminated with nonlinear bias. More specifically, we generate a contaminated data vector with an additional quadratic bias parametrized as a function of the linear bias as in Lazeyras et al. ( 2016 ). Using the FLASK covariance matrix we find that Δχ 2 = 0.2 for the physical cuts, where well below the criterion Δχ 2 1 adopted in Abbott et al. ( 2019a ). Hence the nonlinear biases as modelled above are mitigated by the physical scale cuts. Just for comparison, for the naive scale cuts, we find that Δχ 2 = 14.31 and therefore this further justifies our fiducial analysis choice for the physical scale cuts. 7 RESULTS ON MOCKS In order to validate our pipeline, we ran the implemented nested sampling algorithm using as input a random mock realization as well as the average of the set of mocks. We explored the parameter space: where b i is the constant linear galaxy bias in the redshift shell z i with a flat prior (0.8 b i 3.0), and Δ z i is the respective shift in the mean of the photometric redshift distribution for each redshift bin i : n i ( z ) → n i ( z − Δ z i ). The Gaussian priors are centred in Δ z i = 0 (for the synthetic data used in this section) and width as in Elvin-Poole et al. ( 2018 ). We use the FLASK covariance matrix as the fiducial one. We show that the CosmoLike theoretical covariance matrix yields very similar results in Fig. 4 . 11 In this section, we use the average of the FLASK mocks as a proxy for a noiseless data vector, as used in simulated likelihood analyses (Krause et al. 2017 ). The use of the FLASK covariance matrix gives the uncertainty one would expect for the real data likelihood analyses, which is the goal of the comparison between the harmonic and configuration space performed in the DES-Y1 data in Section 8 . The recovery of unbiased cosmological parameters in this case also shows that the Gaussian likelihood approximation is adequate. Figure 4. Comparison of the bias constraints to the average of the FLASK mocks using either FLASK (DES-Y1 run) or CosmoLike (quick run) covariance matrices with physical scale cuts
Paper: 2019ApJ...882L..30D (fulltext)
the AGN luminosity, or the distance from the BH. For simplicity we vary only one of these, the density, while keeping the others fixed. As discussed in following sections, this change, while simple, does serve to illustrate the types of SEDs that will filter through the wind. Changes in the mass-loss rate of the wind can cause changes in the hydrogen density of the equatorial obscurer. We examine the effects of such variations upon the transmitted SED in Figure 3 , which shows three typical SEDs. As the figure shows, the shape of the SED is highly sensitive to the value of the hydrogen density. Figure 3. SED transmitted through equatorial obscurer and incident upon the BLR is shown for three different values of the hydrogen density. The unextinguished SED is also shown. The SED is dramatically dependent on the hydrogen density of the obscurer. High hydrogen densities produce strong absorption in the XUV region and strong emission in the FUV/optical regions. The density and flux parameters chosen here do not matter in detail. The transmitted SED actually depends on the ionization parameter, which is the ratio of the ionizing flux to the hydrogen density (Osterbrock Ferland 2006 ). Increasing the hydrogen density lowers the ionization parameter inversely. Particular values of the density and flux do not matter as long as the ratio giving the ionization parameter is kept constant. As the ionization parameter increases the level of ionization of the gas increases. The gas opacity decreases as the number of bound electrons decreases. The ionization structure changes in ways that produce the three characteristic SEDs shown in Figure 3 . These are the three cases: 1. Case 1 has the lowest density and the highest ionization, and is shown in black. This wind is fully ionized, has no H or He ionization fronts, and nearly fully transmits the entire incident SED. 2. Case 2 has an intermediate density and is shown in blue. This has a He 2+ − He + ionization-front but no H ionization-front. The incident SED is heavily absorbed for the XUV energies, 11 11 We refer to the region 6–13.6 eV (912–2000 Å) as FUV, 13.6–54.4 eV (228–912 Å) as EUV, and 54.4 eV to few hundred eV (less than 228 Å) as XUV. although most of the hydrogen-ionizing radiation is transmitted. 3. Finally, Case 3 is shown with the red line and has the highest density. The wind has both H and He ionization-fronts, and much of the light in the EUV and XUV regions is absorbed. 4. The Response of the BLR to Changes of the Transmitted Continuum We now show how the EWs of the BLR lines in Figure 2 are affected by changes in the transmitted SED of the equatorial obscurer. Figure 4 shows how the EW of the strongest observed lines reacts as the density, n (H), of the equatorial obscurer varies. These changes are due to variations in the SED filtering through the equatorial obscurer. The three general types of SED shown in Figure 3 produce the three different BLR regimes shown in Figure 4 . We
Paper: 2020A&A...636A.117M (fulltext)
dissociation of TiO and VO in the limb even at the lower altitudes probed by our data, reducing the log VMR of both species to −10, well below the detection limits we set with our injection tests. It is also interesting to note that the work of Evans et al. (2017 , 2018 ) and Mikal-Evans et al. (2019) infers the presence of VO, but not TiO, when both were predicted as inversion-drivers by Hubeny et al. (2003) and Fortney et al. (2008) and were largely expected to occur together. It has been shown in the models of Goyal et al. (2019) that greater abundance of VO over TiO is only expected in a very narrow temperature range: ~ 1700–1800 K assuming rainout condensation, or ~1200–1400 K assuming local condensation. This highlights the importance of robust detections and non-detections of TiO and VO as a diagnostic for temperature and condensation processes in the atmospheres of ultra-hot Jupiters. Despite our non-detection of TiO and VO, Evans et al. (2018) still show a feature in their transmission spectra at 600–800 nm consistent with the presence of VO (or perhaps another molecule absorbing strongly in this range). Additionally, their emission spectra ( Evans et al. 2017 ; Mikal-Evans et al. 2019 ) are still best-fit with models containing VO and clearly show water emission features consistent with the presence of a temperature inversion. It was theorised by Spiegel et al. (2009) that a hot Jupiter witha solar carbon-to-oxygen ratio would require TiO of at least solar abundance to cause a temperature inversion, a level which both we and Evans et al. (2018) rule out in our analysis. Our analysis in this paper now seems to rule out the presence of VO also, although again we emphasise that VO cannot be robustly ruled out using high-resolution cross-correlation methodology until a more suitably accurate line list is published. Nevertheless, although TiO and VO were long thought to be the predominant cause of temperature inversions in hot Jupiters, it may now be time to look for other culprits. Recent theoretical work has postulated a range of potential optical absorbers such as SiO, Fe, Mg, AlO, CaO and metal hydrides ( Lothringer et al. 2018 ; Lothringer Barman 2019 ; Gandhi Madhusudhan 2019 ), some of which (such as AlO) have absorption bands in the 600–800 nm range. Gandhi Madhusudhan (2019) also suggested a theoretical link between the C/O ratio and the presence of a temperature inversion, postulating that a reduced infrared opacity due to a low H 2 O abundance in WASP-121b could contribute towards the existence of an inversion layer; the presence of H 2 O emission features, however, would appear to rule out this possibility. Finally, the transmission spectrum presented by Evans et al. (2018) shows a strong upward trend in the blue optical much steeper than would be consistent with Rayleigh scattering, which they tentatively identify as due to the presence of SH, another molecule known to absorb strongly in the optical and thus
Paper: 2020MNRAS.499..993B (fulltext)
2014 ; Roman-Duval et al. 2014 ). 8 For each of the tested models, Gordon et al. ( 2014 ) provide three different realizations of the best-fitting parameters. Of these, we adopt the best-fitting parameters corresponding to the expectation value of the full likelihood function for each parameter (see section 4.3 of Gordon et al. 2014 for details). The most important parameter of the BEMBB model, in terms of investigating the dust content across the SMC, is the dust surface density Σ d . To transform the dust surface densities to E ( B − V ) values for a direct comparison with our reddening map based on the background galaxies, we follow the formalism described by Whittet ( 2003 ). The relation between the total extinction at a given wavelength, λ, and the optical depth of extinction caused by the dust, τ d , is given by We can express this total extinction in terms of the extinction efficiency factor, Q ext : where N d is the column density of the dust and a is the radius of the dust grains (see section 3.1.1 of Whittet 2003 for a full derivation). The dust surface density Σ d is related to N d via where ρ d is the density of the dust grains. We can calculate the V -band extinction using the V -band extinction efficiency factor Q V , such that We adopt the following values for the remainder of the variables in equation ( 4 ): Q V = 1.4 (Chlewicki 1985 ) and, assuming ‘classical’ dust grains, a = 0.1 μm and ρ d = 3 g cm −3 (Mann Kimura 2000 ; Whittet 2003 ; Kohout et al. 2014 ). Finally, we convert A V into E ( B − V ) following the standard A V = 3.1 × E ( B − V ) (Cardelli et al. 1989 ). Note that the values of Σ d represent only the internal dust content of the SMC. Prior to fitting the HERITAGE data, Gordon et al. ( 2014 ) use the integrated MW H i velocity maps in the directions of the SMC (Stanimirović et al. 1999 ; Muller et al. 2003 ) to subtract the structured emission due to the MW. 4.2 Quantitative comparisons Fig. 6 presents the comparison between our reddening map based on galaxies with low levels of intrinsic reddening and the various reddening maps of the SMC discussed in Section 4.1 . To facilitate the comparison, we have resampled the literature maps to a 20 × 20 arcmin 2 resolution by simply taking the median reddening value of all stars/cells that fall within a given bin. This resampling of the literature maps will undoubtedly result in the loss of small-scale (less than 20 arcmin in size) features in these maps. Any large-scale morphological features, however, such as pervasive enhancements of dust along the main body of the SMC, should be preserved and visible in the maps presented in Fig. 6 . Figure 6. 20 × 20 arcmin 2 resolution reddening maps covering the SMC. Each panel refers to a different literature source. The contours in all panels represent the IRAS 100 μm dust emission. The dashed line in the lower right panel represents the footprint of the HERITAGE observations. Of the eight literature maps shown, three