adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020A&A...643A.110Z (fulltext)
typically multi-periodic oscillators that can show very rich pulsation frequency spectra that challenge asteroseismic analyses and the theoretical interpretation of the observed pulsation frequencies. A- and F-type stars can also show a combination of pressure ( p -), gravity ( g -), and Rossby ( r -) modes (e.g. Uytterhoeven et al. 2011 ; Saio et al. 2018 ). Hence, we investigated all available photometric data sets of β Cas for the presence of g - and r- modes in addition to the already described p -modes. g -modes would be found in the range of ∼3.3 to 0.3 d −1 in frequency (corresponding to ∼0.3 to 3.0 days in period; Kaye et al. 1999 ); r- modes appear as a group of peaks below m times the rotational frequency. BRITE-Constellation and SMEI data are known to allow the detection of g -mode pulsations when the standard procedures for data reduction are applied (e.g. Kallinger et al. 2017 ; Zwintz et al. 2017 ). The BRITE-Constellation and SMEI data for β Cas do not show any signs for g -mode pulsations. We also checked the TESS data for the potential presence of g -modes before we applied the Gaussian filter to remove the instrumental signal, and also did not find any evidence for them (see Fig. B.1 ). In all our data sets, we do not detect a frequency that can be attributed to the rotation of β Cas or a group of peaks typical for r -modes. There are two prominent physical effects that complicate the asteroseismic analysis of δ Scuti stars. One effect is moderate to fast rotation, which can cause a splitting of the pulsation modes with the same n and l values but different m values (e.g. Lignières et al. 2006 ). The second effect is the presence of magnetic fields, which is a rather recent discovery for δ Scuti stars (e.g. Neiner Lampens 2015 ; Neiner et al. 2017 ). In this context, β Cas is a special case because observationally it shows only three independent p -mode pulsation frequencies and not several dozen to hundreds of modes. The reason might be connected to the inclination angle of only 19° ( Che et al. 2011 ) and the presence of the magnetic field. Despite the rather high rotational speed of β Cas, no sign of a rotational splitting can be detected in the present observational material. We calculated the pulsation constant, Q , based on our values for the three independent frequencies, F 1 , F 2 , and F 4 , and our fundamental parameters, T eff = 6920 K and log g = 3.53 cgs. The pulsation constant Q ( Petersen 1976 ; Stellingwerf 1979 ) can be expressed as: where P is the pulsation period in days and M bol is the bolometric magnitude, which is defined as M bol = M V + BC. The absolute magnitude of 1.14 mag is calculated from the distance of 16.8 pc, and the bolometric correction, BC, can be calculated from the relation given by Reed (1998) . The pulsation constant has a value of 0.033 for fundamental mode pulsation ( n = 0), 0.025 for the first overtone ( n = 1), 0.020 for the second overtone ( n = 2), and 0.017 for the third
Paper: 2018A&A...616A.168L (fulltext)
F336W, F438W) = 0.0 and Δ(F275W−F814W) = 0.0 in the chromosome maps. The results are plotted in the two panels of Fig. 2 . We display on the horizontal axis the different chemical compositions investigated. The vertical axis displays the indices ΔC(F275W, F336W, F438W) and Δ(F275W−F814W) for all our synthetic populations. The results are quite clear. The index Δ(F275W−F814W) is affected essentially only by variations in Y; instead, the ΔC(F275W, F336W, F438W) index is in comparison weakly sensitive to Y, but is mainly affected by variations in N (e.g. Piotto et al. 2015). The range of Δ(F275W−F814W) values for P1 stars observed in several clusters like M 3 and NGC 2808 discussed here is therefore due to a range of Y abundances. This is however not found in other clusters, for example NGC 288. The extended P1 sequences in the chromosome maps also display a tilt towards increasing ΔC(F275W, F336W, F438W) for decreasing Δ(F275W−F814W) (e.g. Milone et al. 2017 ). Based on Fig. 2 , this can possibly be explained by a narrow range of N amongst P1 stars. We conclude that the spread in P1 stars indicates that a relatively wide spread of Y is present within this population, possibly accompanied by a narrow range in N. Fig. 2. Expected chromosome map indices of RGB stars with P1 chemical composition (squares), P1 with He enhanced by ΔY = 0.05 (triangles up), P1 with He enhanced by ΔY = 0.15 (triangles down), P2 extreme (crossed squares), P2 extreme with ΔY = 0.05 (stars), and P2 moderate with ΔY = 0.05 (crossed circle); see text for details. In summary, P1 stars are defined by their location on the chromosome maps, which is consistent with the position of stars with homogeneous and field-like [O/Fe] and [Na/Fe] measurements in the clusters like M 4 and NGC 2808 investigated here. These P1 stars are often called first-generation stars in the literature, according to the proposed cluster self-enrichment scenarios. We have shown here that P1 stars in clusters with extended P1 in their chromosome maps, such as NGC 2808 and M 3, show a spread of initial Y and a small range of N. In other cases, like NGC 288 with a non-extended P1 in the chromosome map, the Y and N abundances are also homogeneous, like O and Na. The former behaviour is entirely unexpected in the context of all scenarios for the origin of MPs. 3.2. Distribution of the subpopulations along the RGB An independent test for He-abundance variations among P1 stars is to look at the optical colour distributions of each of the identified subpopulations. Bolometric corrections in optical filters are negligibly affected when moving from typical P1 to P2 metal distributions (e.g. Sbordone et al. 2011 ), but an increase in Y shifts the RGB towards bluer colours because isochrones with enhanced He have hotter T eff values. For each cluster, we first identified different RGB subpop-ulations from the chromosome maps in Fig. 1 . The mean values of the ΔC(F275W, F336W, F438W) and Δ(F275W−F814W)
Paper: 2021A&A...647A.195G (fulltext)
spiral and elliptic galaxies, with the type assigned by visual recognition from archival optical images. For all 31 sources we found the spectral signature of the re-emission by an obscuring torus. From the peak frequency and luminosity we can derive a mean dust temperature of T T ∼ 300 K and a mean covering factor of q T = cos θ T = 0.3; most of the sources have q T 0.4. Therefore, from Eq. ( 7 ), we obtain a mean radius of R T ∼ 5 parsec. To best describe the SED of all the 20 unknown AGN sources with data in the millimeter and submillimeter wavelength band, we had to consider a second emitting toroidal structure at a larger distance from the black hole. It is reasonable to interpret this component as a dust belt inside the galaxy rather than a proper dusty torus surrounding the black hole. The mean obtained value for the temperature distribution is T DB ∼ 50 K, the mean covering factor q DB = 0.6, and the distance distribution shows a peak at ∼150 pc, where some sources reach ∼1 kpc. In Fig. 8 we show the distribution of the emitting structures distance. In evaluating the absorption and consequent re-emission on this scale, we must take into account the contribution of stars. It is reasonable to think that a large fraction of the radiation heating the dust does not come from the AGN, but from nearby stars. Nonetheless, since the dust belt blackbody has a peak luminosity that is, on average, equal or larger than the host galaxy peak luminosity, we can comfortably assume that the stars alone cannot provide enough energy to explain the measured emission. Furthermore, a heavily obscured accretion disk, whose presence is highly suggested by the X-ray spectral emission, could easily provide the necessary energy to heat the dust emitting this spectral feature. This is a clear clue that the host galaxy environment plays a key role in obscuring the AGN. Fig. 8. Distance distribution from the black hole of the IR and submillimeter emitting structures found in our work. The blue columns represent the radius of the classical obscuring torus in the AGN inner region; the red line indicates the radius of an emitting dust belt, of which we have found traces in 20 out of 31 sources. The distances are derived from Eq. ( 7 ). Furthermore, we can make two considerations about the general slope of the SED in these 31 sources. First, all but five sources show a clear sign of absorption in the soft-X ray energy range. Those that do not are SWIFT J0317.1+1542, SWIFT J0329.4−2157, SWIFT J1643.4+0986, SWIFT J2004.6−1111, and SWIFT J0302.2+6853. Of these, only the last source shows no signs of re-emission in the submillimeter band, which in our model is interpreted as the emission from a dust belt. These sources need more in-depth studies, in particular obtaining optical spectra could be very useful. Also, regarding the low-energy emission, most of the sources with data at submillimeter wavelengths show an IR-to-optical luminosity ratio of about one, with a few
Paper: 2016MNRAS.462.3350T (fulltext)
the differing resonance profiles converging on to differing threshold positions. The present calculation has shifted the thresholds to lie in their exact observed positions listed in Table 1 . In Fig. 6 we present the effective collision strengths for some higher lying transitions, the 3d 7 4 F 9/2 –3d 7 2 G 9/2 (1 → 8), 3d 7 2 P 1/2 –3d 7 2 H 9/2 (11 → 13) and 3d 7 2 G 7/2 –3d 7 2 H 9/2 (9 → 13). Excellent agreement is evident for all three transitions when a comparison is made with the data of Storey Sochi ( 2016 ) across all temperatures where a comparison is possible. The collision strengths and effective collision strengths for all 42 486 forbidden and allowed lines are available from the authors on request but we present in Table 4 the effective collision strengths for all transitions among the lowest 11 3d 7 fine-structure levels across nine temperatures of astrophysical importance. 1 Figure 6. The effective collision strengths, Υ 1 → 8 (top), Υ 11 → 13 (middle) and Υ 9 → 13 (bottom), presented as a function of temperature in K (bottom). Solid black lines with stars are the current data set and the dashed line with crosses represent the results from Storey Sochi ( 2016 ). Table 4. Effective collision strengths as defined by equation ( 5 ) are presented between an upper ( j ) and lower state ( i ), across a range of nine temperatures (K), 3800 T 25 100 K. We have denoted ( a ) ≡ ×10 a . 3.3 Line ratios Combining the electron-impact excitation rates with the decay rates ( A -values), it is possible to investigate important infrared and visible line ratios. For simplicity and purpose of this study, we neglect the recombination and ionization process from the neighbouring ion stages and focus on the populating mechanisms solely of Co iii . Assuming local thermal equilibrium and detailed balance, we can derive the collisional radiative modelling approach detailed in (Griffin et al. 1997 ). We can therefore investigate selected transitions that have been suggested as temperature or density diagnostics. We present in Table 5 a comparison of emissivities (ρ ij ) between our current results, and those from Storey Sochi ( 2016 ) for particular transitions at electron densities N e = 10 4 cm −3 and N e = 10 7 cm −3 . The emissivities are calculated relative to the H β line [see equation ( 1 ) in Storey Sochi ( 2016 )], where the effective recombination coefficients can be found in Hummer Storey ( 1987 ) and Storey Hummer ( 1995 ). The wavelengths are those from National Institute of Standards and Technology (NIST) and we use our current set of A -values. In general there is excellent agreement for all emissivities considered with the major outlier ρ 1 → 2 found to be around 30 per cent at N e = 10 4 cm −3 . This is evident from the strong Υ 1 → 2 from Fig. 5 (a) at T = 10 4 K. By replacing this one effective collision strength to Υ 1 → 2 ( T = 10 000) = 4.18, then the ρ 1 → 2 differences are within a much more agreeable 8 per cent. A similar
Paper: 2018MNRAS.479.1055B (fulltext)
therefore agree. At 5.5 z 5.9, the GOLD distribution lies between the bounds shown in Fig. 8 . At z > 5.9, the difference is attributable to the small sample size of the sub-samples (see the text). The differences between the SILVER and GOLD samples at 5.7 z 5.9 are due to the small sample size of the GOLD sample. The difference in number of lines of sight in this range is a factor of 2.5, which reflects the fact that very high-quality spectra are harder to obtain for quasars at z > 6.0. The SILVER and GOLD sample are marginally consistent according to a KS test( p 0.2). On the other hand, the sizes of the SILVER and GOLD samples are similar at 5.9 z 6.1, and agree with each other well. However, the additional lines of sight included in the BRONZE sample are of insufficient quality to distinguish opacity beyond τ eff ≳ 3. Since the mean opacity measured in the SILVER sample is roughly τ eff ∼ 4.5, these S/N 5 spectra yield upper limits (equal to twice the error) which are very poor in comparison. The small sample size in this bin (8 measurements in the GOLD sample, and 16 in the BRONZE one) mitigates the significance of this discrepancy, with the KS level indicating only mild discrepancy ( p 0.2). In addition, two of the BRONZE lines of sight do show tentative signs of residual transmission. The z = 6.34 quasar J1148+0702 displays a transmission peak within 6 Å of the formal end of the proximity zone, while the z = 6.23 quasar J2325 displays such a peak outside of its proximity zone but has an S/N of 1.8, making it impossible to definitely rule of reduction issues. Further scrutiny of these objects is required in order to determine whether these peaks may be related to particularly long and sporadic proximity zones. In light of these results, we decide to adopt the SILVER distribution in every instance in this paper where the data are presented or analysed in the form of a CDF. The maximum-likelihood method described above has been explicitly designed to account for observational errors and sensitivity, and we accordingly use the BRONZE sample only in Section 6 . 5 COMPARISON WITH MODELS In this section, we compare the updated CDFs of Lyman-α transmission in 50 cMpc h −1 windows with three simulations. Here, we briefly describe these simulations and outline their most relevant features. When comparing predictions from these numerical simulations to observational results, it is important to keep in mind a few caveats. First, all of the following numerical models explicitly rescale the ionizing background intensities to match the observed mean effective optical depth of the observations presented in this paper, 〈 F 〉 obs , by freely choosing a parameter A suchthat: At low redshift ( z ≲ 5.7), this rescaling is small and is somewhat justified by the difficulty of self-consistently generating the ionizing UVB. In the following analysis, all simulation snapshots have been rescaled in this way to observed 〈 F 〉 obs for each redshift bin. The
Paper: 2017MNRAS.466.2633S (fulltext)
and Fig. 6 , we have shown that the radial density profile of the ejecta is well described by a power-law function of the velocity v with an exponent 6. This distribution is shallower than those of expanding envelopes in normal SNe (from n 9 to 12 as assumed in our model), which is clearly a consequence of the additional energy injection. The density distribution of freely expanding ejecta is a key to distinguishing existing models of SLSNe and other extraordinary SNe. In the following, we discuss how ejecta with such density structure form in the presence of the central energy source. For simplicity, we restrict ourselves to the Newtonian limit. We consider the following idealistic case. In normal SNe, the strong blast wave driven by a point explosion is the only way to transport the explosion energy to the outermost layer. As a result, only a small fraction of the energy is deposited in the outer envelope, while the inner ejecta possess the bulk of the energy, leading to a steep kinetic energy distribution (Matzner McKee 1999 ). In contrast to normal SNe, the central engine deposits an energy much larger than the supernvova ejecta for a time-scale much longer than the expansion time-scale of the ejecta. In our setting, the additional energy is injected at a constant rate L . Therefore, if the energy is continuously transferred throughout all the layers of the ejecta without loss or stagnation, the kinetic energy flux v 3 of the ejecta powered by the energy injection at a radius R 0 roughly proportional to the energy flux . The presence of relativistic flows leaking from the hot bubble makes the efficient energy transport possible. The flows can directly bring and deposit the additional energy throughout different layers. Thus, the following relation, is expected to hold for SN ejecta with a sufficiently long-term energy supply. In other words, the kinetic luminosity of the flow is constant. This relation gives the density distribution immediately after the ejecta are affected by the energy injection. We denote the time when the ejecta following this relation are created by t 0 . The ejecta are not freely expanding at this time. As the ejecta expand to the surrounding space, the density distribution would gradually be modified. Our goal is to derive the density distribution at the freely expanding stage. We regard the radial velocity v as a Lagrangian coordinate and derive the density distribution as a function of v . We consider a concentric shell with inner and outer velocity coordinates v and v v . The inner and outer boundaries are located at R R 0 ( v ) and R R 0 ( v v ) at t 0 . The width l s of the shell is initially given by, where the last expression is obtained for a sufficiently small v . The boundaries travel at the velocities v and v v with time and reach R R 0 ( v ) v ( t t 0 ) and R R 0 ( v v )( v v )( t t 0 ) at t . For t much longer than t 0 , the initial radius R 0 ( v ) can be neglected and the inner and outer radii are
Paper: 2016MNRAS.457...51L (fulltext)
iron. Finally, the microturbulent velocity (ξ t ) is established by erasing any trend between the abundance from Fe i and EWr. The same approach to constrain ξ t is used consistently in the three independent analysis. The atmospheric parameters are then set in an iterative fashion, first setting the temperature, and then revisiting T eff as the gravity and microturbulence are tweaked. The error associated with the determination of ξ t is estimated by propagating the uncertainty in the slope in the rEW versus Fe abundance plane. This uncertainty is of the order of ≃0.07 km s −1 . The derived T eff are typically based on more than ≃70–130 Fe i lines and have internal uncertainties of about 50–60 K, while the internal uncertainties in log g are of the order of ≃0.06 dex. The derived atmospheric parameters and [Fe i /H] and [Fe ii /H] abundances are listed in Table 2 , along with their associated uncertainties. The left-hand panel of Fig. 2 illustrates how they compare to Y14 results. We observe that our T eff , log g , and ξ t estimates are in excellent agreement with those listed in Y14 , being the average difference between our and Y14 estimates Δ T eff = −19 ± 8 K, Δlog g = −0.0 ± 0.0 dex, Δξ t = −0.09 ± 0.02 km s −1 . The agreement between our [Fe i /H] and [Fe ii /H] abundances and those listed in Y14 is also very good. In particular, we note that the metal-poor stars identified by Y14 , i.e. see Table 1 , are also the most metal-poor stars in our analysis (see Table 2 ). Additionally, the four stars with [Fe/H] ≃ −1.0 dex are the same metal-rich stars in Y14 . In the left-hand panel of Fig. 3 , we plotted the histograms of the [Fe i /H] and [Fe ii /H] distributions when the full spectroscopic analysis is performed. Our ability to portrait the [Fe i /H] and [Fe ii /H] distributions for this small data set critically depends on the adopted bin size (Δ). To adequately represent the shape of the underlying distribution, in Fig. 3 we selected Δ as the value which minimize C (Δ) = (2 me − va )/Δ 2 ; where me and va are the mean and variance of the data set. As one can see, we obtain an iron dispersion that mirrors the dispersion measured by Y14 . We find a large (Δ[Fe/H] ≃ 0.8 dex) iron distribution with two prominent peaks at [Fe/H] ≃ −1.7 and −1.0 dex and a smaller component at [Fe/H] ≃ −1.5 dex. The iron dispersion within the metal-poor stars is larger (Δ[Fe/H] ≃ 0.4 dex) than that expected from observational errors alone, indicating the presence of two separated subpopulations with different metallicity. Photometric analysis . We use VI WFI photometry to derive photometric T eff and log g * estimates. 2 Input T eff values have been computed by means of the ( V − I ) 0 – T eff transformation by Alonso, Arribas Martínez-Roger ( 1999 ) adopting a colour excess E ( B − V ) = 0.02 mag taken from the most update version (2010) of the McMaster catalogue (Harris 1996 ). Surface gravities have been computed assuming the photometric T eff , the
Paper: 2020MNRAS.497.2623J (fulltext)
pressure wherever a shock is detected (see Fig. 2 ) since shocks are the acceleration sites for CRs. The following conditions (Gupta, Nath Sharma 2018b ) have been used to detect whether a particular region within the simulation box is shocked or not: ; Δ x |∇ p |/ p > δ tolerance ; ∇ T .∇ρ > 0. Figure 2. Comparison of density and thermal pressure between TH and TH+CR runs for M12 galaxy at ≈20 Myr simulation time. The three sets of plots (from left) are for SFR = 0.1, 1, and 10 . We also show the CR pressure in a separate plot. Green dots show the shock locations. In the TH+CR runs, these are the locations where CR energy is injected. For all our simulations, we have used δ tolerance = 0.5. Wherever a shock is detected, we redistribute the total pressure between thermal and CR component following the CR pressure component fraction w = p cr /( p th + p cr ). In all our models, we have used w = 0.2. 3 RESULTS To understand the properties of outflowing gas between the models with and without CRs, we investigate (i) the morphology of the outflowing gas, (ii) outer shock position, (iii) mass-loading factor, and (iv) temperature distribution of the gas. These quantities have been used in previous studies to quantify the dynamical impact of CRs. Here we present the results from our simulations of three different galaxies with halo masses of , and using three different constant SFRs: 1 0.1, 1, and 10 . To represent runs with and without CRs, we have used two different labels: ‘TH+CR’ and ‘TH’, respectively. Note that we have used our simulation for M11 galaxy as the fiducial run. The used parameters are given in Table 1 . 3.1 Outflow morphology In Figs 2 , 3 , and 4 , we show the morphology of outflowing gas for three different halo masses for TH and TH+CR runs. For all runs, we find that the shape of the outflow is nearly spherical in nature. For M8, the shape is expected to be more spherical because, in this case, the galactic disc is much smaller than the outflow length-scale and the disc is not included in our simulation. For all cases, the forward and reverse shocks are prominently distinguishable, except for SFR = 0.1 in M12 galaxy. In this case, the outflow has an irregular shape and does not show a prominent reverse shock; the locations of shocks are rather distributed inside the bubble. This can be seen from the shock locations, as displayed by the green dots in the second rows of Figs 2 , 3 , and 4 . Figure 3. Same as Fig. 2 , for M vir = 10 11 M ⊙ . Figure 4. Same as Fig. 2 , for M vir = 10 8 M ⊙ . For SFR = 0.1 (first column of Figs 2 , 3 , and 4 ), a comparison of different snapshots among three haloes shows that for M12 galaxy, before the outflow breaks out of the central region, an acoustic wave propagates from the central region. This can be understood as follows. In our set-up, CRs are initially confined in the galactic disc and the halo has very low CR pressure. Due to the pressure gradient, the CRs start to diffuse out of the disc