adsabs/WIESP2022-NER huggingface dataset:
Paper: 2021MNRAS.505L.115Y (fulltext)
g is the gravitational acceleration. We can use the velocity profile of the shock to calculate the surface density profile of the ejecta blanket Energy and momentum conservation yield limits on the slope of the surface density distribution −2.5 > dln σ/dln r > −3. If we assume a constant density for the settled ejecta, then the thickness of the ejecta blanket scales the same way as the surface density. Most ejecta blankets around craters on the Moon are within this range (Zhu et al. 2015 , 2017 ; Raggio et al. 2016 ). In contrast, ejecta blankets from chemical and nuclear explosions on Earth are slightly steeper −3 > dln σ/dln r > −4, due to air drag (Carlson Jones 1965 ). Using the same shock trajectory we can make a theoretical prediction for the density slope we numerically calculated in the previous section. We saw that ∝ m −β . At distances much larger than the radius of the impactor, the velocity profile is roughly homologous ≈ d / t , where d is the distance from the impact site and t is the time since the impact. Combining these relations with the definition of density m ≈ ρ d 3 we find a scaling relation for the density profile ρ ∝ r −3 − 1/β . For β = 2/3 we obtain dln ρ/dln r = −4.5, which is close to the value obtained in the numeric simulation dln ρ/dln r = −4.1. Eventually, the shock stops and the crater reaches its final size. There are two criteria that can determine the final size of the crater (which will always be the smaller of the two). One possibility is that the shock stops when the shock pressure drops below the elastic modulus of the soil Y , so the final shock radius is where R i ≈ ( m i /ρ 0 ) 1/3 is the radius of the impactor. The second possibility is that the shock stops when the shock pressure drops below the hydrostatic pressure g ρ R , and so In the case of the lunar regolith, this is the deciding condition. 3.2 Craters from PBHs The density of PBHs is much higher than that of meteors, so instead of transferring all of their energy to the target at the impact site, the impactor just passes through and deposits roughly the same amount of energy per unit length along its trajectory inside the target. For this reason, above some critical threshold, the evolution of the shock wave is independent of the density of the impactor. To understand how the shock wave will behave in this case, one can think about this problem as a series of point explosions, where the energy of each explosion scales with its depth E ∝ x . If the mass of the BH is m i , then the effective cross-section is determined by the Bondi radius , and so . For a uniform density soil, the mass in a sphere of radius x around the hotspot is m ≈ ρ 0 x 3 . Hence, . We thus obtain a new power-law relation between the shock velocity and the swept up mass, which is shallower than that obtained for classical craters, i.e. β = 1/3. Using the same reasoning as in the previous section, we find the slopes of ejecta blanket surface density profile As was done
Paper: 2018ApJ...859...66S (fulltext)
carbon. The luminosity was obtained by interpolating and fitting the optical ( B , V , i ′) and the NIR ( J – H – K s ) fluxes from Fransson et al. ( 2014 ) at several epochs, in addition to the five epochs (days 87, 254, 465, 621, 844) for which we have Spitzer data. Thereafter, the luminosity, L ( t ), was fitted using a power law of time. The exponents are found to be −0.55 and −4.0 with a switch at day 310, as shown in the figure. We also calculate the UVO luminosity in uniform temporal bins in order to reconstruct the UVO light curve. The evolution of L UVO was thereafter fitted using a power law, t − β , with β = 0.55 before day 310 and β = 4.0 thereafter. The best-fit scenario is presented in Figure 1 (top left). The nature of the luminosity, L ( t ), derived in this study compares well with the findings by Fransson et al. ( 2014 ), Jencson et al. ( 2016 ), and Gall et al. ( 2014 ). The rapid decline in luminosity at later times can be attributed to a change in the density profile at the shock front. A similar drop in luminosity is also reflected in the evolution of the X-rays along the line of sight (Chandra et al. 2015 ). 2.1. Possible Source of the IR Emission In this study, good fits to the IR spectra of SN 2010jl, shown in Figure 1 , were achieved using the contribution from any of the three sources (a blackbody, astronomical silicates, amorphous carbon) as the IR component. Therefore, the best-fit models do not have any strong bias toward any of the three sources being the most likely one. A blackbody fit is appropriate when the dusty shell is optically thick. Additionally, the blackbody radius ( R IR ) represents the minimum possible radius of the dusty shell from where the IR emission originates. The NIR fluxes can be fit well using the absorption coefficients of optically thin astronomical silicates. The upper limit on the 9.7 μ m flux at late times was obtained from the SOFIA observations (Williams Fox 2015 ). It indicates the absence of silicate features. Therefore, the presence of silicates is doubtful, unless the grains are large ( a ∼ 5 μ m) or the dusty shell is optically thick. With only photometric data in the 2.2–4.6 μ m region, many featureless dust species can fit the spectrum. We choose carbon as a likely dust type owing to its relatively large abundance in space compared to other metals. Moreover, the studies dealing with the IR spectra from the preexplosion era also hint at the presence of carbon dust in SN 2010jl (Dwek et al. 2017 ). However, the CSM of SN 2010jl is reported to have a N-rich (Fransson et al. 2014 ) C-depleted environment (N/C =25 ± 15, N/O = 0.85 ± 0.15). In such environments, O-rich dust species are known to be the primary dust components and all of the carbon mostly gets locked up in CO molecules (Sarangi Cherchneff 2013 ). To support this argument, the observation of a N-rich dusty shell around massive stars, such as the Homunculus nebula surrounding Eta Carinae, does not show any
Paper: 2020ApJ...902L..10H (fulltext)
Earth) for different ocean coverages. Panel B shows how pCO 2 varies with varied solar luminosity for different ocean coverages. Both Panel A and B assume an Earth-like outgassing rate of CO 2 . We did not plot solar luminosities greater than 1.2 S eff as the weathering rate dependence on CO 2 becomes negligible. Panel C shows how the surface temperature varies with varied CO 2 outgassing rates (relative to modern Earth) for different ocean coverages. And Panel D shows how pCO 2 varies with varied outgassing rates for different ocean coverages. Panel C and D assume a modern Earth solar constant. All panels assume a fixed planetary albedo of α = 0.3. Based on the results from Figure 1 , a planet’s buffering capability changes significantly when seafloor weathering becomes the dominant weathering sink, due to the different pCO 2 dependencies of seafloor versus continental weathering. We therefore determine the critical ocean fraction where weathering behavior switches from being dominated by seafloor weathering to being dominated by continental weathering by setting F cw = F sw , and solving for the ocean fraction: We solve for this critical ocean fraction for a range of solar luminosities, setting the rest of the variables equal to the median values that we used for Figure 1 , as a function of pCO 2 (see Figure 2 ). The solid curves in Figure 2 correspond to the at which the weathering fluxes from continental weathering and seafloor weathering are equal. While we calculated over a range of pCO 2 values, only certain pCO 2 pressures would be physical for a given solar luminosity using our weathering model. The dashed curves with the points are from our previous results, where we calculated the actual pCO 2 of a planet given its solar luminosity and ocean fraction (see Figure 1 ). Figure 2. The solid curve represents the ocean fraction at which seafloor weathering and continental weathering are equal, as a function of pCO 2 . We solved this critical ocean fraction curve for different solar luminosities and found that the difference was negligible (the black curve shown is for an S eff of 1.0). If a planet’s ocean coverage is above the curve, then seafloor weathering is the dominant sink of CO 2 , and if a planet’s ocean coverage is below the curve, then continental weathering is the dominant sink of CO 2 . The colored curves and points are data from Figure 1 , allowing us to constrain this parameter space by plotting the actual pCO 2 values that our model converged toward for a given ocean fraction and solar luminosity. We find that for solar luminosities lower than modern Earth’s, an ocean fraction of greater than 90% is needed for seafloor weathering to equal continental weathering. What we find from Figure 2 is that for relative solar luminosities less than or equal to S eff = 1.0, the planet requires an ocean fraction between 90% and 99% for the CO 2 flux from seafloor weathering to equal the CO 2 flux from continental weathering. This is
Paper: 2019MNRAS.490.5335S (fulltext)
thick disc was assumed to span from 9 to 11 Gyr. We also checked with an age span of 10–12 Gyr for the thick disc and found that it gives the same best-fitting parameters. The slight decrease in the mean metallicity of the thick disc compared to the estimate based on simply measuring the metallicity of stars within 5 R /kpc 11 and 1 | z |/kpc 2, is due to the inclusion of stars lying between 2 | z |/kpc 3 in the fitting process. This suggests that there is a small vertical gradient in the metallicity of the thick disc as found by others previously (Kordopatis et al. 2011 ; Duong et al. 2018 ). Note, stars in 2 | z |/kpc 3 were not included in the former scheme as they could be contaminated with stars from the metal-poor stellar halo and could suffer from volume incompleteness. However, they are included in the later scheme because it takes both these effects into account. In Fig. 12 we show the mean metallicity as function of Galactocentric radius R for different slices in height | z | for all stars with positive rotation about the Galaxy, which should eliminate star belonging to the halo. Results from APOGEE-DR14 and GALAH DR2c are shown separately. We also plot Galaxia predictions from the MP and MR models. The MP model clearly has a thick disc that is too metal poor to fit the observed data for | z | > 1 kpc. The new MR model reproduces the GALAH data very well. It also reproduces the APOGEE data very well, except for the slice closest to the mid-plane and the slice furthest from the mid-plane. Compared to GALAH, stars in APOGEE that are close to the mid-plane are metal rich, but the stars progressively become metal poor with increasing height above the mid-plane. These differences could be due to systematics in abundances between the two surveys, but could also be due to the different selection function of the surveys. The effect of selection function is clearly visible in Fig. 12(f) corresponding to the top most slice (2 | z |/kpc 3). Here, the MR model has a thick disc with a mean metallicity of −0.16. However, the mean metallicity of stars in this slice is −0.2. This is due to two reasons. First, because the metal poor stars are more luminous and are visible furthest in a magnitude-limited survey, we find that this effect decreases the metallicity of thick disc stars in this slice by 0.02 dex. Secondly, the top most slice has a larger contribution from the (metal poor) halo compared to other slices and this contamination decreases the metallicity of stars in this slice by −0.015 dex. Fig. 12 shows that close to the mid-plane there is a strong radial metallicity gradient. As we move away from the mid-plane the gradient diminishes progressively to zero. Close to the plane, a radial gradient of −0.07 (dashed line), as used in the Besançon (MP) and MR models, is roughly consistent with the observed data. Figure 12. Mean metallicity as function of Galactocentric radius R for different slices in height | z | for all stars with positive
Paper: 2020ApJ...888....9P (fulltext)
like a galaxy cluster creates a small-scale dipole pattern in the direction of the large-scale CMB temperature gradient. The QE (Hu et al. 2007 ) is based on extracting the correlation between the dipole and background gradient to estimate the lensing convergence . Specifically, the lensing convergence can be estimated from a weighted product of filtered versions of a gradient map and small-scale lensing map :Here, is the pointing unit vector, is angular multipole, and A l is a normalization factor. While designed to pull out the lensing-induced correlations between large-scale and small-scale CMB anisotropy, the QE is also sensitive to correlations due to foreground emission. Of particular concern is the cluster’s own SZ emission, which is typically an order of magnitude larger than lensing signal. As pointed out by Hu et al. ( 2007 ), the magnitude of the SZ bias can be lowered by decreasing the characteristic scale of the low-pass filter on the gradient map, which is set to 2000 in the current work. However, a stronger low-pass filter obviously reduces the number of modes used to measure the gradient, and thus decreases the signal-to-noise. The modified QE instead eliminates this bias by using an SZ-cleaned map for the large-scale gradient map. The large-scale gradient map is chosen because the CMB has much more power on large scales, so the noise penalty from SZ removal has minimal impact. Note that while multiple foregrounds can be removed in principle, in practice the focus has been on removing the SZ signal. This is simply because the SZ signal introduces the largest bias. With the SZ signal present in only one of the two maps, there is no SZ-induced correlation between the two maps and no net bias on the reconstruction of the lensing convergence. However the SZ emission in the small-scale lensing map does add noise to the lensing reconstruction. Since under self-similarity the SZ flux (Kaiser 1986 ), y , is expected to scale with cluster mass M as while the lensing signal is linear in mass (in 1-halo regime), the additional SZ variance will generally be more important for high-mass clusters. The SZ variance will also be more important in low-noise surveys, i.e., when it is larger than the instrumental noise in the convergence map. 3.2. Template Fitting to Reduce the SZ Variance An obvious way to eliminate this SZ variance is by using SZ-free maps for both the small-scale and gradient maps. Of course this would undo the advantages of the modified QE for the instrumental noise in the small-scale map. One can also reduce this extra variance by projecting out a model template for the SZ signal, and thereby reducing the total amount of SZ power in the small-scale map. Given the impossibility in creating a “perfect” SZ template, the template fitting will not completely eliminate the SZ signal in the small-scale map. However, template fitting can significantly reduce the SZ power in the small-scale map, and thus reduce the SZ noise penalty
Paper: 2018MNRAS.473.3755Q (fulltext)
SEDs from Abdo et al. ( 2010a ), a time-scale of one day due to the lack of variability data and γ min = 200 (Zhang et al. 2012 ). 4 RESULTS We used three types of EEDs to fit the SEDs of TeV BL Lacs. The SEDs for the maximum probability and the 1σ error band are shown in Fig. 1 . The one-dimensional (1D) probability distributions and the two-dimensional (2D) confidence contours of the model parameters are shown in Figs 2 – 5 . In the 1D distributions, the dotted lines show the maximum likelihood distributions and the solid lines show the marginalized distributions. In the 2D distributions, the inner contour denotes the 68 per cent CL, while the outer contour represents the 95 per cent CL. The model parameters are listed in Tables 1 – 3 . Figure 1. Left-hand panels: SEDs of the three objects fitted by three types of EEDs, where the solid black, blue dash-dot and magenta dash-dot-dot lines represent the SEDs given by the BPL, PLLP and PLC EEDs, respectively. Right-hand panels: SED contours of the three objects in γ-ray bands with the 1σ error bands, where the shaded areas in grey, blue and orange represent the SED contours given by the BPL, PLLP and PLC EEDs, respectively. Figure 2. 3C66A. Left-hand panels: Distributions of the parameters given by the three types of EEDs, where the dotted lines show the maximum likelihood distributions and the solid lines show the marginalized probability distributions. Right-hand panels: 2D confidence contours of the parameter correlations for the three types of EEDs, where the contours of the parameters with larger correlations are shown. The inner contours denote the 68 per cent CL, while the outer contours represent the 95 per cent CL. Figure 3. As Fig. 2 , but for PKS1424+240. Figure 4. As Fig. 2 , but for PKS1424+240 in the flare state. Figure 5. As Fig. 2 , but for PG1553+113. Table 1. Model parameters of the best fits and the marginalized 68 per cent confidence intervals (CI) for BPL EEDs. Table 2. The model parameters of the best fits and the marginalized 68 per cent confidence intervals (CI) for PLLP EEDs. Table 3. The model parameters of the best fits and the marginalized 68 per cent confidence intervals (CI) are listed for PLC EEDs. 4.1 3C66A The SED fitted by the PLC EED does not cover the Fermi /LAT bands, and χ 2 /dof is worse than that of the other two EEDs. It is found that the SED fitted by the PLC EED within the 68 per cent CL does not cover the Fermi band well compared with the other two EEDs. Hence, the PLC EED was ruled out in modelling the SEDs. We obtain that χ 2 /dof values are 1.22 and 1.20 for BPL and PLLP EEDs, and the parameters are well constrained except for α in both models. However, the flux in the 20–50 keV bands given by the PLLP EED is higher than that for the BPL EEDs, indicating that hard X-ray observations could distinguish between the two types of EED. In summary, the red shift of 3C66A is 0.16 and 0.24 from the BPL and PLLC models, respectively, and the red shift of the
Paper: 2019MNRAS.485.5285L (fulltext)
and H i emission of several radio source populations. Assuming a luminosity dependence for the fractional polarization, O’Sullivan et al. ( 2008 ) realized a simulated polarized image based on the radio source catalogue of Wilman et al. ( 2008 ). Very recently a new simulated catalogue was produced (T-RECS; Bonaldi et al. 2019 ) based on cosmological dark matter simulations to reproduce the clustering of sources and it models the radio sky both in total intensity and polarization with updated information on radio sources. Our simulation, like the above simulations, aims at giving useful information for the advent of the SKA. Similarly, it is based on cosmological RLFs integrated over cosmological volumes but the models adopted to reproduce the characteristics of the radio sources and also the procedure are in general different. In addition, alternative to the previous works, we use observed high-quality images of extended radio sources to reproduce the morphology and the spectro-polarimetric properties of the simulated radio sources. This is especially important as these simulations will be used to study magnetic fields in galaxy clusters (Loi et al. in preparation). For each simulated radio source, our catalogue lists the following parameters: type , in principle we can classify our sources in several subclasses, radio-loud or radio-quiet AGN, SFG, quasar etc. Following Novak et al. ( 2017 ) and Smolčić et al. ( 2017a ) we consider two main families depending on the mechanism that triggers the radio emission: SFG and AGN; redshift , z; size , we used the relations adopted by Wilman et al. ( 2008 ) for radio-loud AGN and SFGs. The size model are redshift-dependent and in particular the SFG size depends also on luminosity; luminosity at 1.4 GHz , we extract this information from the RLFs of Novak et al. ( 2017 ) and Smolčić et al. ( 2017a ) for the SFGs and AGNs, respectively, based on the results of the VLA−COSMOS 3 GHz Large Project (Smolčić et al. 2017b ), extrapolated to 1.4 GHz assuming the spectral index derived in combination with the the VLA−COSMOS 1.4 GHz Large and Deep Projects (Schinnerer et al. 2010 , 2007 , 2004 ); coordinates , (x, y); morphology and spectro-polarimetry properties , we select a model of radio source from a dictionary depending on its luminosity and type. Each model of this dictionary consists of four 1.4 GHz images: the surface brightness I ν in total intensity; the spectral index distribution α determined by assuming that the flux density S ν at a frequency ν is S ν ∝ ν − α ; the fractional polarization defined as the ratio between the polarized intensity and the total intensity FPOL = P / I ; the intrinsic polarization angle which is defined with respect to the Q , and U Stokes parameters as: The images of the dictionary are real high-quality high-resolution images performed at high frequency. In particular, we used VLA images at C and X bands at arcsecond resolution so that the polarization properties
Paper: 2019ApJ...875L...1E (fulltext)
and GRMHD models, as well as from measurements of the ring diameter in the image domain. Our measurements remain consistent across methodologies, algorithms, data representations, and observed data sets. Motivated by the asymmetric emission ring structures seen in the reconstructed images (Section 5 ) and the similar emission structures seen in the images from GRMHD simulations (Section 6 ), we developed a family of geometric crescent models (see, e.g., Kamruddin Dexter 2013 ) to compare directly to the visibility data. We used two distinct Bayesian-inference algorithms and demonstrate that such crescent models are statistically preferred over other comparably complex geometric models that we have explored. We find that the crescent models provide fits to the data that are statistically comparable to those of the reconstructed images presented in Section 5 , allowing us to determine the basic parameters of the crescents. The best-fit models for the asymmetric emission ring have diameters of 43 ± 0.9 μ as and fractional widths relative to the diameter of 0.5. The emission drops sharply interior to the asymmetric emission ring with the central depression having a brightness 10% of the average brightness in the ring. The diameters of the geometric crescent models measure the characteristic sizes of the emitting regions that surround the shadows and not the sizes of the shadows themselves (see, e.g., Psaltis et al. 2015 ; Johannsen et al. 2016 ; Kuramochi et al. 2018 , for potential biases). We model the crescent angular diameter d in terms of the gravitational radius and distance, , as d = αθ g , where α is a function of spin, inclination, and R high ( α ≃ 9.6–10.4 corresponds to emission from the lensed photon ring only). We calibrate α by fitting the geometric crescent models to a large number of visibility data generated from the Image Library. We can also fit the model visibilities generated from the Image Library to the M87* data, which allows us to measure θ g directly. However, such a procedure is complicated by the stochastic nature of the emission in the accretion flow (see, e.g., Kim et al. 2016 ). To account for this turbulent structure, we developed a formalism and multiple algorithms that estimate the statistics of the stochastic components using ensembles of images from individual GRMHD simulations. We find that the visibility data are not inconsistent with being a realization of many of the GRMHD simulations. We conclude that the recovered model parameters are consistent across algorithms. Finally, we extract ring diameter, width, and shape directly from reconstructed images (see Section 5 ). The results are consistent with the parameter estimates from geometric crescent models. Following the same GRMHD calibration procedure, we infer values of θ g and α for regularized maximum likelihood and CLEAN reconstructed images. Combining results from all methods, we measure emission region diameters of 42 ± 3 μ as, angular sizes of