adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019MNRAS.482.1275U (fulltext)
sample into three metallicity bins ([Fe/H] −1.5, −1.5 [Fe/H] −0.7, and [Fe/H] > −0.7) and plot the difference between CaT based and literature [Ca/H] as a function of half-mass relaxation time for each bin in Fig. 26 . For the low-metallicity bin, Kendall’s test provides weak evidence (τ = 0.26 and p = 0.07) for a correlation. Using pymc3 , we fit a significant slope () and find that a linear relationship is favoured over a constant value (ΔBIC = 9.2) for 24 GCs in the low-metallicity bin. The nuclear remnant NGC 5139 appears to be a significant outlier from the low-metallicity GC relationship. For both the 20 GCs in the intermediate-metallicity bin and the eight GCs in the high-metallicity bin, Kendall’s test provides no evidence (τ = 0.18, p = 0.27 and τ = 0.00, p = 1.0, respectively) for a correlation. The pymc3 fits finds an insignificant slope for the intermediate-metallicity subsample (), but a marginally significant slope for the metal-rich subsample (). Neither subsample shows strong evidence that a linear relationship is preferred over a constant value (ΔBIC = −5.7 and 2.3, respectively). A significant relationship between the CaT and the half-mass relaxation time only at low metallicity is consistent with the behaviour of the CaT with the slope of the stellar mass function. For both the mass function slope and the relaxation time relations, the relationship for all GCs in our sample is driven by the low-metallicity GCs. Figure 26. Differences between literature and our CaT-based [Ca/H] as a function of the half-mass radius relaxation time. From top to bottom, we show the difference for GCs with [Fe/H] −1.5, −1.5 [Fe/H] −0.7, and [Fe/H] −0.7. The black line in the top panel shows our fitted relationship between Δ[Ca/H] and the relaxation time for the lowest metallicity GCs. The colours, shapes, and size of the points are the same as in Fig. 18 . Since the slope of the mass function and the relaxation time are anticorrelated, the relaxation time increases from right to left to match the trend seen between the CaT and the mass function slope. As in Fig. 19 , the dashed line shows Δ[Ca/H] = 0, while the dotted lines show Δ[Ca/H] = ±0.1. The lowest metallicity GCs with longer relaxation times show stronger CaT strengths than GCs with shorter relaxation times and the same metallicity. However, intermediate- and high-metallicity GCs show no relationship between CaT strength and relaxation time at fixed metallicity. The observed qualitative behaviour of the CaT with the stellar mass function slope and metallicity is consistent with the predictions of stellar population synthesis models. The Vazdekis et al. ( 2003 , 2012 ) models predict that the slope of the relationship between the CaT strength and the slope of the stellar mass function as a strong function of metallicity, with a strong anticorrelation between the two at low metallicities, little or no mass function dependence at intermediate metallicities and a strong correlation at solar
Paper: 2020MNRAS.495..143C (fulltext)
and one, two, or three Faraday-thin components, each with a contribution to the polarized emission as given in equation ( 4 ), approximated to be independent of frequency over our band. We did not fit for any models with Faraday-thick components, as LOFAR HBA is minimally sensitive to these: from equation ( 3c ), the maximum scale recoverable is φ max-scale = 0.46 rad m −2 , so any polarized source with Faraday thickness exceeding this would not be properly recovered, although the edges in its Faraday spectrum might be visible if they were sufficiently sharp (Van Eck et al. 2017 ). In order to evaluate the quality of the fits across the knot and the lobe we calculate for each pair of models the Bayes factor, K , which is given by where each model M i is defined in terms of parameters θ i , is assigned a prior Pr(θ i | M i ), fits the data with likelihood Pr( D |θ i , M i ), and is supported by Bayesian evidence Pr( D | M i ). Following Kass Raftery ( 1995 ), we evaluate the Bayes factor based on the derived value 2ln K : a positive value supports model 2, constituting weak (values 2), positive (2–6), strong (6–10), or very strong (>10) evidence. Negative values of 2ln K , similarly, support model 1. Fig. 15 shows the results for the model with one Faraday-thin source. The evidence of the models with two or three Faraday-thin sources is less than the evidence for all other tested models and so is not shown. Fig. 15(a) shows the Faraday depth found for the one-source model for each pixel, where it is favoured over the null hypothesis with K > 1. Fig. 15(b) shows 2ln K > 2, where white to blue (2ln K > 2) indicates support for the one-source model and red (2ln K 2) indicates support for the null hypothesis. We find an average Faraday depth in the knot of −50.8 rad m −2 . Despite the fact that there is a significant polarized region detected at 5σ in the Faraday spectrum of the lobe, only 10 pixels are detected in the lobe with the presence of a source favoured over the null hypothesis with K > 1. The average Faraday depth of these pixels is −53.6 rad m −2 , in agreement with that measured from the Faraday spectra, with a standard deviation of 0.7 rad m −2 . The difference of 2.8 rad m −2 between the Faraday depths in the knot and the lobe is significant against this uncertainty. Figure 15. Results of the QU fitting. (a) shows the Faraday depth of the component found for a single Faraday-thin screen plus instrumental polarization. (b) The Bayes factor when comparing the fits for a single thin screen plus instrumental polarization with just instrumental polarization. Red indicates support for the null hypothesis while blue indicates support for the single Faraday-thin screen. White indicates inconclusive. There are small variations in Faraday depth of order Δφ ∼ 0.2 rad m −2 . Fig. 16 shows the distribution of the Faraday depths in the knot. The variance in the Faraday depth is rad 2 m −4 . If the variation in Faraday depth is due solely to
Paper: 2021ApJ...908L...5S (fulltext)
estimate of 5.36 ± 0.30 kpc was obtained by Thompson et al. ( 2001 ) from the detached eclipsing binary OGLEGC 17 (Kaluzny et al. 1996 ). A once challenging aspect of using Gaia parallaxes from the earlier DR2 release (Gaia Collaboration et al. 2018a ) to directly measure the parallax of ω Cen was the systematic uncertainty related to the parallax offset found by Lindegren et al. ( 2018 ), and which has been demonstrated to vary with source color, magnitude, and position (e.g., Arenou et al. 2018 ; Riess et al. 2018 ; Zinn et al. 2019 ). The DR2 offset was calibrated from quasars, which are bluer than the bulk of ω Cen stars and only cover a small fraction of their magnitude range. The DR2 parallax estimates for ω Cen by Gaia Collaboration et al. ( 2018b ) and by Shao Li ( 2019 ) are 0.1237 ± 0.0011 and 0.1368 ± 0.0015 mas, respectively, and are substantially smaller than other determinations, presumably because the parallax offset estimated by Lindegren et al. ( 2018 ) on the basis of quasar data is much smaller than the values found for brighter stars (Arenou et al. 2018 ; Riess et al. 2018 ; Zinn et al. 2019 ). Our own analysis of the DR2 parallax data for stars in ω Cen revealed a large trend of parallax with star magnitude and color, leaving us to conclude that a parallax from DR2 was not reliable without further characterization of its parallax offset. Thanks to the great improvement realized in EDR3, such a measurement is now feasible, and we exclude the DR2 parallax-based estimates from further consideration. Both ground-based and HST observations have shown that ω Cen has a complex structure, with many separate color–magnitude sequences (Bellini et al. 2009 , 2010 , 2017 ) that can have different structural (Bellini et al. 2017 , 2018 ; Calamida et al. 2017 ) and metallicity (Milone et al. 2017 ; Zennaro et al. 2019 ) properties, including an unusually broad range in helium abundance for a globular cluster. We would not expect this large range to influence the peak of the TRGB in the I -band as the mean helium abundance in ω Cen is not much affected by the high and low sub populations, and the TRGB is known to be quite insensitive to star formation and chemical abundance in general (McQuinn et al. 2019 ). The complex kinematic structure of ω Cen does not affect its parallax measurement, but needs to be taken into account in kinematics-based distance measurements (Watkins et al. 2015 ; Baumgardt et al. 2019 ). It is also worth recognizing that the distribution of abundances in the halos of galaxies where TRGB is often measured is largely unknown making comparisons difficult; it is not unlikely that the multiple populations of ω Cen could more closely resemble the complexity of a galaxy’s stellar halo than a simpler, single-population system. 5.2. Comparison with the TRGB Calibration Anchored to 47 Tuc The determination of the parallax to a globular cluster as massive as ω Cen represents a milestone of distance measurements and
Paper: 2017ApJ...850..148W (fulltext)
2003 ) and have been called “GRB-SNe” (see Hjorth Bloom 2012 ; Woosley Bloom 2006 ; Cano et al. 2017 , and references therein). While most GRB-SNe are Type Ic-BL, not all SNe Ic-BL are associated with GRBs. The most luminous SNe Ic are Type Ic superluminous supernovae (SLSNe; Quimby et al. 2011 ; Gal-Yam 2012 ); their peak luminosities are erg s −1 (Gal-Yam 2012 ), tens of times greater than those of ordinary SNe. In this paper, we study a very luminous SN Ic, SN 2011kl, which is associated with an ultralong ( s) GRB 111209A (see, e.g., Golenetskii et al. 2011 ; Palmer et al. 2011 ; Gendre et al. 2013 ; Stratta et al. 2013 ; Gompertz Fruchter 2017 ) at redshift z = 0.677 (Vreeswijk et al. 2011 ). By using WMAP ΛCDM cosmology (Spergel et al. 2003 ) and adopting km s −1 Mpc −1 , , and , Kann et al. ( 2016 ) derived a luminosity distance ( D L ) of 4076.5 Mpc for GRB 111209A/SN 2011kl. With a peak luminosity erg s −1 (Kann et al. 2016 ), 8 8 Greiner et al. ( 2015 ) found a slightly lower peak luminosity ( erg s −1 ) since they ignored the near-infrared contribution. SN 2011kl is the most luminous GRB-SN yet detected and is significantly more luminous than all other GRB-SNe, whose average peak luminosity is erg s −1 with a standard deviation of erg s −1 (Cano et al. 2017 ). The time of peak brightness of SN 2011kl is ∼14 days, slightly larger than the average (13.0 days with a standard deviation of 2.7 days; Cano et al. 2017 ) of other GRB-SNe. The peak luminosity of SN 2011kl is comparable to that of some not-quite-superluminous “SLSNe” (e.g., PTF11rks, PTF10hgi, iPTF15esb, iPTF16bad, and PS15br) and commensurate with the SN-SLSN gap transients observed by Arcavi et al. ( 2016 ); moreover, Liu et al. ( 2017c ) pointed out that the spectrum of SN 2011kl is very different from those of GRB-SNe, but consistent with that of SLSNe. So, the energy sources powering its LC might be similar to those powering the SLSN LCs instead of the ordinary SNe Ic. The most popular prevailing models for explaining SLSNe are the magnetar model (Maeda et al. 2007 ; Kasen Bildsten 2010 ; Woosley 2010 ; Chatzopoulos et al. 2012 , 2013 ; Inserra et al. 2013 ; Chen et al. 2015 ; Wang et al. 2015a , 2016b ; Dai et al. 2016 ; Moriya et al. 2017 ) and the ejecta plus circumstellar medium (CSM) interaction model (Chevalier 1982 ; Chevalier Fransson 1994 ; Chugai Danziger 1994 ; Chatzopoulos et al. 2012 , 2013 ; Liu et al. 2017a ). 9 9 Almost all SLSNe (except maybe pair-instability SLSNe—e.g., SN 2007bi Gal-Yam et al. 2009 ; but see Dessart et al. 2012 and Nicholl et al. 2013 ) cannot be explained by the radioactive heating model (Quimby et al. 2011 ; Inserra et al. 2013 ; Nicholl et al. 2013 , 2014 ). Some SLSNe I exhibiting late-time rebrightening and H α emission lines (Yan et al. 2015 , 2017 ) might be powered by a hybrid model containing contributions from magnetars and ejecta-CSM interaction (Wang et al. 2016c ). There have been several papers modeling the LCs of SN
Paper: 2020MNRAS.496.2715W (fulltext)
index increases and the energy distribution becomes steeper, limiting the number of higher energy electrons. This leads to spectra becoming more ‘thermal’ in shape, with the peak generated from the non-thermal particles flattening out. This is seen in the spectra of Fig. 10 compared to the Z LOS spectra from Fig. 7 . At earlier times, the peaks are similar, with the power-law index not increasing noticeably until later times where the system relaxes to a thermal state much faster than when the index was a constant value. The addition of an increasing power-law index therefore results in the spectral peak being reduced faster and pushed to lower frequencies as the contribution of higher energy particles is reduced. 3.2.2 Distribution along the loop The distribution of non-thermal particles within the loop is defined by equation ( 2 ). As described in Section 2.2 , l is the distance from the loop top to one footpoint, or the loop half length. The factor that controls the distribution within the loop, s , is initially set as this half loop length. As is seen in Fig. 4 the density is always highest at the apex of the loop, dropping off to a minimum at the footpoints. The parameter s was varied to investigate the effect on spectra of different spreads in non-thermal electrons within the loop. The results from using two values of s are shown in Fig. 11 . The top two panels show the spectra along the Z LOS for s parameter values of 0.3 times the half loop length (same as the Z LOS spectra in Fig. 7 ) and 1.5 times the half loop length. Figure 11. Spectra along the Z LOS resulting from changing the non-thermal electron density spatial distribution (top two plots). Two different configurations ( s = 0.3 and 1.5) are considered over the seven time-steps. The results for s = 1.0 are seen in the Z LOS spectra of Fig. 7 . Bottom images are 3D visualization of sample flux tube field lines at 4112 s and their electron distributions. Colour bar units are cm −3 . Also shown in Fig. 11 is a 3D visualization of these two different distributions along the field lines (and throughout the total flux tube) at 4112 s (bottom two panels of Fig. 11 ). For a smaller value of s , the particles are more constrained to the loop top, while a larger s value leads to a more even distribution from the apex down towards the foot points. The point of highest non-thermal density is found in the s = 0.3 plot as the total mass is conserved before the time decay component is added (equation 4 ). For the more compact distribution ( s = 0.3), in Fig. 11 it is clear the system relaxes to a thermal state quicker than for the more extended distribution of electrons and has a lower peak flux overall. However, the peak in emission occurs at 1790 s, later than the 169 s seen for the other distributions and LOSs. Overall, however, the pattern of the spectral evolution is similar. After the peak flux is reached, the decay of the non-thermal particles leads to the decrease in peak flux
Paper: 2019ApJ...871...18R (fulltext)
density and, through a gradient descent, iterate in small steps until the solution has converged. The total electron density is given by the sum of the free electrons from hydrogen, helium, and metals. We approximate this using only the first 30 elements, as others are too scarce to contribute significantly:where Z is the atomic number, A Z is the fractional abundance of element number Z , k is the ionization stage ranging from singly to fully ionized (e.g., k = 2 corresponds to doubly ionized), and Y k is the fractional population of ionization stage k of element Z . For trace elements, we solve for the ionization fractions Y k either using an equilibrium calculation that is a function of temperature or by solving a continuity equation for nonequilibrium ionization states, as detailed in, e.g., Bradshaw Mason ( 2003a , 2003b ). The nonequilibrium solver can be used with all, some, or none of the elements, as determined by the user and appropriate for the study at hand. The code makes an initial guess for the H ii fraction, calculates the rate coefficients, solves Equations ( 1 ) and ( 16 ) for the level populations, and then recalculates the total electron density. The process iterates in small steps until the electron density has converged within a defined relative tolerance (default of 10 −6 ). 2.5. Radiative Losses In previous versions of HYDRAD, the hydrogen and electron densities were essentially equal in the corona (with only a small correction for trace elements), so that the optically thin radiative losses E R (erg s −1 cm −3 ) were approximated by setting n e = n H . We drop that approximation now and use the more accuratewhere is the sum of the emissivity over all ions and all transitions for each ion (more precisely, all in the current version of CHIANTI; Del Zanna et al. 2015 ), and the minus sign indicates that the energy is lost from the system. The loss function Λ also depends on the ionization fractions and therefore also utilizes a nonequilibrium ionization calculation when desired. In the chromosphere, we calculate radiative losses following the prescription of Carlsson Leenaarts ( 2012 ), which calculates the loss of energy from hydrogen, calcium, and magnesium. We now use the ionization fraction of hydrogen determined by the above prescription in this calculation. 2.6. Performance The calculation of the NLTE level populations is a significant computational task. A few simple tests with various parameters have found that the code slows by a factor of 10–20 compared to previous versions. To offset these losses, HYDRAD has been recently parallelized with OpenMP. In Figure 3 , we briefly show a timing test of the code. We have run an example simulation of electron beam heating (see the next section) with energy flux F 0 = 3 × 10 9 erg s −1 cm −2 for 10 s of heating. The simulations were only run for 100 s of simulation time. The timing of the simulation run with the older version of the chromosphere is shown in red, compared
Paper: 2018MNRAS.479.3335I (fulltext)
by observations of polarised optical radiation. However, polarization signatures are only detected in a limited number of sources, and at present there is no compelling evidence for a simple obscuring torus geometry, possibly suggesting a more complex picture (see the recent review by Netzer 2015 , and references therein). The different AGN obscuration models discussed so far may be broadly divided into two categories: static and dynamical. For instance, analytic calculations suggest that the vertical support for a geometrically thick obscuring torus can be provided by infrared radiation pressure (Krolik 2007 ). The different nature of smooth and clumpy dust tori in different AGN luminosity regimes has been analysed by Hönig Beckert ( 2007 ). Circulation of gas leading to the development of radiation-driven ‘fountains’ has also been proposed as a possible mechanism for the formation of the obscuring torus (Wada 2012 ). The detailed evolution of dusty gas clouds illuminated by the central AGN has been followed through 3D radiation hydrodynamic (RHD) simulations (Namekata, Umemura Hasegawa 2014 ); while the dynamics of dusty gas subject to anisotropic disc emission, X-ray heating, and reprocessed IR radiation, has also been analysed via axisymmetric RHD simulations (Namekata Umemura 2016 ). Another class of models consider the obscuring toroidal structure as supported by winds driven by IR radiation pressure, due to the relatively high opacity of dust in the infrared (Dorodnitsyn, Bisnovatyi-Kogan Kallman 2011 ). A further refinement of the model based on multi-dimensional RHD simulations confirms that the geometrically thick obscuration on pc-scales can indeed be provided by outflows driven by IR radiation pressure on dust (Dorodnitsyn, Kallman Bisnovatyi-Kogan 2012 ). On the other hand, 3D time-dependent RHD simulations of dusty tori suggest that UV radiation pressure on dust can drive powerful winds with considerable mass loss rates (Chan Krolik 2016 ). The most recent multidimensional simulations of dusty gas acceleration in star-forming galaxies suggest that dusty clouds are able to survive longer when accelerated by radiation pressure rather than entrained in a hot outflow (Zhang et al. 2018 ). Here, we envisage a simple picture in which AGN radiative feedback, driven by radiation pressure on dust, strongly affects the distribution of dusty gas in the host galaxy. We assume a spherical shell geometry throughout the paper, while we separately discuss the case of anisotropic emission from the accretion disc in Section 5 . Our results are in agreement with the observational data based on the 70 month Swift /BAT AGN catalogue, which is the most complete sample of X-ray selected AGNs, with measured N H and λ values, currently available. This suggests that our model assumptions are reasonable and sufficient for the purpose of comparison with X-ray observations. In summary, the overall picture is entirely consistent with AGN radiation pressure
Paper: 2016ApJ...830..101B (fulltext)
IRIS detected quasi-periodic fluctuations in the relative Doppler velocities of the C i , Si iv , and O iv lines from its pointing position about 40′′ east of the EIS slit's position, within the same ribbon. This may be because IRIS observed that portion of the ribbon to undergo quasi-periodic fluctuations before the onset of explosive chromospheric evaporation, when the amplitudes of velocity fluctuations may simply have been more readily detected. Other contributing factors may be that IRIS has higher spectral resolution, and spatial resolution nearly 10 times better than that of EIS. The IRIS observations revealed, however, that quasi-periodic fluctuations in the redshifted relative Doppler velocities were not as pronounced as those in the intensities (Brosius Daw 2015 ). The observed O iv , Mg vi , Mg vii , Si vii , Fe xiv , and Fe xvi fluctuations provide evidence for quasi-periodic energy injections into the chromosphere (at the location of the flare ribbon observed by EIS) by accelerated, nonthermal particles. This is similar to the conclusion drawn by Brosius Daw ( 2015 ) regarding fluctuations in C i , Si iv , and O iv at the location of the flare ribbon observed by IRIS , all of which occurred before the appearance of Fe xxi . In that work, each episode was thought of as a failed flare because no emission at temperatures characteristic of flares (Fe xxi emission) was produced. The quasi-periodic injections of nonthermal beam particles could be produced by MHD oscillations in a magnetic trap at the site of the initial flare energy release (by reconnection). In this scenario (Zaitsev Stepanov 1982 ), MHD oscillations in the trap are generated by the impulsive energy release itself, and modulate the periodic escape of particles therefrom. Alternatively, the injections could be driven by MHD oscillations in a nearby, nonflaring magnetic loop (Nakariakov et al. 2006 ). In this scenario, the oscillating loop perturbs the plasma external to the loop, which produces variations of the electric current density in the flaring region. These induce plasma micro-instabilities and, hence, anomalous resistivity in the flaring region, which can quasi-periodically trigger the magnetic reconnection events that accelerate nonthermal particles. Yet again, the injections could be supplied by quasi-periodic particle acceleration episodes during dynamic magnetic reconnection in a large-scale current sheet dominated by repeated formation of magnetic islands (Kliem et al. 2000 ). In this scenario, particles are presumably accelerated by DC electric fields near the reconnection X points. Recently, Guidoni et al. ( 2016 ) extended the approach of Drake et al. ( 2010 ; see also Karpen et al. 2012 ) to investigate electron acceleration in sunward-moving magnetic islands (plasmoids) formed by spatially and temporally variable reconnection in a flare current sheet. Their 2.5D simulations of an eruptive flare revealed that energy gains in a single island