adsabs/WIESP2022-NER huggingface dataset:
Paper: 2016A&A...595A..85W (fulltext)
spectroscopy leads to more detailed models being needed to fully describe the data. Our knowledge of many aspects of these models is not yet complete (for example the possible presence of additional astrophysical components in the line of sight towards the source and imperfect atomic data) and we also have to deal with imperfect instrumental calibration, systematic effects and statistical fluctuations, so there will necessarily be differences between the data and the best physical model that can be found. As discussed in Blustin et al. (2002) , the use of goodness-of-fit statistics when modelling RGS data must be coupled with careful consideration of the physical meaning of the models, as physical self-consistency is more important than reaching a nominally “acceptable” goodness-of-fit value. 3.1. Testing the 2012–13 stacked spectrum with continuum model and Gaussian lines As Braito et al. (2014) have previously analysed the Jan. 2013 observation (Obs. 3, the least absorbed of the four 2012–13 observations), we began our analysis using their continuum and absorption model. Their use of EPIC data allows much better contraints of the continuum than is possible using just the RGS data. As observation 3 has lower absorption than the other 2012–13 observations, it reveals ionised absorber phases not previously seen. For this we used a power law model, absorbed by three ionised absorbers ( of 3.77, 2.15 and 0.17, as in their Table 4). We allowed our power law normalisation to vary, and fixed to a value of 2.1 to match their EPIC fits. At this stage we have not fitted any of the emission lines, so have an unacceptable -statistic value of 7000. Table 2 Narrow emission line best fit intensities from the 2012–2013 stacked spectrum and 2004–2007 stacked spectrum. Notes. A star marks uncertainty in the assigned transition. Overall -statistic 1445 (2012–13) and 1548 (2004–07). (a) r, i and f represent the resonance, intercombination and forbidden lines of an He-like triplet, respectively. Fig. 1 2012–13 RGS spectrum and line labels for detected emission lines. r, i and f represent the resonance, intercombination and forbidden lines of a He-like triplet, respectively. A * indicates uncertainty in the line identification. The data are binned by two for display purposes. The data are shown in black, and the model in red. For details of the model shown here, see Sect. 3.5 . We then fitted Gaussian lines to the data, adding a Gaussian if the best fit -statistic improves by , corresponding to a 90% confidence level for three extra free parameters (normalisation, width and central wavelength of each Gaussian model). These Gaussian models are only absorbed by our Galactic , and none of the absorption intrinsic to NGC 1365. For the He-like triplets in this wavelength range we included three components (resonance, intercombination and forbidden) if at least one was detected at the 90% level. These “triplets” really include four components, but at RGS resolution
Paper: 2019A&A...625L...5L (fulltext)
molecules released from the nucleus and an additional source of D-poor water molecules from sublimating icy grains (see dash-dotted line in Fig. 2 ). Laboratory experiments on samples of pure ice show small deuterium fractionation effects ( Lécuyer et al. 2017 ). In experiments with water ice mixed with dust, the released water vapor is depleted in deuterium, explained by preferential adsorption of HDO on dust grains ( Moores et al. 2012 ). This effect goes in the opposite direction to the observed trend, while conversely the VSMOW D/H value from subliming icy grains is likely representative of buried nucleus water ice. Alternatively, a non-steady-state regime of water ice sublimation could explain the factor 2 − 3 deuterium enrichment in water vapor released from the nucleus with respect to VSMOW ( Podolak et al. 2002 ). However, this would occur in episodic time intervals along the comet orbit ( Podolak et al. 2002 ), whereas the HDO/H 2 O values measured for comet 67P at different periods are very uniform ( Altwegg et al. 2017 ). 5. Conclusion Enlarging the number of accurate D/H measurements in both Jupiter-family and Oort cloud comets is required to better constrain the observed correlation. Taking these measurements from the ground is challenging. Nearly simultaneous spectroscopic observations of low-energy rotational lines of and HDO in a matching field of view encompassing a large fraction of the coma using the GREAT spectrometer aboard SOFIA can play a key role in this endeavor. In this context, the next close apparition of comet 67P/Churyumov-Gerasimenko in 2021 November will offer an excellent opportunity to re-measure the D/H ratio in this comet using spectroscopic techniques. The understanding of the observed correlation calls for detailed investigations of the mechanisms leading to dust and chunk ejection, and cometary hyperactivity. Further experimental and modeling work on evaporative fractionation is also clearly needed, and may ultimately establish that all comets share the same Earth-like water D/H ratio, with profound implications on the early solar system and the origin of the Earth’s oceans. Appendix A: Observations and modeling A.1. Observations The observations of comet 46P/Wirtanen reported here were carried out using the GREAT heterodyne spectrometer ( Heyminck et al. 2012 ) aboard SOFIA during five flights between 2018 December 14 and 20 UT, out of Palmdale, CA, USA. The instrument was operated in its upGREAT HFA/4GREAT (HFA/4G) configuration ( Risacher et al. 2018 ; Durán et al. 2017 ), which allows simultaneous observations at five different frequencies. The lowest frequency band, 4G-1, was used for the observations reported here. The tuning setup and the basic instrument characteristics are summarized in Table A.1 . Although several other transitions of interest were covered in the higher-frequency channels, only OH was detected at a low signal-to-noise ratio, and the other upper limits are not constraining
Paper: 2016ApJ...832..170P (fulltext)
we cannot trust them since these very large parameters produce synthetic profiles with a significant portion being out of the observed spectral range (Figure 15 ). The inversion code combined a strongly shifted two-lobed Stokes V profile with an unshifted one to recreate the observed three-lobed Stokes V profile, without any information about what the profiles would look like outside the spectral range. Figure 14. Right panels: observed and best-fit Stokes profiles (black and red, respectively). Left panels: atmospheric parameters inferred for the fast (blue) and the slow components (magenta) from the inversion. The filling factor of the fast component is shown in the lower left corner of the top panel. Figure 15. Synthetic profiles resulting from the fast and the slow atmospheres (blue and magenta lines, respectively) plotted in Figure 14 . The gray line shows the blue one multiplied by a scale factor for better visibility. As a reference, the observed and best-fit profiles are also shown here in black and red, respectively. The dotted vertical lines delimit the observed spectral range. To solve this situation, we decided to repeat the inversion considering as initial models the atmospheric stratifications obtained from a nearby pixel unaffected by this problem whose Stokes V profile is similar. The results are shown in Figure 16 . Although the fit is not as good as in the previous inversion, it is still reasonable. The inversion now finds strongly shifted and reversed two-lobed Stokes V profile, together with an unshifted profile, whose combination reproduces the observed spectral signatures, being a more reliable solution. Figure 16. Best-fit profiles and atmospheric parameters inferred from an inversion of the same pixel as in Figure 14 considering as initial models the results from a close pixel with a similar Stokes V profile. The layout is the same as in Figure 14 . Although these structures resemble Evershed clouds (e.g., Rimmele 1994 ; Shine et al. 1994 ; Cabrera Solana et al. 2006 , 2007 ), we do not find any indication of two velocity packets inside the patch traveling outward as a whole. In addition, Cabrera Solana et al. ( 2007 ) found that the two Evershed cloud packets have opposite magnetic polarities when they cross the sunspot border, which has not been found in our observations. A number of physical mechanisms are known to be able to drive supersonic flows in the penumbra, including siphon flows (Thomas 1988 ) and moving magnetic tubes (Schlichenmaier 2002 ). Both mechanisms might well explain our observations, but a detailed comparison is difficult because the theoretical results are based on idealized models that are often simplistic and do not consider the actual conditions of the penumbra (in particular, the fact that it is a stratified atmosphere with temperatures, pressures, and magnetic fields varying both vertically and horizontally). The 3D radiative MHD simulations of Rempel ( 2012 ), on the other hand, show
Paper: 2018ApJ...856..176G (acknowledgments)
This research is partly supported by the following grants: Royal Society-Newton Mobility grants NI160149, CIC-UMSNH 4.9, and CONACyT 258726 (Fondo Sectorial de Investigación para la Educación). The simulations were carried out in the facilities of the CESCE-UNAM, Iceberg HPC Cluster and the Big Mamma cluster at the LIASC-IFM. V.F. and G.V. thank the STFC for their financial support. Visualization of the simulations data was done with the use of the VisIt software package.
Paper: 2018A&A...612A..69G (fulltext)
range to obtain the constant horizontal polarisation pattern we are looking for. If these structures have an optical depth that is too low or too high, the central belt shows multiple scatterings as seen in both Figs. 12 and 13 . In extreme cases with very high optical depth in the outer torus, we have no photons in this region, a case close to the simulations of model 2 (see Fig. 7 ). These two conditions seem to be important to produce the features we are expecting. A last remark is that the photons detected from the ionisation cone are no longer just scattered once at high optical depth in the cone ( Fig. 12 ). However, the centrosymmetric pattern seems to be approximatively conserved for double scattering in the cone. 4.4 Reproducing the NGC 1068 observations Based on previous simulations, we tried to reproduce the SPHERE observations of NGC 1068. As we observed constant polarisation orientation in the core of NGC 1068 in both the H and Ks bands, we need in our simulation an optical depth τ ≥ 20 at 2.2 μ m to be able to generate such polarisation features. We changed our previous model 5 to adapt it to these larger wavelengths. Here, the torus has an optical depth τ Ks = 19, which gives τ H = 35 and τ V =169. We note that this is in therange of the current estimation of the density of dust in the torus (see Sect. 5.6 ). For this simulation we used a mixture of silicates and graphites to rely on a realistic dust composition for an AGN, inspired by Wolf Henning (1999) . We adapted their mass ratio to a number ratio according to Guillet (2008) leading to 57% silicates, 28.67% parallel graphites, and 14.33% orthogonal graphites. With these parameters, the maps in Fig. 14 are now able to reproduce some of the observed features. Namely, the nearly constant horizontal polarisation in the central part is similar to that observed on NGC 1068 at 1.6 and 2.2 μ m. We are, however, not yet able to reproduce the ridge that appears at the very centre of the SPHERE observations. Fig. 14 Maps of the observed averaged number of scattering with an inclination angle of 90 ° at 1.6 ( top ) and 2.2 μ m ( bottom ). Polarisation vectors are shown, their lengths being proportional to the polarisation degree and their position angles representing the polarisation angle. 5 Discussion 5.1 Photons significance One major concern is the simulation strategy of conserving the packets until they exit the simulation box, whatever their number of interactions.We must verify to what extent the detected photons are relevant. The energy of each packet is decreased according to the albedo at each interaction, as is the number of photons in the packet. Therefore, it is common at the end to record photon packets with energies 10 4 times lower than the initial energy. If a high energy packet reaches a pixel previously populated only with low energy ones, it will totally dominate the pixel so that Q and U values and the polarisation parameters will be essentially linked to this
Paper: 2018A&A...616A..87M (fulltext)
free of atmospheric perturbations, regularly produced by the 50 cm Solar Optical Telescope (SOT) on-board the satellite HINODE between November 2006 and February 2016, are now available. These images have been used in this paper to investigate the variation of the solar granulation over the activity cycle with more accuracy than before. After a presentation of the HINODE/SOT synoptic program in Sect. 2 , the method of analysis of the blue continuum images is described in Sect. 3 . The results are presented in Sect. 4 and discussed in Sect. 5 . 2. Observations: HINODE/SOT synoptic program The HINODE synoptic program provided broadband images obtained at the disk center with the Optical Solar Telescope (e.g., Tsuneta et al. 2009 ). They were taken a few seconds apart in blue, green and red continuum windows at 450.45, 555.05, 668.40 nm respectively, in the G -band lines of the CH molecule at 430.50 nm, in the CN band head at 388.35 nm and in the Ca II H line at 396.85 nm, in general once or twice a day. In this work we have only used the blue continuum images. During the observations, the focus position was controlled in the G -band; when optimized in this wavelength, it is also optimized in the nearby blue continuum 0.4 nm window. But unfortunately, this set of images is not as homogeneous as expected, because a significant number of them appear to be blurred to a varying degree; the cause of blurring may be inaccurate focusing or/and optical aberrations as one will see in Sect. 4 . Consequently, great care is taken in this investigation to only select the sharpest images, in order to get the most homogeneous set of high resolution granulation images currently available, spanning nearly one solar cycle (although the minimum of activity between cycle 23 and cycle 24 was longer than usual). Before February 2008, when a serious telemetric problem occured, between two and four images were taken every day in each wavelength; after that month and until September 2009 the cadence was reduced to about one image taken every two days, but then on increased again to a cadence of about one observation per day. Two adjacent cameras with 2048 × 2048 square pixels of nominal scale 0′′̣055, took rectangular images of nominal size 228′′ × 114′′. We have taken into account the annual variation of the Earth-Sun distance by changing the pixel scale accordingly. After the telemetric problems which occurred in January 2008, the images of the synoptic program were binned by a factor of two. Fortunately, this is not a problem in this work, since the images taken before this date were binned by a factor of two for analysis. So the size of all the analyzed images is 2048 × 1024 pixels. The program ended in February 2016, when both cameras broke down. All images were calibrated with the standard solar reduction software distributed under SSWIDL, which corrects for bad pixels, readout errors, dark current and flat field. A 1024 × 512 pixel subfield of a calibrated image is
Paper: 2019ApJ...886...25B (fulltext)
that model A is strongly favored by the data, while model B is preferred for . It is somewhat indicative to relate values of the odds ratio to σ -levels of Gaussian measurements:where erf is the error function. 4.1.1. Mock Observations We want to assess the number of observations N obs needed to discriminate between the two models, assuming that one of them is a faithful representation of reality. To understand how each parameter impacts the analysis, we run our statistical pipeline assuming we only measure either , , or . In practice, this implies that the integral in Equation ( 18 ) is evaluated on the selected variables while marginalizing over the others. Regarding the production of mock data: we generated 10 3 sets of observations for each value of N obs , in order to produce a statistical estimation on our results. Each of the mock observations was sampled from the model and included in the observation set with probability p det . In Figure 2 , we show values of the odds ratio O AB where the set of observed events is generated from model A, so that O AB is expected to increase as N obs grows. We present results for observations generated from the dynamical (orange) and isolated (blue) models, both for aLIGO/aVirgo (left panels) and ET (right panels). Figure 2. Evolution of the odds ratio as a function of the number of simulated observations generated from the dynamical (orange) and isolated model (blue). The odds ratios are defined such that the posterior of the true model is at the denominator. Equivalent σ levels are reported with dotted lines. The four rows correspond to analysis done with M t , q , , and (from top to bottom); the left and right panels refer to aLIGO/aVirgo and ET, respectively. For each scenario, the thick lines are the median values, while the surfaces represent the 90% credible regions. Let us focus first on the top row, where only the total mass is considered in the analysis. In the case in which the dynamical model is assumed to be true (orange) with aLIGO/aVirgo, the upper limit of the 90% credible interval presents high values even for small N obs . This is because the isolated model only predicts merging BBHs with M t 70 M ⊙ . As a result, any observation where the entire support of the total mass posterior distribution is above 70 M ⊙ can only be described by the dynamical model. Another interesting feature is that the lower bound of the credible interval has values of starting at , which is consistent with our models. In fact, as the number of dynamical BBHs with M t > 70 M ⊙ in our dynamical catalog is equal to 16 (out of 229), the probability of not observing any massive BBHs in 50 observations generated from the dynamical model is equal to p = 1.9 × 10 −3 . It is also interesting to see that the results obtained are similar when observing with ET, suggesting that the differences in total mass range of our models is the dominant effect in the analysis. In contrast, the evolution of the odds ratio for
Paper: 2017ApJ...834..183M (acknowledgments)
We would like to acknowledge the pioneering work of Adam Burrows, Hans-Thomas Janka, and Matthias Liebendörfer, whose contributions to CCSN theory were directly inspirational to the work presented here. This material is based upon work supported by the National Science Foundation under Grant No. 1313036.
Paper: 2016ApJ...821...53V (acknowledgments)
The figure production made use of the PGPLOT software at NRC Canada in Victoria, BC. I thank the referee for clarification.
Paper: 2016ApJ...833...55L (acknowledgments)
J.L. acknowledges support from the Research Grants Council of Hong Kong through grant HKU 703512P.
Paper: 2016ApJ...827..135T (fulltext)
cooling break signature does not appear in the spectrum. Since some young PWNe have a soft injection spectrum of p 2 ∼ −3.0, we also study such PWNe. Figure 2 shows model spectra for the small magnetic field cases ( B , p 2 ) = (3 μ G, −3.0). The braking index is different for the left ( n = 3) and the right ( n = 2) panels, and then we search for the value of γ b for the calculated X-ray flux to be close to F O11 . In both cases, a large TeV γ -ray flux is predicted when compared with Figure 1 without increasing the energy density of the ISRF ( u IR , u OPT ). 4. DISCUSSION AND CONCLUSIONS Considering the plasma outflows from the magnetar, we build a broadband emission model of MWNe based on the one-zone model of young PWNe around RPPs ( TT10 , TT11 , TT13b ). The model is simplified for application to MWNe that have less observational information than PWNe. We apply the model to the MWN around 1E 1547.0-5408, which is the most promising object for detecting MWNe among all the known magnetars because of its large L spin and its small distance to the Earth. Because of poor current observational constraints on the MWN around 1E 1547.0-5408, various combinations of parameters are allowed. However, here, we compare Models 1 and 2 based on past studies of young PWNe, especially focusing on Kes 75 around PSR J1846-0258, which is the only HBP showing a magnetar-like behavior (Gavriil et al. 2008 ). Model 1 has η and γ b similar to Kes 75, while Model 2 has a similar magnetic field strength B . It is known that the mean magnetic field B is very different for each young PWN 3 ≲ B ≲ 80 μ G (e.g., TT13b ; Torres et al. 2014 ). On the other hand , TT11 and TT13b showed that η ∼ 10 −3 is common among young PWNe, except for Kes 75, by using almost the same model as this study, i.e., adopting ( α R , α B ) = (1.0, −1.5) in Equations ( 7 ), ( 8 ), and ( 10 ). Kes 75 has an exceptionally small η compared to other young PWNe for our model and also for other models, although the values of η are different among models (Bucciantini et al. 2011 ; Torres et al. 2014 ). We favor Model 1 over Model 2 because we expect that magnetars have the wind property η and γ b similar to HBPs, where γ b is related to the bulk Lorentz factor of the wind just upstream the termination shock (e.g., Kennel Coroniti 1984 ; Bucciantini et al. 2011 ). Models 3 and 4 are cases for the soft injection spectrum p 2 = −3 and the large break Lorentz factor ( γ b ≫ 10 6 ), although such γ b and p 2 are not typical among young PWNe ( TT13b ). Both models are interesting because they predict a larger TeV γ -ray flux than Model 1 without increasing the local ISRF ( u IR , u OPT ). For example, the γ -ray flux increases if the photons from the nearby star-forming region contribute to the local ISRF ( Torres et al. 2014 ). Model 3 is brighter than Model 2 in TeV γ -rays because the injection of the high energy particles is an increasing function of γ b . Model 4 predicts larger TeV γ -ray flux