adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018ApJ...856...58H (fulltext)
linear relation and the deviation is about 4%. Furthermore, Hansen et al. ( 2014 ) and Niu et al. ( 2015 ) found that the abundance patterns and the decreasing trends of the weak r-process abundances versus atomic number are robust. In this work, using the decreasing relation, we estimate the abundances of the weak r- and main r-process for the elements As and Se, which are listed in Table 1 . For comparison, the corresponding r-process abundances in the solar system (Sneden et al. 2008 ) are also listed. Table 1 Main r-process and Weak r-process Abundances of As and Se Adopted in this Work Note. a Sneden et al. ( 2008 ). The five superposition coefficients in Equation ( 1 ) can be derived by comparing the calculated abundance N i, cal with the observed abundance N i, obs and seeking the smallest χ 2 which is calculated aswhere is the observed uncertainty. Thirty-eight elements are used in the calculations, thus K = 38. The value of K free is five, which means five superposition coefficients. All observed abundances of the star HD 94028 are taken from Roederer et al. ( 2016 ). The abundances derived from neutral lines are adopted for the light neutron-capture elements Mo and Cd. For the best-fitting results, the value of χ 2 is 0.887 for case A and 0.938 for case B. The calculated superposition coefficients for case A are C r , m = 1.50, C pri = 3.90, C s , m = 1.84, , and C Ia = 0. For case B, these coefficients are C r , m =1.49, C pri = 4.10, C s , m = 1.82, , and C Ia = 0. The coefficients C r,m , C pri , and C s,m are larger than 1.0, which means that the fractions contributed by the main r-, primary, and main s-process for HD 94028 are larger than those of the solar system. On the other hand, C sec is smaller than 1.0, which implies that the fraction contributed by the secondary process for this star is smaller than that of the solar system. The coefficient C Ia is 0, which is consistent with the prediction of Kobayashi et al. ( 1998 ) that SNe Ia did not occur for the progenitors with [Fe/H] ≤ −1.0. Direct comparisons of calculated and observed abundances for case A and case B are plotted in Figures 1 (a) and (b), respectively. In the upper panels, the solid lines are the calculated abundances and the filled circles are the observed abundances of HD 94028. The lower panels show the related offsets () and the corresponding observed uncertainties. From Figures 1 (a) and (b), we can see that, for most elements, the calculations are consistent with the observations within the uncertainties. Comparisons between the individual component abundances and the observed abundances in HD 94028 are shown in Figures 2 (a) and (b). Figure 1. Upper: comparisons of calculated results (solid lines) with the observed abundances (filled circles). The observed upper limits are represented by circles with downward arrows. The data are taken from Roederer et al. ( 2016 ). Lower: the relative offsets ( ) and the observed uncertainties. Figure 2. Abundance
Paper: 2019ApJS..245...21S (fulltext)
shift for all sources in each track, the task uti _ doppler _ fix is then called. Applying this calibration technique is straightforward and is described in the MIR cookbook. We applied this new correction to every calibrated data set for the MASSES program, including the previously published data described in Paper I . The effect of the correction is significant for Perseus protostars given the relatively narrow lines (on order of 1 km s −1 ). We found that over the course of a track for Perseus, the Doppler velocity correction for each integration could amount to differences of up to ∼1 km s −1 . In comparison of pre- and post-correction data at locations where the spectral lines are bright, flux differences in each channel of up to 50% were not uncommon. The correction, by removing the effects of the smearing, significantly improved the sharpness of all narrow spectral line features, allowing for easier identification of weak narrow lines and higher signal-to-noise ratio for stronger ones. In Figure 1 , the products before and after applying the Doppler correction fix are shown for C 18 O(2–1) spectra at a single pixel for Per-emb-2 and Per-emb-40. It is apparent that without the fix, the velocity has a significant shift, and the spectral features are smoothed. Because the real-time error was not identified until after the publication of Paper I , the results presented there were adversely affected; we stress that the data presented in this paper improves on and supersedes that presented in Paper I . After the Doppler shift correction was applied, all data were converted to MIRIAD format (Sault et al. 1995 ). Figure 1. Primary-beam-corrected C 18 O(2–1) spectra, taken at the 1.3 mm continuum peak pixel for Per-emb-2 (top panel) and Per-emb-40 (bottom panel). The blue spectra show what the spectra looked before the fix, while the red spectra show after the fix. 3.2. Data Imaging All imaging was done using MIRIAD and is also described in detail in Paper I . We repeat most of the details here, as there are a couple of minor differences which will be specified below. For spectral lines, the continuum is first subtracted using task uvlin , where a range of line-free channels (visually identified) is specified by the user. The task fits a DC offset to these channels and subtracts the offset from the window. For both continuum and spectral lines, an inverse Fourier transform was used on all visibilities (i.e., specifying all tracks for each target as specified in Table 1 ) to create a dirty image using the MIRIAD task invert and specifying options “systemp” and “double.” During the invert task, we create two data products of different resolutions by specifying Brigg’s robust parameters of both −1 and 1. This differs from the subcompact release (Paper I ), where we only delivered products that use robust = 1. A robust parameter of 2 is similar to natural weighting, which minimizes the noise in the image, while a robust parameter of −2 is
Paper: 2017MNRAS.471..523W (fulltext)
surface mass density; D L , D S and D LS are the angular diameter distances from the observer to the lens, to the source and from the lens to the source, respectively. We can get some simple but important results about the lensing efficiency for the DC14 model even before calculating the lensing probabilities. We notice that, from equation ( 2 ), NFW profile is a specific form that has (, , )(1, 3, 1), and SIS profile is similar to (, , )(2, 2, 2), where in the latter case, can be any non-zero number and we let 2 for definiteness. Therefore, it would be helpful to plot the parameters , and versus M halo from 10 11 to 10 14.9 M , as shown in Fig. 1 . We find that the inner slope has 1, an NFW like value, only in a narrow range of halo mass around 10 12 M , and it flattens for lower and higher mass ranges. This would result in a much lower lensing efficiency compared with that of NFW model and even further lower compared with SIS model. Figure 1. The parameters of , and as functions of M halo . Another parameter that helps us to understand the lensing efficiency is s in lensing equation ( 10 ). According to strong lensing theory, multiple images can occur only for sufficiently large values of s . Fig. 2 shows how s changes with M halo for both DC14 and NFW models, with a fixed typical value 0.45 of the lens redshift and the average value 1.56 of the source redshift (Inada etal. 2012 ). We find that the values of s for NFW model uniformly surpass that of DC14 in the whole range of halo mass of (10 11 , 10 14.9 M ), and the difference increases markedly for M halo 10 12 M . This would also mean an obviously lower lensing efficiency for the DC14 model compared with the NFW model. Figure 2. The parameter s as a function of M halo for NFW (dotted line) and DC14 (solid line). In both cases, the redshifts of source and lens are set to be 1.56 and 0.45, respectively. Figure 3. The relation between c and M 200 . Figure 4. The relation between i and M 200 . Figure 5. The relation between r s and M 200 . 2.2 <xref ref-type="bibr" rid="bib77">Schaller15</xref> model The Schaller15 model is derived from EAGLE simulations; the total (baryons plus DM) density profile consists of two terms (Schaller etal. 2015b ): where the first term is the NFW profile and the second term is NFW-like in that it shares the same asymptotic behaviour at small and large radii and has a slope of 2 at its scaleradius, r r i . We write the surface mass densities corresponding to the two terms as where and with For later calculations, we need to know the characteristic densities c and i , and characteristic radii r s and r i as functions of the total mass M 200 , which reads (Schaller etal. 2015b ) In practice, however, it is impossible to derive so many parameters from the only equation ( 16 ), we thus fit c etc. to M 200 with the data given by Schaller etal. ( 2015b ). The fitted results are displayed in Figs 3 5 . The fitting formulae are where X log10( M 200 ), and ca 0.06, cb
Paper: 2016A&A...589A..98G (fulltext)
we also define . We chose three different statistics: (). This statistic picks up the maximum deviation from the continuum spectrum for each simulated PDS. The observed value is then compared with the simulated distribution and the significance is evaluated directly. By construction, it implicitly accounts for the multitrial search performed all over the frequencies. is the Anderson–Darling (AD) statistic ( Anderson Darling 1952 ) obtained for the th set of compared with a distribution. Analogously, KS is the Kolmogorov–Smirnov (KS) statistic obtained for the th set of compared with a distribution. For each of the three statistics, comparing the values obtained from the observed PDS with the corresponding distribution of simulated values immediately yields the significance of possible deviations such as that of a QPO, or the goodness of the fit, as indicated by the AD and KS statistics. As in G12, in addition to the KS, we chose the AD statistic because it is sensitive to a few outliers from the expected distribution. For each GRB the choice between the two competing models was determined by the likelihood ratio test (LRT) in the Bayesian implementation described by V10. As for the aforementioned statistics, from the posterior predictive distribution we sampled the pdf of the statistic defined as where denotes the model obtained with the parameters at the mode of the posterior distribution of a generic model . The statistics in Eq. ( A.9 ) is then sampled using the simulated PDS () and compared with the observed value. For the LRT test, we performed simulations and accepted the bpl model when the probability of chance improvement was lower than 1% (see Sect. 2.3 ). As remarked in Sect. 2.3 , while this type of LRT accounts for the whole parameter space of the simpler model, the same does not hold for the alternative one, as is the case when we use the Bayes factor. Consequently, the rejection of pl does not necessarily imply that bpl is a better option. However, for the limited scope of this investigation, on independent grounds bpl represents a sensible alternative (Sect. A.1.1 ). Finally, after we determined the best–fitting model, we sampled through the MCMC simulation the joint posterior distribution of the model parameters and provided mean and standard deviation for each of them. As an example, Fig. A.1 shows the marginal posterior distributions of all the pairs of parameters of the bpl model for 050713A. Fig. A.1 Example of marginal posterior distributions for the pairs of parameters of the bpl model obtained from simulated posterior simulations for 050713A (only 2000 points are shown for clarity). Solid lines show the contour levels. The goodness of fit is established by the -values associated to the AD and KS statistics. In addition, we also compare the distribution of of the observed PDS against the expected distribution, as shown in Fig. A.2 for 050713A. Fig. A.2 Distribution of for 050713A (Fig. 1 ). The dashed line shows the
Paper: 2020ApJ...895..113T (fulltext)
from 108 full-sky maps (Takahashi et al. 2017 ; Namikawa et al. 2019 ). The error bars are the standard deviations scaled by . The solid red, dashed orange, and dotted brown curves are the theoretical predictions based on BiHalofit, GM12 , and SC01 , respectively. The middle panels plot the relative deviations from the red curves: . In the bottom panels, these deviations are further divided by the relative standard deviation (). Although our fitting surely improved the prediction accuracy, noticeable discrepancies from the simulations were introduced by several sources. First, the finite thickness of the lens planes employed in the ray-tracing simulations may affect the simulations at l 200 (the same effect on convergence PS is demonstrated in Figure 10 of Takahashi et al. 2017 ). Second, the flat-sky formula in Equation ( 16 ) is inaccurate at large angular scales (the accuracy of the flat-sky approximation in the cosmic-shear PS is detailed in Kilbinger et al. 2017 ; Kitching et al. 2017 ). For example, in the squeezed limit, the minimum multipole is fixed as l 3 = 50, but a larger l 3 can mitigate the discrepancy (Namikawa et al. 2019 ). Since reducing the finite thickness of lens planes requires more numerically expensive simulations, we will leave the detailed study for future work. 6.2. Cosmic Shear Let us now consider the cosmic-shear signals in galaxy-shape measurements, which probe lower redshifts than CMB lensing. Sato et al. ( 2009 ) ran cosmological N -body simulations and subsequent ray-tracing simulations under the flat-sky approximation. Despite their small field of view (5 × 5 deg 2 ), they acquired sufficiently many weak-lensing maps (1000) for an accurate BS measurement. Their cosmological model was consistent with the WMAP 3 yr result (Spergel et al. 2007 ). Figure 13 plots the convergence BS at a source redshift of z s = 1, measured from the 1000 maps by Kayo et al. ( 2013 ). The theoretical and simulation results were consistently binned with . The simulation results were valid (within 5% error) up to l ≃ 4000, as confirmed by comparing the results with those from low- and high-resolution maps (Sato et al. 2009 ; Valageas et al. 2012 ). Overall, the plot shows a similar trend to the matter BS at z = 0.55 (see Figure 5 ). Our fitting formula well agreed with the simulation (within 10% level up to l = 4000). The deviation at small scales ( l ≳ 4000) was attributed to the limited resolution of the simulation. Figure 13. Convergence BSs measured from 1000 simulation maps at the source redshift (Sato et al. 2009 ; Kayo et al. 2013 ). Black circles are the measured averages, and the red curves were predicted by our model. The dashed orange and brown dotted curves were obtained by GM12 and SC01 , respectively. The field of view of each map is , and the error bars are the standard deviations scaled by . The bottom panels plot the relative deviations from the red curves. As the cosmological models in this and the previous
Paper: 2019ApJ...875..137B (fulltext)
in previous studies (Kapranidis Liebert 1986 ; Dufour et al. 2007 ), atmosphere model codes used to analyze this object relied on constitutive physics that are not totally appropriate for the dense atmosphere of this cool white dwarf. In particular, the analysis of Kapranidis Liebert ( 1986 ) relied on models that assumed a Thomas–Fermi equation of state (Kapranidis 1983 ). Because of this assumption, they found that electron thermal conduction was an important energy transport mechanism, a conclusion that does not hold if we use more realistic equations of state (Bergeron et al. 1995 ; Kowalski et al. 2007 ). Also, Kapranidis Liebert ( 1986 ) computed their synthetic spectra from pure helium atmosphere structures, thus ignoring the impact of heavy elements on the pressure and temperature stratification. Moreover, a simple Lorentzian profile was assumed for the Ca i resonance line. Despite all those simplifying assumptions, they managed to obtain an excellent fit to the resonance line (with T eff = 4500 K and ) and were even able to find a good agreement to the unknown feature at 4500 Å. Unfortunately, no detail regarding this puzzling structure was given, so we cannot know how they were able to explain it. Dufour et al. ( 2007 ) revisited WD 2251−070 using more realistic atmosphere models, where heavy elements are included in the calculation of the atmosphere structures. That being said, a number of unjustified approximations remained, such as the ideal gas law, the ideal Saha ionization equilibrium, and Lorentzian profiles. Their best solution ( T eff = 4000 K and ) was in good agreement with the photometry and with the Ca i resonance line (but they were unable to explain the absorption feature near 4500 Å). Here, we revisit this star using our improved models (Blouin et al. 2018a ) and our new Ca i resonance line profiles. To do so, we use the spectroscopic observations of Dufour et al. ( 2007 ), the Gaia data release 2 (DR2) parallax measurement ( π = 117.15 ± 0.05 mas; Gaia Collaboration 2016 , 2018 ), photometry from Bergeron et al. ( 1997 ), the Panoramic Survey Telescope And Rapid Response System, Chambers et al. ( 2016 ), and the Two Micron All-Sky Survey. Our best fit to the spectroscopic and photometric observations is shown in Figure 9 , and the corresponding atmospheric parameters are given in Table 1 . Figure 9. Our best solution for WD 2251−070. The top panel shows our spectroscopic fit, and the bottom panel shows our fit to the BVRI , grizy, and JHK photometry. Table 1 WD 2251−070 Atmospheric Parameters To obtain this solution, we used the photometric technique (Bergeron et al. 1997 ) to determine T eff and . More precisely, the solid angle π ( R / D ) 2 and the effective temperature were found by adjusting the model fluxes to the photometric observations. From the solid angle, we computed the white dwarf radius R ( D is given by the Gaia parallax) and, from there, we found the mass and the surface gravity using the
Paper: 2021ApJ...908..189B (fulltext)
priors, the relative magnitudes of each component are able to vary freely. While fits performed without priors can produce biased results if the training sets cannot sufficiently describe the shapes of the components, the use of a prior derived from the training set applies constraints on both the shapes and the magnitudes of the components. Thus, caution should be taken to ensure that there is a high level of confidence that the given training set is sufficient to describe both the shape and magnitude of the component before a training set prior is applied. 12 12 In principle, if one is uncertain that the training set is fully representative of the data, it may be possible to apply a “weak” prior, which falls in between the highly constrained and uniform priors used in the examples here. A complicating factor in applying this method is that the posterior will no longer be Gaussian if the prior takes a non-Gaussian form. Note that when using a prior distribution, instead of minimizing the DIC in order to choose the number of SVD modes, we instead maximize the Bayesian evidence , given by where the parameters x are the coefficients of the SVD modes, is the likelihood, is the model composed of the SVD modes, and π ( x ) is the prior distribution. Note that the dimensionality N p of the parameter space will change when the number of SVD modes to be fit is varied. Maximizing the evidence ensures that the prior is properly taken into account. When using a training set prior, variations in modes unimportant to the training set are suppressed, but a larger number of modes may still be chosen by the evidence. Thus, the number of SVD modes may no longer provide as much information about the quality of the estimate of the signal. However, if the training sets do not encompass the true form of one or more of the components, the constraints provided by the prior will prevent the full data from being fit well. In this case, goodness-of-fit statistics such as or will be able to identify a poor fit. Further details regarding the use of these metrics for fits with priors are given in Section 3 . 2.4. Test Strategy In this section, we present a step-by-step outline for assessing the quality of the signal extraction in an observational setting when the true form of the signal is unknown. This test can be performed for fits with or without priors; however, we suggest that the test be performed without a prior distribution first owing to the fact that the use of an incorrect prior distribution will lead to significant bias in the extracted signal, as described in Section 2.3 . If the fit “passes” the test as described below, one may wish to include a training set prior to possibly improve the quality of the signal reconstruction further. 1. Fit many data realizations created from the training set curves. If no prior is used, create a distribution of the chosen numbers of SVD modes. This distribution will have the same dimensionality as the number of
Paper: 2017A&A...608A..62H (fulltext)
estimated and modelled radii (see e.g. Hillenbrand White 2004 ; Mann et al. 2015 ), where the models generally have 510 smaller radii than the observational estimates. The general problem of modelling such stars is the fact that the outer boundary condition influences the interior owing to the very deep convective envelopes. More sophisticated atmosphere models (see e.g. Baraffe et al. 1998 ) give an improved outer boundary condition, but are not able to resolve this problem. The outer boundary condition can also be altered by adding magnetic fields to the models, as has been carried out by Feiden (2016) . Magnetic fields reduce the efficiency of convection, forcing the star to increase its radius in order to radiate its energy away. Indeed, López-Morales (2007) found that there is a clear correlation between stellar magnetic activity and the radius discrepancy in binary stars. A direct consequence of surface magnetic fields is the development of starspots. We added the effect of starspots to our models by including the model of Spruit Weiss (1986) . With this spot model, presented in Sect. 3.2 , it is possible to inflate the radius of a star with a single parameter, which represents the effective spot coverage and can be chosen such that a common age for the system can be found. We used this approach to achieve common ages for the systems V636 Cen and Kepler 16 by inflating the secondary in these systems. The fully convective secondary of Kepler 16 needed only a very small spot coverage of 3% to match the age of its primary. V636 Cen on the other hand needed a huge spot coverage of 44%. This value is in the range of 35–51% found by Jackson Jeffries (2014) , who used a similar model to match polytropic models to six DDLEBs, the Pleiades, and the young cluster NGC 2516. Similar values have been found by O’Neal et al. (2004) , who observed spot coverages of up to 42%. Even though this study observed stars with higher activity than the DDLEBs discussed here, it confirms that our results do not give unreasonably high values. It should also be noted that spots introduce systematical errors in observations and therefore in the radii determinations. Windmiller et al. (2010) for example showed that spots on the EB GU Boo can lead to an error of up to in interferometric radius determination, while the statistical errors from light curve fitting are reported with uncertainties of only 0.5–1%. Mixing length <inline-formula specific-use="simple-math"><italic>α</italic><sub>MLT</sub></inline-formula>. Throughout this study we used a solar calibrated mixing-length parameter , which is kept constant throughout all evolutionary phases and for all mass and metallicity values. We only modified in the case of V2291 Oph because the models had lower temperatures than observed, and this discrepancy could not be explained by a change in composition. The simplest way to increase in these systems is to increase . We needed at least to match the observations. This is