adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020A&A...638A.145T (fulltext)
results of both the binary pipeline ( Fig. 2 ) and the main GALAH pipeline ( Fig. B.1 ), confirming their close agreement. We observe some trends and biases in these figures, such as the inconsistency for the lowest metallicities in the bottom panel of Fig. 2 , which are a consequence of both the flexibility of the The Cannon model and the adequacy of the training set,where it is difficult to ensure equal representation of spectra in all bins of the modelled parameter space (see Sect. 3.3.1 in Buder et al. 2018 ). 4.2 Photometric validation In this validation check and in Sect. 4.3 , we used the benchmark validation sample, which is a subset of GALAH objects withexternal information about the temperature from the infrared flux method (IRFM; Casagrande et al. 2019 ) and radii (asteroseismology; Stello et al. 2017 ; Sharma et al. 2019 ). This sample of 620 giant stars is therefore suitable for benchmark testing, and is dominated by stars from K2 campaigns C1 and C6 ( Sharma et al. 2019 , see also Fig. 13 ). Compared to the previous spectroscopic validation, this sample has almost no overlap with the training set and hence no SME parameters to compare to. We took the results from the binary pipeline, using only the photometric fit (Eq. ( 7 )), and compared them to the IRFM temperature values and reddening values from SFD (Schlegel, Finkbeiner, David) reddening maps ( Schlegel et al. 1998 ) and 3D reddening maps based on Pan-STARRS 1 photometry ( Green et al. 2018 ; hereafter the Bayestar maps). This comparison is shown in Fig. 3 , where we display results with and without the Gaia magnitudes in the photometric fit. We distinguish the Gaia magnitudes from other photometric passbands used in this work because their uncertainties are approximately an order of magnitude lower. They therefore affect the fit more strongly. Fig. 2 Comparison of SME and The Cannon labels for The Cannon validation sample. The Cannon values produced by the binary pipeline are subtracted from the SME values. Fig. 3 Comparison of T eff (orange) and E ( B − V ) (green) parameters as derived by the binary pipeline (using only the photometric fit) to the literature values (indicated on the horizontal axis). The literature values are subtracted from those given by the binary pipeline. The lighter shaded distributions correspond to results from the binary pipeline when the Gaia magnitudes are excluded from the fit. Figure 3 shows some important problems. The most striking is a bias towards higher temperatures as compared to both the GALAH DR2 values and those from the IRFM. An additional test that we performed using a larger validation sample (without the need for external information about the radii) also shows strong grid effects, which diminish considerably, however, when we exclude Gaia magnitudes. The grid effect has a spacing of about 250 K, and it might originate from the SED model in combinationwith small uncertainties of Gaia data. Excluding Gaia magnitudes from the fit
Paper: 2018ApJ...869....9G (fulltext)
membership probabilities for the 7312 sample stars. Figure 8 shows that the stars with high GMM membership probabilities ( P GMM ≥ 0.8) generally have high RF membership probabilities, and the stars with low GMM membership probabilities ( P GMM 0.2) have low RF membership probabilities in general. However, some stars with near zero GMM probabilities still have a certain probability of being RF memberships, indicating that the color and magnitude parameters play non-negligible roles in the RF-based membership determination. In our previous work, we have confirmed that color and magnitude parameters cannot be ignored in the RF-based membership determination (Gao 2018a ). As a model-dependent clustering technique, the GMM method cannot directly handle the color and magnitude parameters since they do not follow Gaussian distributions. Fortunately, the RF method does not require any mathematical model, only a reliable training set is needed. This allows us to easily identify most likely cluster members of M67 in the 11-dimensional parameter space, even if the color and magnitude parameters are taken into account. The 1502 likely cluster members ( P RF ≥ 0.6) are listed in Table 3 , this table is published in its entirety in the electronic edition. It should be noted that Gaia Collaboration et al. ( 2018a ) have investigated M67 using Gaia -DR2 data and obtained 1520 cluster members, but they did not calculate membership probabilities for these stars. Among the 1520 cluster members in Gaia Collaboration et al. ( 2018a ), 1240 (∼82%) and 1201 (∼79%) stars are determined to be likely ( P RF ≥ 0.6) and high-probability ( P RF ≥ 0.8) cluster members by the RF method, respectively. This once again indicates the high effectiveness of the RF method for identifying likely cluster members in high-dimensional parameter space. Figure 8. A comparison between the GMM and RF membership probabilities of the 7312 sample stars. Table 3 A Catalog of Fundamental Information for 1502 Likely Cluster Members with P RF ≥ 0.6 Note. This table contains Gaia -DR2 identifier, positions and RF membership probabilities for the 1502 likely cluster members (≥0.6). Only a portion of this table is shown here to demonstrate its form and content, a machine-readable version of the full table is available. ID: Gaia -DR2 identifier; R.A., decl.: right ascension and declination; P RF : RF membership probability. (This table is available in its entirety in machine-readable form.) Only a portion of this table is shown here to demonstrate its form and content. A machine-readable version of the full table is available. 3. Distance, Proper Motion, and Radial Velocity We have obtained 1361 high-probability cluster members ( P RF ≥ 0.8), 1260 of which have quite high membership probabilities ( P RF ≥ 0.9). We use the 1260 cluster members to calculate the weighted mean parallax (distance) and proper motion of the cluster, but also taking into account the observational errors. Using 687
Paper: 2018ApJ...866...59T (fulltext)
surface term for stellar models is the method proposed by Kjeldsen et al. ( 2008 ). They note that the offset between observed and best model frequencies turns out to be closely fitted by a power law. However, there are issues with many of the surface-term corrections, even for main-sequence stars; and most models perform very badly in the subgiant and red-giant region. Schmitt Basu ( 2015 ) found that the two-term model proposed by Ball Gizon ( 2014 ) works better than other models across a large portion of the HR diagram, and consequently we adopt that for this work. 4. Results and Discussions We show the evolutionary track of our models in Figure 3 . The figure also shows the two error boxes. T eff = 4766 ± 200 K and have been used for the large error box while T eff = 4974 ± 161 K and for the smaller error box. We find that many models fall into the two error boxes. In order to obtain the best-fit model for KIC 8263801 from among these models, we calculated the likelihood for each model and determined the model with the highest likelihood. The likelihood function is defined aswherewith the quantity indicating the observed T eff , [Fe/H], and frequency ν n , l ; while the corresponds to these values of the model. The quantity σ i represents the observational error of . In the study, we use the function as follows:where In the above expression, are the surface-term corrected frequencies of the models. The values of likelihood function for model of KIC 8263801 as a function of mass, metallicity, age, and radius are shown in Figure 4 . We choose the model that maximizes as a candidate for the best-fit model. The model parameters are shown in Table 5 . To clearly compare all of the theoretical frequencies of the best-fit model with observed frequencies, we show the échelle diagram of the best-fit model in Figure 5 . In the figure, open symbols are the model frequencies corrected for the surface term, the filled symbols refer to the observable frequencies. Circles are used for l = 0 modes and squares for l = 2 modes. Figure 4. Likelihood values for models of KIC 8263801 as a function of mass, metallicity, age, and radius, corresponding to panels (a)–(d) respectively. Figure 5. Échelle diagrams for the best-fit model. Open symbols are the surface-term corrected frequencies of the best-fit model; filled symbols refer to the observed frequencies. Circles are used for l = 0 modes, squares for l = 2 modes. Table 5 The Parameters of the Best-fit Model Finally, we derive the parameters of star as the likelihood weighted mean and standard deviation from the models and obtain a mass of , age of t = 1.48 ±0.21 Gyr, and radius of R / R ⊙ = 10.53 ± 0.28. Our best-fit model has an age of 1.596 Gyr, the age as determined from the likelihood weighted average is not too different, and thus the question arises as to whether this star can be a member of NGC 6866. There are a number of results about the age of NGC 6866. For example, Loktin et al. ( 1994 ) derived
Paper: 2020MNRAS.493.3911H (fulltext)
well-studied extragalactic radio source Cygnus A to demonstrate the applicability of our code. This is one of the nearest Fanaroff–Riley type I (FR II) galaxies and has a well-defined jet and counterjet with a jet inclination angle thought to be between 55 ○ and 85 ○ (Bartel et al. 1995 ), with a redshift of z = 0.0562 ± 0.000067 (Carilli Barthel 1996 ). There is observational morphological evidence to suggest that the jets are precessing ( K19 ). We visually identified 48 jet points (with an astrometric error of 0.4 arcsec from VLA data) from the 5-GHz map of Perley, Dreher Cowan ( 1984 ), as shown in Fig. 5 . Jet points are taken to be any discrete, distinct feature that appears to be part of the jet; this includes both compact knots and sub-regions of more extended bright parts of the jet. For longer sub-regions of the jet, we placed multiple points, spaced by at least the resolution of the image, in order to constrain the curvature of the jet in these regions. Figure 5. Locations of visible jet knots within Cygnus A used in the model. Note the curvature in both the jet and counterjet paths. The final points in both the jet and counterjet lie above the primary hotspots of the two lobes, as discussed in the text. The inner, straighter points of the jet used in Fig. 7 are identified as being within the indicated brackets. One important constraint on the application of the MCMC model to real-world data is the requirement for the precessing jet to stay within the lobes. Since the model does not take lobe structure into account beyond the terminal hotspot, the constraint was implemented by a joint prior on position angle and precession cone opening angle. This dual parameter calculates the two extreme edges of the cone for any given position angle, and keeps them within the lobes, which are approximately 25 ○ across at their widest point. Without this additional prior, we found that unphysical jet paths were permitted as part of the fit, broadening the credible interval on precession period. As with the tests described in earlier sections, the priors were flat within the permitted ranges. We initially fitted the model to all identified points (see Fig. 6 ), including the terminal hotspot, which in both lobes is taken to be the more compact or ‘primary’ hotspot; we do not include the larger, brighter secondary hotspot since the position of this is almost certainly dominated by post-shock hydrodynamics (Cox, Gull Scheuer 1991 ). Then, with respect to the potential for hydrodynamic influences to dominate the jet path within the lobes, we fitted the model only to the straight portion of the jet, terminating just inside the lobes (Fig. 7 ). The full number of regions produced a peak in the posterior distribution corresponding to a precession period of 1 Myr. Figure 6. Main: ‘Corner plot’ of one-dimensional and two-dimensional marginalized posterior probability distributions for the model parameters, given the observed jet path of Cygnus A, including
Paper: 2016A&A...589A..86B (fulltext)
a coronal loop in our model) is significantly lower. While our whole computational domain covers more grid points () than the Rappazzo et al. (2008) models (up to ), in their model they describe only a single loop structure (straightened out), and consequently can reach significantly higher Reynolds numbers. Table 1 Exponents for different coronal heating scaling laws as used in Eqs. ( 10 ) and ( 11 ) together with the fitting results from Sect. 5.3 . The coronal heating scaling law we fit to our model data indicates a dependency on the magnetic field that lies between the exponents given by Rappazzo et al. (2008) and van Ballegooijen et al. (2011) . We also find that the homogeneous atmospheric background in the MHD-turbulence setup of Rappazzo et al. (2008) may be problematic when estimating the coronal magnetic energy input or braiding of field lines because the Alfvén crossing time , which determines their scaling law exponents, varies strongly within our stratified atmosphere. In a recent study van Ballegooijen et al. (2014) speculate that the quasi-static behavior of the coronal magnetic field found in models like ours is due to the limited spatial resolution of the photospheric magnetic driving. They suggest that in the case of increased resolution, the heating might be wave-dominated. Certainly, with increasing resolution AC heating will also be present, but this does not automatically imply that the DC heating becomes less efficient. To the contrary, models that employ reduced MHD (allowing for a higher spatial resolution) show that MHD turbulence will lead to a DC-type heating by the build-up of current sheets also for high spatial resolution (e.g., Rappazzo et al. 2008 ). This is backed by the argument that in the coronal part of the atmosphere a field line will adapt quickly to any wave excitation from below because coronal Alfvén speeds are at least one order of magnitude faster than within the lower atmosphere (cf. Fig. A.1 and Table A.1 ). More realistic full MHD simulations with increased resolution will have to show the relative merits of AC and DC heating based on the photospheric driving. The temperature scaling laws we tested against our model field lines ( Rosner et al. 1978 ; Serio et al. 1981 ) indicate only general trends for the most heated field lines and EUV-emissive loops. This puts all attempts to set up scaling laws of the coronal heating into their context, namely that these laws are deduced from and are only applicable to a small subset of coronal field lines, namely the ones that are bright in EUV or X-rays. Non-emissive and weakly heated field lines are often not consistent with scaling laws intended for 1D loop structures. Some of their plasma parameters need to be determined differently, for example, through self-consistent 3D MHD simulations. It is an interesting question whether such observationally motivated scaling laws would benefit from new and much more precise observations of hot coronal loops. We
Paper: 2017ApJ...839...83W (fulltext)
reflecting enhanced and variable N abundances locally in the circumstellar material (CSM) surrounding many of the individual SNRs. There are some indications that the abundances in the twice solar models begin to have some unexpected impacts on relative line intensities. For example, the twice solar models show [O iii ]/H β as already being strong at 100 km s −1 and decreasing dramatically to 200 km s −1 and above, which is exactly opposite to the lower-abundance models over this same range. Also, more salient to the above discussion, the ratio of [N ii ]/[S ii ] varies by over 40% from 100 to 300 km s −1 shocks, whereas this ratio is nearly constant for lower-abundance sets over the same velocity range. As abundances increase, the balance of energy transfer and cooling being carried by the various ions must be changing in ways that are different from the lower-abundance models. From even this cursory comparison, it seems likely that the elevated abundances may contribute both to the high ratios and to the observed scatter of line ratios we observe in M83. Figures 32 and 34 of Allen et al. ( 2008 ) show their 2× solar abundance grids projected onto the same line ratio plots as shown in the middle and right panels of Figure 10 . Allen et al. apply these models to AGN situations, where one attempts to separate shocked emission from photoionized regions, but they should apply to SNR shocks as well. The majority of the SNRs in all three galaxies plotted in Figure 10 lie within the classical shock-heated region toward the upper right in these diagrams, but a significant number of objects appear toward the lower left region nominally ascribed to H ii regions. However, note that the model grids extend significantly down into the nominally photoionized regions of the plots at lower left. (Presumably lower shock velocities might extend even further into this regime.) Here again, we see evidence that at the higher abundances assumed in these models some of the traditional expectations begin to break down. We encourage further investigation of the properties of shock models at elevated abundances that can further elucidate this interesting and so far little-explored region of parameter space. 4.2. Ramifications The high metallicity of M83 has ramifications for the massive stars that are the precursors to many of the SNRs we have observed. It has long been known that abundances affect both the momentum transfer and mass loss in the stellar winds of massive stars (e.g., Vink et al. 2001 ; Kudritzki 2002 ), though the situation is complicated (Smith 2014 ). While most such studies consider only solar abundances and below, the trends with increasing abundance are clear, with higher abundances giving rise to significantly stronger and more massive winds. Coupling this with the hot, high pressure ISM in M83 that was inferred from X-ray data (Long et al. 2014 ), one might expect this wind mass loss to be constrained to the region immediately surrounding the
Paper: 2016MNRAS.457.3115S (fulltext)
The signal source comes from a quasi-monochromatic Continuous Wave HeNe laser emitting at 3.39 μm. Figure 2. Description of the experimental setup. M: injection mirrors for the infrared signal at 3.39μm; L1: focusing lens for the MIR injection into the Periodically Poled Lithium Niobate (PPLN) waveguide; L2: focusing lens for the converted signal into a fibre single mode at 810 nm; L3: collimating lens for the pump signal; P: off-axis parabola; D: dichroic mirror used to separate converted signal from pump signal; BS: beam splitter used to separate the MIR signal in two separated ways. The 3.5 mW signal is shared equally between the two interferometric arms thanks to a pellicle beam splitter BS. Over the MIR stage, a set of mirrors and objectives allow the injection of the signal into frequency converters in each arm. These converters consist of periodically poled Ti-indiffused waveguides in LiNbO 3 . The waveguides are designed to provide low-loss single-mode guiding in the MIR range. Poling periods around 22 μm enable the phase-matching for the SFG process. The entire length of the non-linear conversion section is 92 mm. The non-linear stages are powered by a distributed feedback laser laser emitting at 1065 nm to generate a wave at 810 nm through the SFG process. On each interferometric arm, the maximum power conversion efficiency is measured in the range of 3·10 −5 when each non-linear crystal is powered by a 60 mW pump power. Notice that this overall throughput includes the non-linear efficiency and the whole losses resulting from the launching and collimating stages, including the stringent spatial filtering. This current low conversion efficiency is to be improved for future application, e.g. by using pigtailed techniques as demonstrated by Büchter (Büchter, Herrmann Sohler 2011 ) The pump signal is injected backward using a taper, and is then back-reflected towards the output of the Periodically Poled Lithium Niobate (PPLN) waveguide thanks to a dichroic mirror directly deposited at the waveguide endface [Anti-Reflection (AR) at 3.39 μm and High-Reflection (HR) at 1065 nm, see Fig. 3 ]. The waveguide taper allows us to ensure a better control of the pump beam geometry leading to the optimal spatial overlapping in the non-linear crystals between the pump at 1065 nm and the signal at 3.39 μm. Figure 3. PPLN injection scheme. The pump wave at 1065 nm propagates in the reverse direction, and is coupled into the waveguide thanks to a taper. After a back-reflection by a dichroic mirror, the pump field interacts with the signal at 3.39 μm to generate the converted wave at 810 nm. We define the normalized conversion efficiency curve of the SFG process as the ratio between the converted power and the signal one as a function of the signal wavelength. This curve depends on the crystal's optical and geometrical parameters (Fig. 4 ) and can be tuned by a fine control of its temperature. The full width at half-maximum of the conversion
Paper: 2017MNRAS.468..790H (fulltext)
gravitational lensing distortion matrix ( 1 ) across the image, on the employed weight function W , and also the employed ellipticity definition. In the case of a flat weight, W ()const., and the centre of light as image centre, equations ( 9 ) and ( 13 ) yield unweighted moments. The observed ellipticity ( 10 ) and the intrinsic ellipticity ( 14 ) are then related by An advantage of based on unweighted moments is that for g 1, one may use the observed ellipticity as an unbiased estimator, for the reduced shear g , i.e. , where angular brackets denote averages over the galaxy intrinsic ellipticity distribution (which is assumed rotation invariant), regardless of the details of the intrinsic ellipticity distribution (Seitz Schneider 1997 ). Unweighted moments and ellipticities derived from them are susceptible to image noise in the outer image regions [due to the factor i j in the integral in equation ( 9 )]. Moreover, unweighted moments and ellipticities are strongly affected by errors in the separation of the galaxy light from the light of neighbouring galaxies. Ellipticity estimation from real galaxy images thus often employs down-weighting of the outer regions of the image. The drawback of such radially weighted moments is that they complicate the relation between observed and intrinsic ellipticities. For a general choice of ellipticity definition and weight function W (), and sufficiently small reduced shear g , the observed ellipticity as a function of reduced shear g can be approximated by a linear response in g : where the intrinsic ellipticity , and the shear polarizability If P can be estimated with sufficient accuracy (e.g. from the observed galaxy image itself) and is invertible, one may obtain an unbiased shear estimator by applying P 1 as a shear polarizability correction factor: If the average shear polarizability P for the observed galaxy population is known, another unbiased shear estimator can be constructed that employs P 1 as polarizability correction: The above image ellipticity-based shear estimators can be treated in a uniform manner, 7 with appropriate definitions for the observed ellipticity and intrinsic ellipticity . For example, equation ( 17 ) suggests with and from unweighted moments. For e computed from unweighted moments, one obtains (e.g. Bernstein Jarvis 2002 ) 8 A simplified version of the estimator by Kaiser, Squires Broadhurst ( 1995 , KSB) yields with and from moments with a Gaussian radial weight function, and estimated from the individual observed galaxy images. As for the shear, the ellipticities and can be decomposed into tangential and cross components with respect to a given angular direction or comoving transverse direction : with , , and . 2.4 Density and intrinsic shape correlations The intrinsic shapes of galaxies may have a preferred orientation towards nearby other galaxies and matter overdensities. The preferred orientation of galaxies towards matter overdensities may be quantified by the