adsabs/WIESP2022-NER huggingface dataset:
Paper: 2016ApJ...825...19W (fulltext)
not directly calculated. Instead, Equations ( 6 ) and ( 7 ) are combined by using the posterior samples: to define the likelihood that one then draws from:Performing these two steps repeatedly produces an ensemble of data sets drawn from Equation ( 6 ). Applying Equation ( 8 ) to our M–R relation requires evaluating the lower levels of the forward model described in Equation ( 4 ). Part of the subtlety of checking data-model consistency arises from the hierarchical nature of our model. In particular, of Equation ( 8 ) includes both the population-wide parameters C , γ , σ M and the individual parameters we can use the posterior samples of either of these groups of parameters to calculate . We show the result of both choices in Figure 5 , using the posterior samples from the baseline M–R relation (second line of Table 1 ). Conceptually, using samples from the individual true mass and radius posteriors () evaluates the fit of the model to the currently observed set of planets (the green histograms on the left), while using posterior samples for the M–R relation parameters () evaluates the fit of the model to altogether new sets of planets (the blue histograms on the right). For the former case, the same and as the observed data set are used; for the latter case, is drawn from the distribution of for the observed planets that have a similar mass, and is drawn from the observed data set’s full distribution of without controlling for radius (the size of the radius error bars are fairly constant across the data set). The second aspect of hierarchical model-checking which requires some effort is the identification of a robust statistic to quantify the discrepancy between the observed data and the model-generated data. Optimally this statistic would test the fit of every part of the modeled probability distribution, including the “average” behavior of the model, the “extreme” behavior out on the tails of the distribution, and for hierarchical models, the accuracy of the grouping and relational structure that is illustrated by graphs like Figure 1 . Due to high dimensional parameter space, this proves to be very difficult, so one must identify several statistics which test such aspects separately. To illustrate the problem, we choose two: f 1 σ , the fraction of a given data set’s simulated mass, radius observations which fall within the 68% coverage interval of our probabilistic, baseline M–R relation (blue region in Figure 4 ), and f μ , the fraction of a given data set’s mass measurements whose 1 σ error bars cross the mean relation μ (see Equation ( 2 )) of that same model. Therefore, f 1 σ tests how well the width of our probabilistic M–R relation fits the data, and f μ tests how tightly grouped the data are around the mean compared to the normal distribution of our model. The results of these tests are displayed in Figure 5 . The observed data’s f 1 σ statistic (top) falls at the 63rd and 83rd percentiles (i.e., within “one sigma”) of the
Paper: 2019MNRAS.485.3264P (fulltext)
main age periods in the ring. Age, mass, and Av inferred from the fit are indicated. The data are circles, model is the line, squares are the model after convolution with the filter width used for the data, the horizontal bar indicates the width of the filter. The red point is the photometric emission in the HST /Ha + [N ii ] filter, which includes the line and continuum emission; the yellow point on the vertical grey line is the predicted H α emission from the ionizing budget of the cluster and added up to the continuum prediction, which includes the photosphere contribution, all convolved with the shape of the HST filter (hence the grey line in the younger clusters). The cluster number and its relative coordinates – left corner of each plot – are to be used to find its location in any of the images in this work. To illustrate the range of extinctions and its effect, for each age period two distinct clusters, one requiring minimum Av, other requiring larger Av, are shown. The discrepancy between the predicted H α emission and the data for the youngest 4 Myr burst (illustrated with the clusters 30 y 138), which is due to the diffuse nature of the nebular gas in the ring (discussed in Section 3 ), can be noticed. The cluster no 145 is an example of the few in which the measured emission is in line with the prediction. The simultaneous inclusion of UV, optical, and IR data in the fit was found to be critical for the age determination in the particular range covered in the ring, in the 1 Myr to a few 100 Myr (see below). Moreover, the moderate extinction at the clusters location (Section 5 ) also helped in better constraining ages as the observed SED is close to its intrinsic shape and minimum correction due to dust is required in the fit. On this basis, special features in the SED that were found determinant for the age estimates are: the steep UV–optical–IR spectral slope for assuring the ages of the younger, less than 10 Myr old population (in this case the cluster H α emission cannot be used (Section 3.1 ) but the wide wavelength coverage up to and minimum dust extinction makes the result very solid); the Balmer break for ages older than 20 Myr; the near-IR bands helped to constrain the oldest population beyond 60 Myr because of the progressive curvature of the SED, simultaneously, towards the UV and IR bands. Dating of the clusters detected only from onwards was found largely uncertain: the SSP fits are based on four spectral bins only and the rather featureless shape of the continuum in the workable range, , is alone insufficient to properly constrain the age. For this reason, these clusters are not further analysed in this work. The denser dusty environment of these clusters is evidenced by comparing the UV - with the K -band image of the ring (Fig. 1 ), to see that these clusters are often located behind the thicker dust filaments. Putting in context the cluster properties of NGC 1097’s ring as compared with those of massive young star
Paper: 2020MNRAS.492.1834P (fulltext)
energy redistribution, and the absorption lines of these individual elements dominate different regions of the spectrum, the shape of the bright and dark spots on the stellar surface varies with wavelength. Figure 3. Time evolution of the emergent flux from the surface of the star at different rotational phases in a Gaussian passband centred at 1700 Å with a standard deviation of 50 Å (upper panel) and the TESS passband (lower panel). Predicted flux distribution for reference abundances agrees reasonably with the IUE observations (SWP 19761 and LWR 15774) 2 in Fig. 2 . Small differences are most likely caused by missing opacity in model atmospheres. 3.2 Effect of the individual elements The inhomogeneous distribution of flux in most spectral bands, together with stellar rotation, leads to periodic photometric variability of the star. Fig. 4 shows the overall modelled photometric variability of the star, together with the photometric variability computed only taking into account the effect of each individual chemical element separately, to show the contribution of each element to the overall variability of the star. The central wavelengths and half-widths of the filters used in the plots are taken from Prvák et al. ( 2015 ). While the shape of the curves of the individual elements remains roughly the same at all wavelengths, being ‘composed’ in different ratios results in a curve of varying shape. Figure 4. Light curves of φ Phe computed using only the abundance maps of the individual elements (2010 January), along with the overall variability of the star (solid line) in different passbands. The light curves have been shifted vertically to better demonstrate the variability and marked by central wavelengths of the corresponding filters. Note the different vertical scale of the two panels. Fig. 5 shows the dependence of the amplitude of the photometric variability on wavelength. Again, the amplitude of the overall variability, defined as one-half of the difference between the maximum and the minimum magnitude of the star, is plotted together with the amplitude of the variability computed only taking into account the influence of the four chemical elements, one at a time. The variability of the star is largest in the far-UV part of the spectrum, especially around 1300–1400 Å, mainly because of the very low overall flux in this region. In the near-UV and the optical region up to about 6000 Å, the amplitude in narrow passbands varies significantly with wavelength due to the significant absorption in lines. In the red and the infrared, the variability is mostly governed by re-emission in continuum and the amplitude is relatively constant throughout the region. Figure 5. Amplitude of the photometric variability of φ Phe (2010 January) computed as a function of wavelength in Gaussian passbands with a standard deviation of 50 Å. Yttrium, which shows the most pronounced abundance variation, seems to also have the strongest effect on the variability,
Paper: 2017ApJ...848...16B (fulltext)
Double Star Catalog (WDS; Mason et al. 2001 ), a compilation of all published astrometric measurements of visual binaries. In response, the USNO group assembled all of the data on 40 Eri BC contained in the WDS. We also provided them with a precise measurement based on archival Hubble Space Telescope ( HST ) images obtained in 2006. Moreover, and crucially, in early 2017 they obtained two new high-precision observations with the speckle camera on the 26 inch telescope at the USNO in Washington, D.C. From these sources, there are 22 new measurements since the last one listed by H74 , covering 1974.8 to 2017.2—as well as 158 earlier observations between 1851.1 and 1973.8. Using these data, Mason et al. ( 2017 , hereafter MHM17 ) have computed updated elements for the 40 Eri BC orbit, leading to a significant revision of the dynamical masses. Although the semimajor axis is nearly unchanged from H74 , the new elements lead to a reduction in the orbital period to P = 230.29 ± 0.68 year—a 20% increase in P −2 , and thus in the masses, compared to H74 . The new dynamical mass for 40 Eri B based on the MHM17 analysis and the 2007 Hipparcos parallax is 0.573 ± 0.018 M . In the remainder of this paper, we will also re-evaluate other parameters of the WD, and then discuss the astrophysical implications of these new results. 4. Astrophysical Parameters of 40 Eri B The most precise method to determine the atmospheric parameters ( T eff and log g ) of DA WDs is the spectroscopic technique (Bergeron et al. 1992 ; hereafter BSL92 ; Liebert et al. 2005 ; hereafter LBH05 ; GBR11 ), wherein the observed Balmer line profiles are compared with predictions of modern model atmospheres using χ 2 minimization. The model atmospheres and synthetic spectra we use are similar to the pure-hydrogen LTE models described in Tremblay Bergeron ( 2009 , and references therein). In the case of 40 Eri B, the atmosphere is purely radiative, and thus the solution does not depend on uncertainties in the treatment of convective energy transport. As described in Section 2 (and in BSL92 , their Section 6.4 ), 40 Eri B, with a directly measured dynamical mass, is a key object to test the validity of the spectroscopic technique. We have available nine high signal-to-noise, medium-resolution (∼6 Å FWHM) spectra of 40 Eri B obtained over the past 25 years, mostly with the Steward Observatory 2.3 m telescope at Kitt Peak; details of these spectra are given by GBR11 . The individual spectroscopic determinations of T eff and log g from these spectra are plotted in Figure 1 . The standard deviations (combined in quadrature with the formal internal errors of the fits) are σ ( T eff ) = 220 K and σ (log g ) = 0.036, which are represented by the 1 σ and 2 σ ellipses in Figure 1 . These dispersions are similar to the typical external errors of 1.4% in T eff and 0.042 dex in log g found by LBH05 in fitting DA spectra (values corrected by Bédard et al. 2017 for a minor error in LBH05 ). By
Paper: 2020A&A...635A.117B (fulltext)
values of the parameters varied here, and we refer the reader to Appendix A for the parameters that remain constant in our simulations, including the initial orbital elements for the planets. We tested the three different prescriptions of the general relativity introduced in Sect. 2.3 . They gave very similar results so that in the following we compare the other effects with respect to the simulations performed using the prescription of Kidder (1995) . The planetary reference values were taken to be representative of the Earth; in particular, the quantity k 2,p Δ τ p is equal to 213 s ( Neron de Surgy Laskar 1997 ). The stellar reference values were chosen to be representative of fully convective M dwarfs ( Bolmont et al. 2015 ). For all the simulations we performed, we calculated the transit timing variations ( Agol Fabrycky 2018 ) as follows: (i) for each transit we find the time of the transit mid-time by performing an interpolation to find the precise time a given planet crosses a reference direction. This corresponds to the “Observed transit time” O ; (ii) we evaluate the “Calculated transit times” C by performing a linear fit 2 of the transit times calculated in step i) over the total number of transits; (iii) we calculate the difference O – C to obtain the TTVs as a function of the epoch (or transit number). To quantify the impact of each additional effect on the simulated TTVs, we compute the difference between the TTVs calculated taking into account an additional effect and the TTVs obtained for a Newtonian N -body integration. Table 1 Reference values for the parameters we vary in this study. Notes. We assume that all planets in the system have the same potential Love number of degree-2 k 2 f ,p , fluid Love number k 2,p , and time lag Δ τ p . 4 Influence of each effect on the TTVs We performed a set of six simulations of the TRAPPIST-1 system to test the impact of the additional effects listed in Sect. 2 , and we compare each with a Newtonian N -body simulation. One after another, we explored the effect of each parameter using the reference values in Table 1 and general relativity. We tested the influence of the dissipative part of the planetary tide by assuming for all planets, with all the other parameters set to zero. We repeated the operation for the non-dissipative part of the planetary tide (through k 2,p , equal for all planets), the rotational flattening of the planets (through k 2 f ,p , equal for all planets), the non-dissipative part of the stellar tide (through k 2,⋆ ), the rotational flattening of the star (through k 2 f ,⋆ ), and for general relativity. Figure 1 shows the results for planets b to d. The top panels of Fig. 1 show the transit timing variations for the three planets for the seven simulations, and the bottom panels show the difference between the TTVs and the TTVs corresponding to the pure N -body simulation. The different additional effects have a very limited impact on the shape of the TTVs, but computing
Paper: 2017ApJ...837...59D (fulltext)
of 2 mbar ambient pressure. The light intensity is ∼10 kW m −2 . The erosion of the dust starts milliseconds after the laser is turned on and is observed by a camera from the side. The measurements are based on the thermal creep of gas, which occurs if a temperature gradient is present along capillaries if the mean-free path of the ambient gas molecules is on the order of the size of the capillaries (Knudsen 1909 ). Pores in a dust bed act like capillaries. The basic setting is therefore a dust bed consisting of the sample material set in a vacuum chamber at a few mbar ambient pressure. A temperature gradient is generated by illuminating the surface of the sample. The resulting temperature profile is flat over the topmost dust layer but then decreases further down into the dust bed (de Beule et al. 2015 ). It leads to a gas flow by thermal creep from the cool deeper layers upwards toward the warm surface. The thermal creep flow itself is independent of a pressure and does not induce an overpressure. However, in the top layer without temperature gradient, a pressure is needed to maintain this flow (de Beule et al. 2015 ). Therefore, a pressure maximum evolves and the top layer of about 200 μ m is subject to an internal overpressure. If this stress is larger than the tensile strength, parts of a dust layer will be ejected. In recent work by de Beule et al. ( 2013 ), it was shown that the mass ejection rate N is inversely proportional to the cohesion per mass F and gravity g as In general, the idea is that a temperature gradient grows with time, the pressure takes some time to build up to the level of tensile strength and if a particle layer is ejected, the process starts all over again. This way, the time interval for particle ejections and therefore the ejection rates are coupled to the cohesive strength or tensile strength of the sample. The lower the cohesion, the smaller the time interval needed to build up the necessary pressure, and less cohesive samples result in higher ejection rates. In the case of this experimental setup and dust samples, the time span is on the order of milliseconds. de Beule et al. ( 2013 ) also showed that the brightness of the recorded frames of the erosion is a measure for the ejection rate. Hence, comparing the inverse of the brightness of the pictures in Figure 3 gives a relative value for the cohesion force (plus gravity) of the different dust samples. Figure 3. Overlay of ejected dust for different tempered samples. The overlay contains 2000 frames, which is a record time of 4 s. The laser intensity is ∼12 kW m −2 and the time of illumination is the same for each sample. Based on work by Muntz et al. ( 2002 ) and Sone Itakura ( 1990 ), de Beule et al. ( 2015 ) find for the overpressurewhere L x 1 marks the length (thickness) of dust layers with a temperature gradient and L x 2 is the active depth with constant temperature below the surface. On short timescales of milliseconds, the established temperature
Paper: 2021A&A...645A.108R (fulltext)
quantities ( α , δ , , μ δ , D (SpT), v rad ) by adopting their uncertainties and relevant correlations. We drew 10 000 initial conditions, which were evolved for 10 Gyr in the future and traced back in the past for a sufficiently long time (i.e., up to τ evo ). For each star, we note the rest-frame velocity, v rf , the past and future Galactic Cartesian coordinates, and the corresponding velocity components. We estimated the escape probability from the Milky Way, P esc , for each star as the fraction of initial conditions where ( v rf − v esc ) > 0. We determined the vertical component of the angular momentum, L Z , which indicates the direction towards which the star has been ejected. We also numerically estimated the eccentricity, e , the median orbital apocenter and pericenter, R apo and R peri , and the maximum vertical displacement, Z max . The most recent Galactic plane crossings, R ( Z = 0), were recorded and used to determine the flight time from such locations, τ flight . The ejection velocity, v ej , was determined as the velocity possessed by the star at R ( Z = 0), which was corrected for the solar motion and the Galactic rotation. Table 5 lists the relevant orbital quantities for the 12 MS runaway candidates in a Galactocentric reference system that is left-handed, that is, with the x - and y -axis pointing, respectively, from the Milky Way’s center towards the Sun and along the direction of the Galactic rotation. Table 5. Orbital parameters of our MS candidates. Notes. The meaning of the columns from left to right is as follows: current X Y Z Galactocentric Cartesian coordinates, vertical component of Galactocentric rest-frame velocity, Galactocentric rest-frame velocity, escape probability, orbit eccentricity, vertical component of the angular momentum, median pericenter and apocenter, maximum vertical displacement from the plane, Galactocentric radius at the most recent intersection of past trajectories with the Z = 0 plane, ejection velocity from Z = 0, and flight time from Z = 0. 6. Properties of the runaway candidates 6.1. Orbit analysis As expected for nearby runaway MS stars, all but one candidate are bound to the Milky Way’s potential, since they have v rf that is on average two times smaller than v esc at their Galactic coordinates. The only exception is 2207−4329 that has an escape probability of P esc = 0.4, which is defined as the fraction of simulated trajectories with v rest > v esc . Due to our selection cut of v tan > 150 km s −1 (Eq. ( 1 )), all stars in our sample (both the MS and BHB candidates) ended up having | v rad |≲ v tan within the error bars. Three stars, 2034−5307, 2207−4329, and 2259−4931, have the largest v tan ≳ 450 km s −1 of all MS candidates. This unusual feature can also be noted in the Toomre diagram of Fig. 11 , where the three stars have large negative Galactic Cartesian V components (i.e., they lag behind the Milky Way’s rotation), as it is more typically seen for the majority of BHB
Paper: 2017ApJ...837L...8B (fulltext)
for their stellar halos from the Galaxy Halos, Outer disks, Substructure, Thick disks and Star clusters (GHOSTS) survey ( http://vo.aip.de/ghosts/survey.html ; Radburn-Smith et al. 2011 ; Monachesi et al. 2016a ). RGB star counts allowed GHOSTS to reach V -band (Vega) surface brightness sensitivities of ∼33 mag arcsec −2 , revealing roughly power-law stellar halos between minor axis distances of 10 kpc to >50 kpc. These profiles were integrated within the observed range of radii and extrapolated to total stellar halo mass as discussed above. The RGB color is sensitive to stellar halo metallicity (Monachesi et al. 2016a ), and we adopt a derived [Fe/H] value at 30 kpc along the minor axis following the observational calibration of [Fe/H] as a function of RGB colors for globular clusters (Streich et al. 2014 ) assuming . We add stellar halo masses and metallicities at 30 kpc along the minor axis for the MW (Sesar et al. 2011 ; Xue et al. 2015 ; Bland-Hawthorn Gerhard 2016 ), M31 (Gilbert et al. 2014 ; Ibata et al. 2014 ), and NGC 3115 (Peacock et al. 2015 ). Peacock et al. ( 2015 ) estimated a mass in low-metallicity stars in NGC 3115 of , which they adopted as an estimate of the stellar halo mass. As there is a considerable population of high-metallicity stars at large radii in NGC 3115, which could be argued to legitimately belong to its stellar halo, we choose to adopt as a lower limit to its halo mass. No published estimate of stellar halo mass exists for Cen A; we adopt the metallicity along the minor axis at 30 kpc from Rejkuba et al. ( 2014 ). Stellar halo masses (for UGC 180, NGC 1084, NGC 2903, and NGC 4220) or upper limits (for NGC 3351, NGC 3368, NGC 4258, and M101; galaxies that were either presented as upper limits or detections with 1 σ error bars overlapping with zero) are adopted from Trujillo Fliri ( 2016 ; UGC 180) and Merritt et al. ( 2016 ). The stellar halo masses from Merritt et al. ( 2016 ) are extrapolated to total stellar mass as described above and as tabulated by Harmsen et al. ( 2017 ). As these halo masses were constrained using integrated light, these galaxies have no stellar halo metallicity estimates. Total stellar masses were adopted from the source papers, adjusted to a universally applicable Chabrier ( 2003 ) stellar IMF. Bulge-to-total ratios were derived from near-IR ( K -band or longer) photometry from a variety of sources (described in Table 1 ; for Cen A we assume and UGC 180's B / T was roughly estimated to be ). Typical differences between independent estimates for the same galaxies are , comparable to the quoted uncertainties. BH masses and uncertainties (or limits) were adopted primarily from Kormendy Ho ( 2013 ), where again the source papers are tabulated in Table 1 . Table 1 Galaxy Properties Note. Stellar masses are assumed to have an uncertainty of 0.15 dex. a Galaxies with dominant classical bulges. References. (1) Licquia Newman ( 2015 ), (2) Bland-Hawthorn Gerhard ( 2016 ), (3) Sesar