adsabs/AstroCLIMB huggingface dataset:
Paper: SEGUE-2: Old Milky Way Stars Near and Far
Authors: Constance M. Rockosi, Young Sun Lee, Heather L. Morrison, Brian Yanny, Jennifer A. Johnson, Sara Lucatello, Jennifer Sobeck, Timothy C. Beers, Carlos Allende Prieto, Deokkeun An, Dmitry Bizyaev, Michael R. Blanton, Luca Casagrande, Daniel J. Eisenstein, Andrew Gould, James E. Gunn, Paul Harding, Inese I. Ivans, H. R. Jacobson, William Janesh, Gillian R. Knapp, Juna A. Kollmeier, Sébastien Lépine, Martín López-Corredoira, Zhibo Ma, Heidi J. Newberg, Kaike Pan, Jakub Prchlik, Conor Sayers, Katharine J. Schlesinger, Jennifer Simmerer, David H. Weinberg
Figure caption: (u − g)0 vs. (g − r)0 diagram illustrating the overlap between K and M giants in their respective target types. Spectroscopically confirmed K giants are shown with blue symbols, M giants with red, and carbon stars as green stars.
Paper: SEGUE-2: Old Milky Way Stars Near and Far
Authors: Constance M. Rockosi, Young Sun Lee, Heather L. Morrison, Brian Yanny, Jennifer A. Johnson, Sara Lucatello, Jennifer Sobeck, Timothy C. Beers, Carlos Allende Prieto, Deokkeun An, Dmitry Bizyaev, Michael R. Blanton, Luca Casagrande, Daniel J. Eisenstein, Andrew Gould, James E. Gunn, Paul Harding, Inese I. Ivans, H. R. Jacobson, William Janesh, Gillian R. Knapp, Juna A. Kollmeier, Sébastien Lépine, Martín López-Corredoira, Zhibo Ma, Heidi J. Newberg, Kaike Pan, Jakub Prchlik, Conor Sayers, Katharine J. Schlesinger, Jennifer Simmerer, David H. Weinberg
Figure caption: Two examples of correction functions (red curves and lines) from NGS1 (left panels) and ANNRR (right panels). The correction function for the temperature and metallicity is derived from a quadratic fit, whereas a linear fit is used for gravity.
Paper: Objective Separation between CP1 and CP2 Based on Feature Extraction with Machine Learning
Authors: Lun-Hua Shang, A-Li Luo, Liang Wang, Li Qin, Bing Du, Xu-Jiang He, Xiang-Qun Cui, Yong-Heng Zhao, Ri-Hong Zhu, Qi-Jun Zhi
Figure caption: The color–magnitude diagram of new CP1 sample stars (the red plus signs) of this paper and published CP2 sample stars (blue dots) from Renson & Manfroid (2009); Hou et al. (2015) and Qin et al. (2019). E(BP − RP) is the color excess, A  G  is extinction, G RP and G BP are the mean photometry at bands 330–680nm and 630–1050 nm, respectively. The parameters are obtained from Gaia DR2 (Andrae et al. 2018). The upper and right-hand panels are the statistical distributions for ﹩G+5{\rm{log}}10(\varpi )-10-{A}_{G}﹩ and G BP − G RP − E(BP − RP) of the samples, respectively. The blue and red lines are the corresponding distributions of the new samples and published samples, respectively. The gray lines are the total distribution of all CP1 stars obtained from the LAMOST spectra (new + published CP1 stars).
Paper: Objective Separation between CP1 and CP2 Based on Feature Extraction with Machine Learning
Authors: Lun-Hua Shang, A-Li Luo, Liang Wang, Li Qin, Bing Du, Xu-Jiang He, Xiang-Qun Cui, Yong-Heng Zhao, Ri-Hong Zhu, Qi-Jun Zhi
Figure caption: The spatial distribution of CP2 and CP1 stars from LAMOST DR8 in Galactic coordinates. “HW+QL” represents the stars were obtained from the published papers of Hou et al. (2015) and Qin et al. (2019). “RM+HPB+QL” represents the stars are obtained from the published papers of Renson & Manfroid (2009), Hou et al. (2015), and Qin et al. (2019), respectively. The published CP1 and this paper are represented by gray crosses and blue dots, respectively. The CP2 stars obtained from the published papers and this paper are represented by green plus signs and red dots. The thick gray-solid line is the equatorial plane.
Paper: Objective Separation between CP1 and CP2 Based on Feature Extraction with Machine Learning
Authors: Lun-Hua Shang, A-Li Luo, Liang Wang, Li Qin, Bing Du, Xu-Jiang He, Xiang-Qun Cui, Yong-Heng Zhao, Ri-Hong Zhu, Qi-Jun Zhi
Figure caption: The line index diagrams. The interaction between the four most important features was obtained from the XGBoost classifier. The blue and red points are the line index of CP2 and CP1 stars, respectively. The cyan-solid and cyan-dashed ellipses are the 2σ and 3σ confidence level of CP1 stars. The black-solid and black-dashed ellipses represent the 2σ and 3σ confidence level of CP2 stars, respectively.
Paper: Objective Separation between CP1 and CP2 Based on Feature Extraction with Machine Learning
Authors: Lun-Hua Shang, A-Li Luo, Liang Wang, Li Qin, Bing Du, Xu-Jiang He, Xiang-Qun Cui, Yong-Heng Zhao, Ri-Hong Zhu, Qi-Jun Zhi
Figure caption: The statistical distribution of stellar atmospheric parameters of CP1 and CP2 stars. The upper and bottom rows are the statistical distributions of stellar atmospheric parameters of CP1 and CP2 stars, respectively. For each row, from the left-hand to right-hand panels are the statistical histogram of T eff, log g and [Fe/H], respectively.
Paper: Objective Separation between CP1 and CP2 Based on Feature Extraction with Machine Learning
Authors: Lun-Hua Shang, A-Li Luo, Liang Wang, Li Qin, Bing Du, Xu-Jiang He, Xiang-Qun Cui, Yong-Heng Zhao, Ri-Hong Zhu, Qi-Jun Zhi
Figure caption: The distribution of stellar atmospheric parameters of CP1 and CP2 stars in LAMOST DR8 Low-Resolution Spectroscopic Survey. The left-hand panel is T eff vs. log g diagram, the middle panel is T eff vs. [Fe/H] diagram, and the right-hand panel is log g vs. [Fe/H] diagram. The upper row is the distribution of the stellar atmospheric parameters of CP1 stars, while the bottom row is the distribution of the CP2 stars.
Paper: Objective Separation between CP1 and CP2 Based on Feature Extraction with Machine Learning
Authors: Lun-Hua Shang, A-Li Luo, Liang Wang, Li Qin, Bing Du, Xu-Jiang He, Xiang-Qun Cui, Yong-Heng Zhao, Ri-Hong Zhu, Qi-Jun Zhi
Figure caption: The line index distributions of the four important features were obtained from the XGboost classifier. Panels (a) to (d) represent the line index distributions of features, 4935 Å blend, 4416 Å, 5081 Å blend and 4402 Å, respectively. The line index distributions of CP2 and CP1 stars are the red and blue histograms, respectively. μ and σ are the mean and standard deviation of corresponding distributions.
Paper: Objective Separation between CP1 and CP2 Based on Feature Extraction with Machine Learning
Authors: Lun-Hua Shang, A-Li Luo, Liang Wang, Li Qin, Bing Du, Xu-Jiang He, Xiang-Qun Cui, Yong-Heng Zhao, Ri-Hong Zhu, Qi-Jun Zhi
Figure caption: As in Figure 7, but for computing the feature importances of separation between CP1 and CP2 stars.
Paper: Objective Separation between CP1 and CP2 Based on Feature Extraction with Machine Learning
Authors: Lun-Hua Shang, A-Li Luo, Liang Wang, Li Qin, Bing Du, Xu-Jiang He, Xiang-Qun Cui, Yong-Heng Zhao, Ri-Hong Zhu, Qi-Jun Zhi
Figure caption: As Figure 15, but for the sample of CP2 stars.
Paper: Objective Separation between CP1 and CP2 Based on Feature Extraction with Machine Learning
Authors: Lun-Hua Shang, A-Li Luo, Liang Wang, Li Qin, Bing Du, Xu-Jiang He, Xiang-Qun Cui, Yong-Heng Zhao, Ri-Hong Zhu, Qi-Jun Zhi
Figure caption: The red and blue spectra are the averaged spectra of CP2 and CP1 stars, respectively. The value on the vertical axis is the SHAP value which represents the impact on the separation between CP2 stars and CP1 stars. The upper and bottom panels show the comparison in the wavelength ranges from 3800 Å to 4700 Å and from 4700 Å to 5600 Å, respectively.
Paper: The Impact of Observing Strategy on Cosmological Constraints with LSST
Authors: Michelle Lochner, Dan Scolnic, Husni Almoubayyed, Timo Anguita, Humna Awan, Eric Gawiser, Satya Gontcho A Gontcho, Melissa L. Graham, Philippe Gris, Simon Huber, Saurabh W. Jha, R. Lynne Jones, Alex G. Kim, Rachel Mandelbaum, Phil Marshall, Tanja Petrushevska, Nicolas Regnault, Christian N. Setzer, Sherry H. Suyu, Peter Yoachim, Rahul Biswas, Tristan Blaineau, Isobel Hook, Marc Moniez, Eric Neilsen, Hiranya Peiris, Daniel Rothchild, Christopher Stubbs
Figure caption: Selected metrics, relative to their values at baseline, as a function of the cumulative season length (the total amount of time a field is observed) of different observing strategies. To improve readability, points that are nearby in the x-axis are binned with only the mean and error bar plotted for that bin. Shorter season lengths degrade strong lensing and SNe performance, but as long as the season length does not fall much below its value at baseline, these metrics do not seem to be strongly affected. We have highlighted specific simulations with numbered annotations. Simulation 1 is a larger area footprint, which removes visits from the extragalactic area. Simulation 2 artificially improves cadence (and hence SN performance) by removing the mini-surveys. Simulation 3 replaces some visits with short exposures that cannot be used for SN observations, thus reducing signal-to-noise. And Simulation 4 takes some observations at high airmass, which is usually avoided. This indicates that taking some number of high airmass observations is an effective way to increase season length without degrading metric performance significantly. List of annotations: (1) footprint_newBv1.5_10yrs, (2) wfd_depth_scale0.99_v1.5_10yrs, (3) short_exp_5ns_5expt_v1.5_10yrs, and (4) dcr_nham1_ugri_v1.5_10yrs
Paper: The Impact of Observing Strategy on Cosmological Constraints with LSST
Authors: Michelle Lochner, Dan Scolnic, Husni Almoubayyed, Timo Anguita, Humna Awan, Eric Gawiser, Satya Gontcho A Gontcho, Melissa L. Graham, Philippe Gris, Simon Huber, Saurabh W. Jha, R. Lynne Jones, Alex G. Kim, Rachel Mandelbaum, Phil Marshall, Tanja Petrushevska, Nicolas Regnault, Christian N. Setzer, Sherry H. Suyu, Peter Yoachim, Rahul Biswas, Tristan Blaineau, Isobel Hook, Marc Moniez, Eric Neilsen, Hiranya Peiris, Daniel Rothchild, Christopher Stubbs
Figure caption: Selected metrics, relative to their values at baseline, as a function of different weather simulations, for an older FBS version, v1.3. The simulations make use of real weather data and differ on what cloud coverage would be required before observations are paused: 30% cloud cover (realistic), 70% cloud cover (v1.3-like, in other words similar to previous simulations), any cloud cover (optimistic, where the dome would never close due to bad weather), and no down time (optimistic, no down time, where the telescope would always observe no matter what). While bad weather only has a small impact on static science metrics by slightly reducing the median depth, it has an enormous impact on transient science metrics by as much as 150%. This highlights the importance of including realistic weather in simulations to make accurate predictions of scientific returns.
Paper: TESS Transit Timing of Hundreds of Hot Jupiters
Authors: Ekaterina S. Ivshina, Joshua N. Winn
Figure caption: Distribution of χ 2 based on constant-period models fitted to the transit times of all the planets in our sample. The gray shaded area encompasses the region between the 2.5% and 97.5% levels of the chi-square distribution. (Left) Using TESS data only. Red points are cases for which at least some of the data had 30 minute sampling. (Right) Using TESS data and previously reported data from the literature.
Paper: TESS Transit Timing of Hundreds of Hot Jupiters
Authors: Ekaterina S. Ivshina, Joshua N. Winn
Figure caption: Transit depth and orbital period for all of the known transiting planets (gray), and the systems in our sample (colored points, with the color conveying the TESS apparent magnitude).
Paper: TESS Transit Timing of Hundreds of Hot Jupiters
Authors: Ekaterina S. Ivshina, Joshua N. Winn
Figure caption: TESS light curve of WASP-12b. The groups of red points are the transit intervals, which are centered on the predicted transit times and extend for four transit durations.
Paper: TESS Transit Timing of Hundreds of Hot Jupiters
Authors: Ekaterina S. Ivshina, Joshua N. Winn
Figure caption: Histogram of the number of transit times available for a given target after sigma clipping has been applied when fitting a linear model to timing data.
Paper: TESS Transit Timing of Hundreds of Hot Jupiters
Authors: Ekaterina S. Ivshina, Joshua N. Winn
Figure caption: NGTS-11 TESS light curves. The transits of NGTS-11 c are shown in red.
Paper: TESS Transit Timing of Hundreds of Hot Jupiters
Authors: Ekaterina S. Ivshina, Joshua N. Winn
Figure caption: Individual transits of WASP-12b. The red curves are the best-fitting models.
Paper: TESS Transit Timing of Hundreds of Hot Jupiters
Authors: Ekaterina S. Ivshina, Joshua N. Winn
Figure caption: Folded light curve of WASP-12b.
Paper: TESS Transit Timing of Hundreds of Hot Jupiters
Authors: Ekaterina S. Ivshina, Joshua N. Winn
Figure caption: Top panel: Timing residuals of WASP-12 b. Blue points are based on previously reported transit times drawn from the literature. Red points are based on TESS data. The black curve shows the residuals of the best-fitting model in which the period changes uniformly with time. Bottom panel: close-up view of the TESS data points.
Paper: TESS Transit Timing of Hundreds of Hot Jupiters
Authors: Ekaterina S. Ivshina, Joshua N. Winn
Figure caption: Top panel: Timing residuals of WASP-161 b. The blue point is based on a previously reported transit time drawn from the literature. Red points are based on TESS data. Bottom panel: close-up view of the TESS data points.
Paper: TESS Transit Timing of Hundreds of Hot Jupiters
Authors: Ekaterina S. Ivshina, Joshua N. Winn
Figure caption: Top panel: Timing residuals of WASP-4 b. Blue points are based on previously reported transit times drawn from the literature. Red points are based on TESS data. Bottom panel: close-up view of the TESS data points.
Paper: TESS Transit Timing of Hundreds of Hot Jupiters
Authors: Ekaterina S. Ivshina, Joshua N. Winn
Figure caption: Top panel: Timing residuals of TrES-5 b. Blue points are based on previously reported transit times drawn from the literature. Red points are based on TESS data. Bottom panel: close-up view of the TESS data points.
Paper: TESS Transit Timing of Hundreds of Hot Jupiters
Authors: Ekaterina S. Ivshina, Joshua N. Winn
Figure caption: Timing residuals of CoRoT-2 b. Blue points are based on previously reported transit times drawn from the literature. The white point is a 5σ outlier that was not included when performing the fits.
Paper: TESS Transit Timing of Hundreds of Hot Jupiters
Authors: Ekaterina S. Ivshina, Joshua N. Winn
Figure caption: Top panel: Timing residuals of WASP-45 b. Blue points are based on previously reported transit times drawn from the literature. Red points are based on TESS data. Bottom panel: close-up view of the TESS data points.
Paper: TESS Transit Timing of Hundreds of Hot Jupiters
Authors: Ekaterina S. Ivshina, Joshua N. Winn
Figure caption: Top panel: Timing residuals of TrES-1 b. Blue points are based on previously reported transit times drawn from the literature. Red points are based on TESS data. Bottom panel: close-up view of the TESS data points.
Paper: TESS Transit Timing of Hundreds of Hot Jupiters
Authors: Ekaterina S. Ivshina, Joshua N. Winn
Figure caption: NGTS-11 c individual transits (upper panel: TESS sector 3; lower panel: TESS sector 30).
Paper: TESS Transit Timing of Hundreds of Hot Jupiters
Authors: Ekaterina S. Ivshina, Joshua N. Winn
Figure caption: Timing residuals of NGTS-11 c. The points are based on TESS data.
Paper: The Super-Alfvénic Rotational Instability in Accretion Disks about Black Holes
Authors: Hans Goedbloed, Rony Keppens
Figure caption: Fragmentation of the Spectral Web of the corotating SARIs for an accretion disk with parameters ϵ ≡ B 1 = 0.045, μ 1 ≡ B  θ1/B  z1 = 10, ﹩\beta \equiv 2{p}_{1}/{B}_{1}^{2}=10﹩, δ ≡ r 2/r 1 − 1 = 1, and mode numbers m = 1, k = 50, for fixed matching radius r 0 = 0.5. The radial profiles of the continua are shown above the main panel.
Paper: The Super-Alfvénic Rotational Instability in Accretion Disks about Black Holes
Authors: Hans Goedbloed, Rony Keppens
Figure caption: Spectral Web of the corotating SARIs for an accretion disk with parameters ϵ ≡ B 1 = 0.0141, μ 1 ≡ B  θ1/B  z1 = 1, ﹩\beta \equiv 2{p}_{1}/{B}_{1}^{2}=100﹩, δ ≡ r 2/r 1 − 1 = 0.1, as in Figure 5, but mode numbers m = 10, k = 50. The radial profiles of the overlapping continua are shown above the main panel. Their real frequencies are indicated by the green line in the main panel. The “outer” modes cluster toward the tip of the forward Alfvén continuum ﹩\{{{\rm{\Omega }}}_{{\rm{A}}}^{+}\}﹩ at r = r 2, and the “inner” modes cluster toward the tip of the backward Alfvén continuum ﹩\{{{\rm{\Omega }}}_{{\rm{A}}}^{-}\}﹩ at r = r 1.
Paper: The Super-Alfvénic Rotational Instability in Accretion Disks about Black Holes
Authors: Hans Goedbloed, Rony Keppens
Figure caption: Three eigenfunctions of the SARI corresponding to Figure 5: (a) first “inner” mode, σ = − 9.5587, ν = 0.39118; (b) fourth “outer” mode, σ = − 9.1031, ν = 0.06368; and (c) 10th “inner” mode, σ = − 9.2741, ν = 0.001316. Note that the near singularities ﹩{{\rm{\Omega }}}_{{\rm{A}}}^{+}﹩ for the “inner” modes (a) and (c), or ﹩{{\rm{\Omega }}}_{{\rm{A}}}^{-}﹩ for the “outer” mode (b), are located outside the physical interval.
Paper: The Super-Alfvénic Rotational Instability in Accretion Disks about Black Holes
Authors: Hans Goedbloed, Rony Keppens
Figure caption: Blown-up parts of the three eigenfunctions of Figure 6 in the cutoff regions with the approach beyond the near singularity toward the actual outer boundary (for the “inner” modes) or the inner boundary (for the “outer” modes); to visualize this, the amplitudes were multiplied with the factors (a) 30, (b1) 2 × 103, and (c1) 106. The perpendicular current density distribution corresponding to the eigenfunctions of Figure 6(b) and (c) is shown in panels (b2) and (c2).
Paper: The Super-Alfvénic Rotational Instability in Accretion Disks about Black Holes
Authors: Hans Goedbloed, Rony Keppens
Figure caption: Asymptotic approach (green arrows) of the image in the z-plane of the end points ﹩\tilde{x}=0﹩ and ﹩\tilde{x}=1﹩ of the physical interval (in blue) to the two singularities z = ±1 of the associated Legendre functions. Their circles of convergence are dashed.
Paper: The Super-Alfvénic Rotational Instability in Accretion Disks about Black Holes
Authors: Hans Goedbloed, Rony Keppens
Figure caption: Logarithmic Spectral Web corresponding to the “inner” corotating SARIs of Figure 8 with nine analytic eigenvalues and their locus (green dots and dashed line). The eigenvalues obtained by numerical solution of the “extended” Legendre Equation (40) with w = 2.1881 are indicated by green diamonds. The horizontal scale refers to the frequency shift with respect to the cluster point, ﹩{\rm{\Delta }}\sigma \equiv \sigma -{{\rm{\Omega }}}_{{\rm{A}}}^{-}({r}_{1})﹩.
Paper: The Super-Alfvénic Rotational Instability in Accretion Disks about Black Holes
Authors: Hans Goedbloed, Rony Keppens
Figure caption: Spectral Web of the corotating SARIs for an accretion disk with parameters ϵ ≡ B 1 = 0.1041, μ 1 ≡ B  θ1/B  z1 = 1, ﹩\beta \equiv 2{p}_{1}/{B}_{1}^{2}=100﹩, like in Figure 8, but much larger thickness, δ ≡ r 2/r 1 − 1 = 1, and mode numbers m = 10, k = 50, for fixed matching radius r 0 = 0.5. The radial profiles of the continua are shown in the top figure.
Paper: The Super-Alfvénic Rotational Instability in Accretion Disks about Black Holes
Authors: Hans Goedbloed, Rony Keppens
Figure caption: Schematic representation of the two continuum functions ﹩\sigma ={{\rm{\Omega }}}_{{\rm{A}}}^{-}(r)\equiv {{\rm{\Omega }}}_{0}(r)-{\omega }_{{\rm{A}}}(r)﹩ and ﹩\sigma ={{\rm{\Omega }}}_{{\rm{A}}}^{+}(r)\equiv {{\rm{\Omega }}}_{0}(r)+{\omega }_{{\rm{A}}}(r)﹩ with the two near singularities ﹩{{\rm{\Omega }}}_{{\rm{A}}}^{-}({r}_{1})﹩ and ﹩{{\rm{\Omega }}}_{{\rm{A}}}^{+}({r}_{2}^{* })﹩ for an “inner” corotating SARI. The vertical line indicates the real part σ of the eigenvalue of the mode. The eigenvalues of the “inner” SARIs are dictated by the asymptotic approach ﹩\sigma \to {{\rm{\Omega }}}_{{\rm{A}}}^{-}({r}_{1})﹩ indicated by the green arrow.
Paper: The Super-Alfvénic Rotational Instability in Accretion Disks about Black Holes
Authors: Hans Goedbloed, Rony Keppens
Figure caption: Quasi-discrete solutions corresponding to the Spectral Web of Figure 12. The functions Π1 and Π2 display a tiny (invisible) jump such that W com ≠ 0 but quite small, (a) σ = 5.5, ν = 0.2, (b) σ = 5.5, ν = 0.02.
Paper: A Statistical View of the Stable and Unstable Roche Lobe Overflow of a Tertiary Star onto the Inner Binary in Triple Systems
Authors: Adrian S. Hamers, Hila Glanz, Patrick Neunteufel
Figure caption: Mobile diagrams for a system in which unstable tertiary RLOF is followed by a merger in the inner binary system. The merged star, a new MS star, forms a tight binary with the tertiary star (now a WD). The latter evolves into a double WD system that merges into a single WD (the total WD mass before merger is just below the Chandrasekhar mass; an SN Ia explosion is assumed not to occur). When applicable, the current mutual inclination between the inner and outer orbits (i rel) is indicated. Note that the mobiles (black solid lines) are only shown for bound (sub)systems and when the code is in secular integration mode. Directly following (triple) CE, the code switches to direct N-body integration, in which case the orbital configuration may not be stable; stars are, in this case, depicted as in isolation (although they may be actually bound).
Paper: A Statistical View of the Stable and Unstable Roche Lobe Overflow of a Tertiary Star onto the Inner Binary in Triple Systems
Authors: Adrian S. Hamers, Hila Glanz, Patrick Neunteufel
Figure caption: Mobile diagrams for a system in which triple CE leads to the unbinding of the tertiary star. The inner binary (consisting of two MS stars) survives initially, but its orbital periapsis decreases. It merges at a later time when the stars expand during the MS, forming a new single MS star that eventually becomes a CO WD.
Paper: A Statistical View of the Stable and Unstable Roche Lobe Overflow of a Tertiary Star onto the Inner Binary in Triple Systems
Authors: Adrian S. Hamers, Hila Glanz, Patrick Neunteufel
Figure caption: Mobile diagrams for a system in which triple CE occurs with an exchange interaction, leaving a binary with swapped components. The latter subsequently leads to a merger of two WDs and an SN Ia explosion. Note that the original inner binary, in isolation, would not have been conducive to ultimately producing an SN explosion.
Paper: A Statistical View of the Stable and Unstable Roche Lobe Overflow of a Tertiary Star onto the Inner Binary in Triple Systems
Authors: Adrian S. Hamers, Hila Glanz, Patrick Neunteufel
Figure caption: Mobile diagrams for a system in which stable triple mass transfer occurs, and a significant amount of mass is accreted onto the inner binary.
Paper: A Statistical View of the Stable and Unstable Roche Lobe Overflow of a Tertiary Star onto the Inner Binary in Triple Systems
Authors: Adrian S. Hamers, Hila Glanz, Patrick Neunteufel
Figure caption: Probability density distributions for initial values of several parameters, making a distinction between all systems considered in the population synthesis calculations (black dotted lines), all systems undergoing triple RLOF (blue dotted lines), triple RLOF systems leading to stable transfer (solid green lines), and triple RLOF systems leading to CE evolution (red dashed lines). The four panels show initial distributions of the inner semimajor axis, a 1, the outer orbital periapsis distance, a 2(1 − e 2), the outer orbital mass ratio q 2 ≡ m 3/(m 1 + m 2), and the cosine of the mutual inclination, i rel.
Paper: A Statistical View of the Stable and Unstable Roche Lobe Overflow of a Tertiary Star onto the Inner Binary in Triple Systems
Authors: Adrian S. Hamers, Hila Glanz, Patrick Neunteufel
Figure caption: Distribution of the age of the system in the Monte Carlo simulations when triple RLOF was triggered (black solid line). The red dashed line shows the distribution of the duration of triple RLOF when mass transfer is stable (here, multiple RLOF episodes are possible for a single system).