adsabs/AstroCLIMB huggingface dataset:
Paper: Refined M-type Star Catalog from LAMOST DR10: Measurements of Radial Velocities, Teff, log g, [M/H], and [α/M]
Authors: Shuo Li, Yin-Bi Li, A-Li Luo, Jun-Chao Liang, You-Fen Wang, Jing Chen, Shuo Zhang, Mao-Sheng Xiang, Hugh R. A. Jones, Zhong-Rui Bai, Xiao-Xiao Ma, Yun-Jin Zhang, Hai-Ling Lu
Figure caption: Kiel diagrams of this work and the comparison studies. From top to bottom, the panels show the Kiel diagrams from APOGEE, this work, Qiu2023, Ding2022, and LASPM. The meanings of the red and green dots in the left column are the same as those explained in Figure 15. The meanings of the orange dots and plus signs are the same as those explained in Figure 17. A detailed discussion of these dots and plus signs is provided in Sections 4.2.2 and 4.3. The gray lines in each panel represent 9 Gyr theoretical isochrones generated using the PARSEC (A. Bressan et al. 2012), with metallicities of −0.6, -0.3, 0.0, and 0.3 dex. The same PARSEC isochrone settings are adopted in Figures 24 and 25, and will not be described again.
Paper: Refined M-type Star Catalog from LAMOST DR10: Measurements of Radial Velocities, Teff, log g, [M/H], and [α/M]
Authors: Shuo Li, Yin-Bi Li, A-Li Luo, Jun-Chao Liang, You-Fen Wang, Jing Chen, Shuo Zhang, Mao-Sheng Xiang, Hugh R. A. Jones, Zhong-Rui Bai, Xiao-Xiao Ma, Yun-Jin Zhang, Hai-Ling Lu
Figure caption: Teff vs. ﹩{({G}_{\rm{BP}\,}-{G}_{\,\rm{RP}})}_{0}﹩ diagrams of dM stars from this work and the comparison studies. From top to bottom, the panels show the Teff vs. ﹩{({G}_{\rm{BP}\,}-{G}_{\,\rm{RP}})}_{0}﹩ diagrams from APOGEE, this work, Li2021, Ding2022, and LASPM. The meanings of the red dots in the left column are the same as those explained in Figure 15. The meanings of the red plus signs are the same as those explained in Figure 19. A detailed discussion of these dots and plus signs is provided in Sections 4.2.2 and 4.3. The settings of the PARSEC isochrones (gray lines) shown in the panels are the same as those described in Figure 23.
Paper: Refined M-type Star Catalog from LAMOST DR10: Measurements of Radial Velocities, Teff, log g, [M/H], and [α/M]
Authors: Shuo Li, Yin-Bi Li, A-Li Luo, Jun-Chao Liang, You-Fen Wang, Jing Chen, Shuo Zhang, Mao-Sheng Xiang, Hugh R. A. Jones, Zhong-Rui Bai, Xiao-Xiao Ma, Yun-Jin Zhang, Hai-Ling Lu
Figure caption: Teff vs. (W1 − W2)0 diagrams of dM stars from this work and the comparison studies. From top to bottom, the panels show the Teff vs. (W1 − W2)0 diagrams from APOGEE, this work, Li2021, Ding2022, and LASPM. The settings of the PARSEC isochrones (gray lines) shown in the panels are the same as those described in Figure 23.
Paper: Mining Young Stellar Objects in unTimely: Probing Variability and Physical Properties
Authors: Jingyi Zhang, Yanxia Zhang, Zihan Kang, Hu Zou, Changhua Li, Dongwei Fan, Yongheng Zhao, Jinghang Shi
Figure caption: Example light curves of different types of variables: linear, periodic, curved, burst, drop, and irregular from left to right.
Paper: Mining Young Stellar Objects in unTimely: Probing Variability and Physical Properties
Authors: Jingyi Zhang, Yanxia Zhang, Zihan Kang, Hu Zou, Changhua Li, Dongwei Fan, Yongheng Zhao, Jinghang Shi
Figure caption: Luminosity (L) as a function of effective temperature (Teff). The values are derived from SED fitting of best-fit models, superimposed on PARSEC evolutionary tracks for solar metallicity.
Paper: Mining Young Stellar Objects in unTimely: Probing Variability and Physical Properties
Authors: Jingyi Zhang, Yanxia Zhang, Zihan Kang, Hu Zou, Changhua Li, Dongwei Fan, Yongheng Zhao, Jinghang Shi
Figure caption: JHK color–color diagram for the sample. The loci of unreddened dwarfs and giants are plotted as blue and orange curves, respectively. The classical T Tauri stars’ loci are represented by a brown dotted line. Reddening vectors extend from the terminal points of each locus at intervals of ΔAV = 5 mag.
Paper: Mining Young Stellar Objects in unTimely: Probing Variability and Physical Properties
Authors: Jingyi Zhang, Yanxia Zhang, Zihan Kang, Hu Zou, Changhua Li, Dongwei Fan, Yongheng Zhao, Jinghang Shi
Figure caption: The SED of the target with black circles representing NIR and WISE photometry. The black line represents the best-fitted stellar photosphere.
Paper: Mining Young Stellar Objects in unTimely: Probing Variability and Physical Properties
Authors: Jingyi Zhang, Yanxia Zhang, Zihan Kang, Hu Zou, Changhua Li, Dongwei Fan, Yongheng Zhao, Jinghang Shi
Figure caption: Spatial distribution of the YSOs in this work. The blue filled rectangles represent the sky coverage of nearby star-forming regions (C. Zucker et al. 2019).
Paper: Mining Young Stellar Objects in unTimely: Probing Variability and Physical Properties
Authors: Jingyi Zhang, Yanxia Zhang, Zihan Kang, Hu Zou, Changhua Li, Dongwei Fan, Yongheng Zhao, Jinghang Shi
Figure caption: The distribution of SD/σ of binned NEOWISE data and unTimely data.
Paper: Mining Young Stellar Objects in unTimely: Probing Variability and Physical Properties
Authors: Jingyi Zhang, Yanxia Zhang, Zihan Kang, Hu Zou, Changhua Li, Dongwei Fan, Yongheng Zhao, Jinghang Shi
Figure caption: Stellar mass–luminosity relation derived from the SED-fitting luminosities and masses calculated using solar-metallicity PARSEC evolutionary tracks.
Paper: Mining Young Stellar Objects in unTimely: Probing Variability and Physical Properties
Authors: Jingyi Zhang, Yanxia Zhang, Zihan Kang, Hu Zou, Changhua Li, Dongwei Fan, Yongheng Zhao, Jinghang Shi
Figure caption: Fractional flux change (ΔW2) between maximum and minimum phases as a function of stochasticity (SD/σ). Here SD denotes the standard deviation of flux measurements for a given light curve, and σ represents the mean flux uncertainty. The vertical dashed line at SD/σ = 20 marks the classification threshold.
Paper: Capturing Turbulence with Numerical Dissipation: A Simple Dynamical Model for Unresolved Turbulence in Hydrodynamic Simulations
Authors: Vadim A. Semenov
Figure caption: Gas density (gray) and small-scale turbulent energy slices (orange) in the coarse-grained high-resolution simulation (DNS; the top set of panels) and low-resolution resimulations of the same ICs with the SLES model (the bottom set of panels). The maps are shown at t = 2/3 tcross,0. The leftmost panel in the top row shows the density slice at the native resolution of the DNS, while the other columns show coarse-grained values, with density being volume-weighted and eturb computed from Equation (8); the corresponding grid size is indicated at the top. The bottom panels show slices at the same time step from low-resolution resimulations using the corresponding grid sizes, where eturb is predicted using the SLES model. Thus, in total, four simulations are shown in the plot as indicated by the green shaded background: one DNS and three SLES resimulations. Low-resolution SLES resimulations produce smoother solutions than the DNS, but they capture the main global features of the small-scale turbulence, in particular, the correlation with gas density.
Paper: Capturing Turbulence with Numerical Dissipation: A Simple Dynamical Model for Unresolved Turbulence in Hydrodynamic Simulations
Authors: Vadim A. Semenov
Figure caption: Schematic overview of different types of subgrid turbulence models: implicit (ILES), explicit (LES), and proposed in this work, semi-implicit large-eddy simulations (SLES). The SLES model combines the explicit modeling of the subgrid turbulent energy and its decay, similar to explicit LES, with the implicit source term provided by numerical dissipation, akin to ILES. The labels on the x-axis of the power spectrum cartoon represent the driving scale (Ldrive), resolution scale (Δ), and physical dissipation scale (η). Green colors illustrate the explicit subgrid turbulence energy, eturb, while “cascade” and “decay” represent the source and sink terms Σ and ϵ in Equation (1), respectively.
Paper: Capturing Turbulence with Numerical Dissipation: A Simple Dynamical Model for Unresolved Turbulence in Hydrodynamic Simulations
Authors: Vadim A. Semenov
Figure caption: Cell-by-cell correlation between gas density and small-scale velocity dispersion, ﹩\sigma \equiv \sqrt{2{e}_{{\rm{turb}}}/\rho }﹩ on a 323 grid. The left column shows the DNS, i.e., each point shows coarse-grained values described in Section 3.1; the right column shows a 323 resimulation with the SLES model, where each point shows cell values at the native resolution, with eturb being predicted by the SLES model. The color shows the divergence of coarse-grain (for DNS) or resolved (for SLES) velocity. The rows correspond to different time steps: from top to bottom, 1, 3, and 5 tcross,0. Solid black lines show the medians and 18th to 84th interpercentile ranges. The red dashed line shows the adiabatic scaling corresponding to eturb ∝ ρ5/3, with the normalization being the same in all panels. The SLES model captures the relation between ρ and σ in terms of normalization, slope, the magnitude of scatter, and its scaling with the resolved velocity divergence.
Paper: Capturing Turbulence with Numerical Dissipation: A Simple Dynamical Model for Unresolved Turbulence in Hydrodynamic Simulations
Authors: Vadim A. Semenov
Figure caption: Comparison of the global mean small-scale turbulent energy, ﹩{E}_{{\rm{turb}}}\equiv {\langle {e}_{{\rm{turb}}}\rangle }_{{\rm{box}}}﹩, in coarse-grained DNS (dashed line) and low-resolution SLES resimulations with different values of the dissipation parameter, Cϵ (colored lines). For this comparison, we use the grid size of 323, i.e., the cell size Δ ≡ Lbox/32. In the DNS, eturb is computed following Equation (8) while in the low-resolution resimulations, it is tracked by the SLES model. The mean Eturb is normalized to the total initial kinetic energy, i.e., Ekin,0 ≡ Eturb(< Lbox) at t = 0. The time axis is normalized by the initial box-crossing time of turbulence, tcross,0 ≡ 0.5 Lbox/σ0, or ≈0.058 sound-crossing times for the initial Mach number of ﹩{ \mathcal M }\approx 8.6﹩. The value of Cϵ = 1.6 provides the best match to the global Eturb and therefore we will use it as our fiducial choice in the rest of the paper. The agreement with the DNS can be further improved by making Cϵ dependent on the local subgrid Mach number, as shown with the thin line and described in the text.
Paper: Capturing Turbulence with Numerical Dissipation: A Simple Dynamical Model for Unresolved Turbulence in Hydrodynamic Simulations
Authors: Vadim A. Semenov
Figure caption: Predicted subgrid velocity dispersion (top) and star formation efficiency per freefall time (bottom) in star-forming gas within 1 × 1 kpc patches as a function of the total gas density. Only star-forming gas with ϵff > 0.1% is used in the averages; σ is mass-weighted while ϵff is SFR-weighted. Results of each simulation are stacked using 21 snapshots equally spaced between t = 500 and 700 Myr, with solid lines showing running medians and shaded regions showing 16th–84th interpercentile ranges. For reference, the results from the PHANGS sample of nearby star-forming galaxies are shown with the dotted contour in the top panel (J. Sun et al. 2020, 75% of data points) and with the green band in the bottom panel (D. Utomo et al. 2018). On average, the SLES model predicts higher σturb and lower ϵff than the LES model, but the models lie within the scatter of each other and agree with the observed values.
Paper: Capturing Turbulence with Numerical Dissipation: A Simple Dynamical Model for Unresolved Turbulence in Hydrodynamic Simulations
Authors: Vadim A. Semenov
Figure caption: Face-on view of the same galaxy resimulated with our new SLES (top row) and explicit LES (bottom panel) models for unresolved turbulence. Columns show, from left to right, slices of gas density (n ≡ ρ/mp; for simplicity, we assume that the average molecular weight equals 1), subgrid turbulent velocity (﹩{\sigma }_{{\rm{turb}}}\equiv \sqrt{2{e}_{{\rm{turb}}}/\rho }﹩), locally variable star formation efficiency per freefall time (ϵff; see Equation (10)), and the surface density of SFR computed using star particles younger than 100 Myr. To guide the eye, thin lines indicate density isocontours corresponding to n = 20 cm−3. Overall, both models produce similar distributions of σturb, ϵff, and resulting ﹩{\dot{{\rm{\Sigma }}}}_{{\rm{\star }}}﹩. The most noticeable differences are the higher levels of turbulence on the disk outskirts and in the center in the former, which originate partially due to the filtering of the bulk shear in the explicit model.
Paper: Capturing Turbulence with Numerical Dissipation: A Simple Dynamical Model for Unresolved Turbulence in Hydrodynamic Simulations
Authors: Vadim A. Semenov
Figure caption: Comparison of coarse-grained DNS (dashed lines) with the SLES resimulations at corresponding resolutions (colored lines). The top panel shows the evolution of the global Mach number, ﹩{ \mathcal M }=\sqrt{2{E}_{{\rm{kin}}}}﹩, in the adopted units (〈ρ〉 ≡ 1). The bottom panel shows the mean small-scale turbulent energy, ﹩{E}_{{\rm{turb}}}\equiv {\langle {e}_{{\rm{turb}}}\rangle }_{{\rm{box}}}﹩, normalized by the initial total kinetic energy in the box, Ekin,0. The SLES model captures both the evolution of Eturb and its scale dependence. As shown in the Appendix, Eturb is underestimated for 1283 due to a lack of convergence on these small scales; however, we still show the results for this grid to demonstrate that the scaling of Eturb with the grid size remains the same in SLES.
Paper: Capturing Turbulence with Numerical Dissipation: A Simple Dynamical Model for Unresolved Turbulence in Hydrodynamic Simulations
Authors: Vadim A. Semenov
Figure caption: Power spectra in DNS (dashed lines) and SLES resimulations (solid colored lines): velocity (u; top), density (ρ; middle), and small-scale turbulent energy (eturb; bottom). The velocity power spectrum is compensated by k−5/3, while the ρ and eturb spectra are compensated by k−1.1. Gray dashed lines show the results from DNS at the native 10243 resolution, while thin colored dashed lines show coarse-grained statistics on the grids corresponding to the SLES resimulations. The wavenumber, k, is normalized by the minimal value corresponding to the box size, ﹩{k}_{{\rm{\min }}}=2\pi /{L}_{{\rm{box}}}﹩. Vertical dotted lines show Nyquist frequencies for low-resolution grids, i.e., kNy = πNgrid/Lbox.
Paper: Capturing Turbulence with Numerical Dissipation: A Simple Dynamical Model for Unresolved Turbulence in Hydrodynamic Simulations
Authors: Vadim A. Semenov
Figure caption: Top panel: comparison of the ρ–σ relation between the coarse-grained DNS (dashed black lines) and low-resolution SLES resimulation (solid blue lines). Bottom set of panels: dependence of scatter in σ on gas compression rate (negative divergence of the resolved velocity field, −∇ · u) at three fixed densities indicated with green lines in the top panel and labeled in the corner of each bottom panel. Each of the panels shows cells with densities bound by 1.5 times higher and 1.5 times lower than these values. For reference, the dotted red lines show the medians from the low-resolution resimulations with an explicit LES model proposed by W. Schmidt & C. Federrath (2011). The SLES model qualitatively reproduces the trend of σ with ∇ · u, although it does show a steeper increase at high compression rates. This behavior, however, is analogous to that in a more complex explicit LES model.
Paper: Capturing Turbulence with Numerical Dissipation: A Simple Dynamical Model for Unresolved Turbulence in Hydrodynamic Simulations
Authors: Vadim A. Semenov
Figure caption: Cell-by-cell distribution of the predicted total velocity dispersion, including subgrid turbulence and thermal motions, ﹩{\sigma }_{\mathrm{tot}}\equiv \sqrt{{\sigma }_{\mathrm{turb}}^{2}+{c}_{s}^{2}}﹩, as a function of gas density, colored by the galactocentric radius of the cell. The panels correspond to the simulations with our new SLES (top) and explicit LES models (bottom). The diagonal dotted lines show the constant values of the local virial parameter: from top to bottom, αvir = 100, 10, and 1. The contours show 68%, 95%, and 99% of cells weighted by mass. The data is stacked using 21 snapshots equally spaced between t = 500 and 700 Myr. The inner 500 pc, where the effects of shear are strong, is excluded from the plot. In the average ISM, both models produce similar distributions.
Paper: Capturing Turbulence with Numerical Dissipation: A Simple Dynamical Model for Unresolved Turbulence in Hydrodynamic Simulations
Authors: Vadim A. Semenov
Figure caption: Evolution of global star formation properties in our galaxy simulations with two different models for unresolved turbulence. The panels show, from top to bottom, the total SFR, the total mass of star-forming gas, its average density, and the local star formation efficiency per freefall time, ϵff. Solid lines show the tracks smoothed on a 100 Myr timescale using a Gaussian filter. The SLES model results in ∼2 times lower SFR than the explicit LES. This difference is mainly due to the difference in the predicted local ϵff, while the total mass and average density of star-forming gas are similar between the runs.
Paper: Planets Across Space and Time (PAST). VIII. Kinematic Characterization and Identification of Radial Velocity Variables for the LAMOST–Gaia–TESS Stars
Authors: Di Wu, Di-Chang Chen, Ji-Wei Xie, Ji-Lin Zhou, Hai-Feng Wang, Weikai Zong, Subo Dong, Maosheng Xiang, A-Li Luo
Figure caption: The distribution of disk stars in the vertical angular momentum Lz vs. radial action ﹩\sqrt{{J}_{\,\rm{R}\,}}﹩ plane for the LAMOST_MRS–Gaia–TESS sample, with the thin disk (N = 172,435), thick disk (N = 6361), and halo (N = 101) populations shown in blue, red, and black, respectively. Contour density maps illustrate the stellar distributions, with darker regions indicating higher star densities, the peak density location for each component is marked with a yellow star. Color bars for each component are scaled separately since their sample sizes differ significantly, the ticks show an estimated number of samples in each grid. As shown, different components occupy distinct regions in the Lz–JR plane, reflecting their kinematic differences.
Paper: Planets Across Space and Time (PAST). VIII. Kinematic Characterization and Identification of Radial Velocity Variables for the LAMOST–Gaia–TESS Stars
Authors: Di Wu, Di-Chang Chen, Ji-Wei Xie, Ji-Lin Zhou, Hai-Feng Wang, Weikai Zong, Subo Dong, Maosheng Xiang, A-Li Luo
Figure caption: Distribution and number density of stellar streams in the [Fe/H] vs. [α/Fe] plane for LAMOST_MRS–Gaia–TESS sample. Each subplot represents a unique stellar stream, with data points color coded by Vtot and arranged from top left to bottom right according to [Fe/H]. Contour lines indicate stellar density, where darker shades represent higher densities, while a yellow star denotes the typical value of [Fe/H] for each stream. The two error bars represent the values for thin disk stars (blue solid line) and thick disk stars (red dashed line) for reference.
Paper: Planets Across Space and Time (PAST). VIII. Kinematic Characterization and Identification of Radial Velocity Variables for the LAMOST–Gaia–TESS Stars
Authors: Di Wu, Di-Chang Chen, Ji-Wei Xie, Ji-Lin Zhou, Hai-Feng Wang, Weikai Zong, Subo Dong, Maosheng Xiang, A-Li Luo
Figure caption: Kinematic and chemical properties (from top to bottom: total velocity Vtot, [Fe/H], and [α/Fe]) as functions of the relative probability between thick disk to thin disk (TD/D) for the LAMOST_MRS–Gaia–TESS stars. The vertical dashed lines indicate where TD/D = 1. Medians and 1σ dispersion for different TD/D groups are marked in the plots. Histograms of TD/D and kinematic/chemical properties are displayed in the topmost and right panels, respectively.
Paper: Planets Across Space and Time (PAST). VIII. Kinematic Characterization and Identification of Radial Velocity Variables for the LAMOST–Gaia–TESS Stars
Authors: Di Wu, Di-Chang Chen, Ji-Wei Xie, Ji-Lin Zhou, Hai-Feng Wang, Weikai Zong, Subo Dong, Maosheng Xiang, A-Li Luo
Figure caption: Same as Figure 4 but for the distribution in the [α/Fe] vs. [Fe/H] plane for the LAMOST_MRS–Gaia–TESS sample. The red dashed line represents the division between the thick and thin disks from Y. S. Lee et al. (2011), and the distribution reveals distinct chemical abundance trends among these populations.
Paper: Planets Across Space and Time (PAST). VIII. Kinematic Characterization and Identification of Radial Velocity Variables for the LAMOST–Gaia–TESS Stars
Authors: Di Wu, Di-Chang Chen, Ji-Wei Xie, Ji-Lin Zhou, Hai-Feng Wang, Weikai Zong, Subo Dong, Maosheng Xiang, A-Li Luo
Figure caption: Top panel: Galactocentric radius (R) vs. height (Z) for the LAMOST_MRS–Gaia–TESS sample. A yellow star marks the location of the Sun (8.18 kpc, 0.027 kpc). Bottom panel: Galactocentric radius (R) vs. angle (θ) for the MRS sample, with (8.18 kpc, 0∘) marking the position of Sun. Red points represent the TOI host stars.
Paper: Planets Across Space and Time (PAST). VIII. Kinematic Characterization and Identification of Radial Velocity Variables for the LAMOST–Gaia–TESS Stars
Authors: Di Wu, Di-Chang Chen, Ji-Wei Xie, Ji-Lin Zhou, Hai-Feng Wang, Weikai Zong, Subo Dong, Maosheng Xiang, A-Li Luo
Figure caption: The Toomre diagram of LAMOST_MRS–Gaia–TESS stars for different Galactic components (top panel) and stellar streams (bottom panel). Different colors represent different memberships. Dotted lines indicate constant values of the total Galactic velocity Vtot in steps of 25 km s−1 and 50 km s−1.
Paper: Planets Across Space and Time (PAST). VIII. Kinematic Characterization and Identification of Radial Velocity Variables for the LAMOST–Gaia–TESS Stars
Authors: Di Wu, Di-Chang Chen, Ji-Wei Xie, Ji-Lin Zhou, Hai-Feng Wang, Weikai Zong, Subo Dong, Maosheng Xiang, A-Li Luo
Figure caption: Radial velocity and stellar parameters obtained from Gaia RVS (left panel) and LAMOST (right panel) in comparison with APOGEE. Dark red fitted regression lines illustrate the correlations between the respective data sets, with slope and intercept values indicated. The line y = x is plotted as a red dashed line for reference.
Paper: Capturing Turbulence with Numerical Dissipation: A Simple Dynamical Model for Unresolved Turbulence in Hydrodynamic Simulations
Authors: Vadim A. Semenov
Figure caption: Convergence of the average eturb estimate from DNS with resolution (line styles) and coarse-graining scale (line colors). Our fiducial 10243 DNS is converged for 643 and all coarser grids, while at 1283 significant deviations become noticeable as it probes eturb on the scales affected by numerical dissipation: 1024/128 = 8 cells.
Paper: Planets Across Space and Time (PAST). VIII. Kinematic Characterization and Identification of Radial Velocity Variables for the LAMOST–Gaia–TESS Stars
Authors: Di Wu, Di-Chang Chen, Ji-Wei Xie, Ji-Lin Zhou, Hai-Feng Wang, Weikai Zong, Subo Dong, Maosheng Xiang, A-Li Luo
Figure caption: Orbital eccentricity (left panel) and binary mass ratio (right panel) as functions of orbital period for the well-fitted binary samples. Both parameters show positive correlations with orbital period, with Pearson correlation coefficients of 0.48 and 0.33, respectively, and p-values < 0.05. Dotted lines in the left panel represent the upper envelope from T. Mazeh (2008). On the eccentricity–period plane, the well-fitted sample shows a concentrated distribution of eccentricities across all periods, with a tendency for lower eccentricities at shorter periods indicating tidal circularization effort.