adsabs/AstroCLIMB huggingface dataset:
Paper: A Novel Approach to Identifying Substructures through Analysis of Metallicity Distribution Functions
Authors: Young Kwang Kim, Young Sun Lee, 영선 이, Timothy C. Beers
Figure caption: MDFs of the selected substructures (red histograms) in bins of rapo and OP for the St﹩\ddot{a}﹩ckel potential. Bins of rapo increase from top to bottom; the OP bins increase from left to right in a given rapo bin. The ranges of rapo (black) and OP (bold black) are listed in the upper left corner of the respective panels. Because the shapes of MDFs are slightly different from Gaussian distributions, overlapping substructures are distinguished not by the division of the two Gaussian fits, represented with the orange and green curves, but by the median [Fe/H] of the bin corresponding to the minimum between the two peaks. These [Fe/H] values are indicated by the vertical blue dotted lines; the vertical black dotted lines represent the MDF peaks of the selected substructures. See text for the adopted rapo and OP ranges. Our analysis only considers the bins with N ≥ 50.
Paper: A Novel Approach to Identifying Substructures through Analysis of Metallicity Distribution Functions
Authors: Young Kwang Kim, Young Sun Lee, 영선 이, Timothy C. Beers
Figure caption: Top panels: distribution of LRS 2 stars in the Vϕ vs. Vr plane (left panel) and MDFs of stars in each bin of velocity space (right panel). The areas in velocity space in the left panel are shown as large circles with a radius of 106 km s−1, with their centers (A, B, C, D, and E) listed at the bottom. The small circle marks the boundary of ED-2 from E. Dodd et al. (2023). Colored points indicate the metallicity of each star, coded by the color bar. Bottom panels: similar to the top panels, but only for stars in the B region in the Vθ vs. Vϕ plane. Note that these stars satisfy ﹩\sqrt{{[{V}_{{\rm{r}}}-(-110)]}^{2}+{[{V}_{\phi }-(-290)]}^{2}}﹩< 106 km s−1 in the top left panel (see text). The black dotted vertical lines in the right panels are the MDF peaks for another stellar stream in LRS 2.
Paper: A Novel Approach to Identifying Substructures through Analysis of Metallicity Distribution Functions
Authors: Young Kwang Kim, Young Sun Lee, 영선 이, Timothy C. Beers
Figure caption: Top panels: same as in Figure 7, but for stars with ∣b∣ > 40°, d > 3 kpc (distance from the Sun), which are the criteria used by R. P. Naidu et al. (2020), with an additional cut of r > 10 kpc (distance from the Galactic center) applied. Bottom panels: MDFs of individual substructures along with the Gaussian fits of those substructures and their sum (bottom left) and all substructure stars (bottom right) in the region LZ < −2 × 103 kpc km s−1 and Etot > − 1.4 × 105 km2 s−2 of stars in the top panels. The region of LZ < −2 × 103 kpc km s−1 and Etot > −1.4 × 105 km2 s−2 (dotted orange box in the top panels) includes the I’itoi, Sequoia, and Arjuna substructures with high energy and strong retrograde motion (R. P. Naidu et al. 2020). In the bottom right panel, the MDF with 0.1 dex bin size (brown) distinctly shows at least three [Fe/H] peaks corresponding to Arjuna, Sequoia, and I’itoi, in order of metal-rich to metal-poor.
Paper: A Novel Approach to Identifying Substructures through Analysis of Metallicity Distribution Functions
Authors: Young Kwang Kim, Young Sun Lee, 영선 이, Timothy C. Beers
Figure caption: Top panel: differences in the eccentricities of our sample stars between the St﹩\ddot{a}﹩ckel (eS) and McMillan (eM) potentials, as a function of eM. The black circles are the stars in the eccentricity range 0.5 < eS ≤ 0.7 calculated from the St﹩\ddot{a}﹩ckel potential. The blue, black, and red circles between the two vertical blue lines represent the stars in the range 0.5 < eM ≤ 0.7 computed from the McMillan potential. The bottom left panel is the MDF of the stars in the range 0.5 < e≤0.7 in both potentials. The bottom middle panel shows the MDFs of the stars for (eM − eS) ≥ 0. The black histogram is produced with the stars (black circles in the upper right corner of the top panel) in the ranges 0.5 < eS ≤ 0.7 and eM > 0.7, while the blue histogram is produced with the stars (blue circles in the top panel) in the ranges 0.5 < eM ≤ 0.7 and eS ≤ 0.5. Similarly, the bottom right panel is for (eM − eS) < 0. The black histogram is produced with the stars (black circles in the lower left corner of the top panel) in the ranges 0.5 < eS ≤ 0.7 and eM ≤ 0.5, while the red one applies for stars (red circles in the top panel) in the ranges 0.5 < eM ≤ 0.7 and eS > 0.7.
Paper: A Novel Approach to Identifying Substructures through Analysis of Metallicity Distribution Functions
Authors: Young Kwang Kim, Young Sun Lee, 영선 이, Timothy C. Beers
Figure caption: Top panel: MDFs of the LRS 2 stars in region B of Figure 5, divided into Vθ > −50 km s−1 (orange) and Vθ ≤ −50 km s−1 (magenta). Middle panel: MDFs of all LRS 2 stars with two bin sizes (blue with 0.2 dex bins and green with 0.1 dex bins). The orange and magenta curves denote best fits of the green histogram by two Gaussians with peaks at [Fe/H] = −2.28 and −1.64. The black curve represents the sum of the individual Gaussians. Bottom panel: MDFs of two proposed populations among the LRS 2 stars, dubbed LRS 2A (blue) and LRS 2B (red). LRS 2B stars are chosen by applying an [Fe/H] < −2.1 cut (obtained by the Gaussian fits in the middle panel) to the stars of LRS 2 with Vθ > −50 in the B region, whereas LRS 2A stars are the rest of LRS 2, excluding LRS 2B stars. The black dotted vertical lines in each panel are the MDF peaks for a new stellar stream, LRS 2B and LRS 2.
Paper: A Novel Approach to Identifying Substructures through Analysis of Metallicity Distribution Functions
Authors: Young Kwang Kim, Young Sun Lee, 영선 이, Timothy C. Beers
Figure caption: MDFs of each substructure selected in the St﹩\ddot{a}﹩ckel potential (left panels) and McMillan potential (right panels). GSE is chosen by the selection method of D. Horta et al. (2023) for the St﹩\ddot{a}﹩ckel potential and that of D. K. Feuillet et al. (2021) for the McMillan potential, with an additional cut of e > 0.7. The numbers in parentheses are the number of common stars in the St﹩\ddot{a}﹩ckel and McMillan potentials for each substructure. The black dotted vertical lines in the top left and top right panels for the St﹩\ddot{a}﹩ckel and McMillan potentials, respectively, are the MDF peaks of LRS 1, LRS 2, and GSE. The black dotted vertical lines in the bottom left and bottom right panels are the MDF peaks for LRS 3 and LRS 4 for the St﹩\ddot{a}﹩ckel and McMillan potentials, respectively.
Paper: A Novel Approach to Identifying Substructures through Analysis of Metallicity Distribution Functions
Authors: Young Kwang Kim, Young Sun Lee, 영선 이, Timothy C. Beers
Figure caption: Distribution of APOGEE DR17 stars in the metallicity range −1.3 < [Fe/H] < −0.9 in the [Al/Fe]–[Fe/H] (top panel) and [Mg/Fe]–[Fe/H] (bottom panel) plane. These stars have uncertainties for both [Al/Fe] and [Mg/Fe] less than 0.05 dex and S/N greater than 100 in the H-band spectrum. The solid red line is a reference line to separate the accreted stars from the in situ stars (see the text for the derivation of the line). In situ stars with [Al/Fe] ≥ −0.075 are shown as black circles, while accreted ones with [Al/Fe] < −0.075 are shown as blue circles.
Paper: A Novel Approach to Identifying Substructures through Analysis of Metallicity Distribution Functions
Authors: Young Kwang Kim, Young Sun Lee, 영선 이, Timothy C. Beers
Figure caption: Spatial and velocity distributions of LRS 2B (blue), ED-2 (green) of E. Dodd et al. (2023), ED-2 candidates (red), and ED-2_DESI sample (orange) from B. Kim et al. (2025), in heliocentric coordinates. In the top three panels, the blue, red, green, and orange arrows represent the mean projected velocity vectors for these substructures. This figure is displayed in the same coordinate system as that of E. Balbinot et al. (2023) for comparison.
Paper: The M- and X-class White-light Flares in Super Active Region NOAA 13664/13697 Observed by ASO-S/LST/WST
Authors: Zhichen Jing, Ying Li, Jingwei Li, Qiao Li
Figure caption: Overview of the flare events in NOAA 13664 and 13697. (a) and (b) GOES SXR 1–8 Å flux during the appearing time of NOAA 13664 and 13697. The red and blue vertical lines indicate the WLFs at 3600 Å and non-WLFs from the two regions, respectively. (c)–(f) WST 3600 Å images and its base-difference ones for two example WLFs from the two regions. The WL brightenings are outlined by red contours, which are derived from the base-difference images. (g) Histogram of SXR duration with red and blue colors representing the WLFs and non-WLFs, respectively. (h) Scatter plot of SXR duration vs. peak SXR flux with red and blue plus symbols indicating the WLFs and non-WLFs, respectively. (i) Spatial distribution of WLFs (red color) and non-WLFs (blue color). The plus and diamond symbols denote the flares from NOAA 13664 and 13697, respectively.
Paper: The M- and X-class White-light Flares in Super Active Region NOAA 13664/13697 Observed by ASO-S/LST/WST
Authors: Zhichen Jing, Ying Li, Jingwei Li, Qiao Li
Figure caption: (a) Time profile of the sunspot number (solid curve) and its smoothed profile (dotted curve) during the period of 2022 October and 2024 June. (b) Temporal evolution of the WLF occurrence rate (magenta curve, corresponding to the right axis), together with the smoothed sunspot number (dotted curve) during the period. The red and blue asterisks in this panel (and also in panel (c)) mark the occurrence rates of WLFs in NOAA 13664 and 13697, respectively. (c) Scatter plot of the WLF occurrence rate vs. the smoothed sunspot number. The straight line is their linear fit.
Paper: The M- and X-class White-light Flares in Super Active Region NOAA 13664/13697 Observed by ASO-S/LST/WST
Authors: Zhichen Jing, Ying Li, Jingwei Li, Qiao Li
Figure caption: Top panels show the relationships of maximum pixel enhancement (rp), maximum mean enhancement (rm), corrected area (S), WL duration (τ), and WL energy (E) with peak SXR flux for the 22 WLFs in NOAA 13664 and 13697. Bottom panels exhibit the relationships of E with rp, rm, S, and τ. When the Pcc value (in red) is greater than 0.40, we make a linear fit (see the black curve). The Pcc values in green denote the results of the long-term-continuous sample in Z. Jing et al. (2024) for reference.
Paper: Theoretical Perspective on the Occurrence of Electrostatic Solitary Waves and Double Layers in Ganymede’s Wake Region
Authors: R. Rubia, M. B. Dhanya, Kuldeep Singh, Ioannis Kourakis
Figure caption: Variation of ω with the wavenumber k for the normalized parameters corresponding to (a) Set 1: ni,1,0 = 0.07, ni,3,0 = 0.07, ne,0 = 0.93, nb,0 = 0.07, σi,1 = 0.05, σi,2 = 0.2, σi,3 = 0.1, σb = 0.1 and vb,0 = 0.05, and κ = 6 and (b) Set 2: ni,1,0 = 0.13, ni,3,0 = 0.33, ne,0 = 0.87, nb,0 = 0.13, σi,1 = 0.2, σi,2 = 0.2, σi,3 = 0.5, σb = 0.1, vb,0 = 0.001, and κ = 6.
Paper: The M- and X-class White-light Flares in Super Active Region NOAA 13664/13697 Observed by ASO-S/LST/WST
Authors: Zhichen Jing, Ying Li, Jingwei Li, Qiao Li
Figure caption: Histograms of the maximum pixel enhancement (rp) (a), maximum mean enhancement (rm) (b), brightening area (S) corrected for projection effect (c), and WL duration (τ) at 3600 Å (d). Results for the 22 WLFs from NOAA 13664 and 13697 are shown in red, and those for the long-term-continuous sample in Z. Jing et al. (2024) are in green for comparison.
Paper: The M- and X-class White-light Flares in Super Active Region NOAA 13664/13697 Observed by ASO-S/LST/WST
Authors: Zhichen Jing, Ying Li, Jingwei Li, Qiao Li
Figure caption: (a) and (b) Histograms of the peak-time difference between WL and the SXR time derivative or HXR. Purple and orange colors represent Group 1 and 2 WLFs, respectively. (c)–(f) Distributions of the maximum pixel enhancement (rp), maximum mean enhancement (rm), corrected area (S), and WL duration (τ) of Group 1 (purple) and 2 (orange) WLFs.
Paper: Theoretical Perspective on the Occurrence of Electrostatic Solitary Waves and Double Layers in Ganymede’s Wake Region
Authors: R. Rubia, M. B. Dhanya, Kuldeep Singh, Ioannis Kourakis
Figure caption: Slow ion-acoustic mode corresponding to the parameters of Set 2. We have depicted the variation of (a) the pseudopotential S(ϕ) with ϕ, (b) the electrostatic potential ϕ, and (c) the electric field, with the moving coordinate ξ.
Paper: Theoretical Perspective on the Occurrence of Electrostatic Solitary Waves and Double Layers in Ganymede’s Wake Region
Authors: R. Rubia, M. B. Dhanya, Kuldeep Singh, Ioannis Kourakis
Figure caption: Variation of (a) pseudopotential, S(ϕ) with ϕ, (b) electrostatic potential ϕ, and (c) electric field with ξ for the fast ion-acoustic mode 1 corresponding to Set 2.
Paper: Deep Potential: Recovering the Gravitational Potential and Local Pattern Speed in the Solar Neighborhood with GDR3 Using Normalizing Flows
Authors: Taavet Kalda, Gregory M. Green
Figure caption: A demonstration of the performance of our normalizing-flow model of the stellar phase-space distribution function in three different projections. We plot two-dimensional histograms of the selected stars (left column) and of 221 = 2,097,152 points sampled from the trained normalizing flow (middle column), as well as a comparison between the two (right column). The top row shows a face-on view of the 1000 pc data set, projected onto the x–y plane. The middle row shows one velocity-space projection (vϕ vs. vR), and the bottom row shows the sky projection (in a Lambert cylindrical equal-area projection: ℓ vs. ﹩\sin b﹩). The density of the sampled normalizing-flow points has been renormalized by an overall constant to match the density of the stars in the training set. For each bin, we define the Poisson significance as ﹩({n}_{{\rm{NF}}}-{n}_{{\rm{data}}})/\sqrt{{n}_{{\rm{data}}}}﹩, where nNF is the renormalized number of samples in the bin drawn from the normalizing flow and ndata is the number of sources in the data set in the same bin. As can be seen above, our normalizing flow captures nearly all prominent features in the stellar distribution, with the exception of some residuals seen in areas of high dust extinction in the sky projection. We thus obtain a smooth, differentiable representation of the Galactic stellar population.
Paper: Deep Potential: Recovering the Gravitational Potential and Local Pattern Speed in the Solar Neighborhood with GDR3 Using Normalizing Flows
Authors: Taavet Kalda, Gregory M. Green
Figure caption: Two-dimensional projections of the model acceleration ﹩{\boldsymbol{a}}=-{\rm{\nabla }}{\rm{\Phi }}({\boldsymbol{x}})﹩ within r < 0.8 kpc. The left panel shows the average azimuthal acceleration in the x–y plane. The middle panel shows the average circular velocity in the x–y plane, calculated as ﹩\sqrt{-R{a}_{R}}﹩. The right panel shows the average vertical acceleration in the R–z plane. The projections are obtained by averaging over the third spatial dimension. For the left and middle panels, we integrate over −0.25 kpc < z < 0.25 kpc, while for the right panel, we integrate over −0.25 kpc < y < 0.25 kpc (holding R constant).
Paper: Deep Potential: Recovering the Gravitational Potential and Local Pattern Speed in the Solar Neighborhood with GDR3 Using Normalizing Flows
Authors: Taavet Kalda, Gregory M. Green
Figure caption: Vertical (left panel) and radial (right panel) density profiles for the full nonparametric model (gray) vs. a simple analytic model fit (red). The gray background in each panel shows the amount of volume that has a given density as a function of a single spatial coordinate. The analytic model is made up of three Miyamoto–Nagai disks and a Navarro–Frenk–White profile and is evaluated along x = y = 0 in the left panel and y = 0 along with varying levels of z = const for the right panel. We observe significant fluctuations in the full nonparametric model density, which may arise in part from density estimates ρ = ∇2Φ(x)/(4πG) being sensitive to small fluctuations in the nonparametric potential model, nonstationarity in the Milky Way, or real density fluctuations in the Milky Way.
Paper: Deep Potential: Recovering the Gravitational Potential and Local Pattern Speed in the Solar Neighborhood with GDR3 Using Normalizing Flows
Authors: Taavet Kalda, Gregory M. Green
Figure caption: Comparison of the value for the best-fit pattern speed (vertical blue line) within 1 kpc of the Sun from this work and literature estimates (shown as dots) for the pattern speeds of the Galactic bar and spiral arms in the Milky Way, adapted from Figure 10 of J. A. S. Hunt & E. Vasiliev (2025). Uncertainties are provided when available. Qualitative estimates are shown in red and quantitative estimates in blue. Some works provide either multiple pattern speed models (A. Castro-Ginard et al. 2021; Y. C. Joshi & S. Malhotra 2023; Y. R. Khalil et al. 2025) or multiple values for the respective feature (D. Kawata et al. 2021; W. H. Trick et al. 2021).
Paper: Deep Potential: Recovering the Gravitational Potential and Local Pattern Speed in the Solar Neighborhood with GDR3 Using Normalizing Flows
Authors: Taavet Kalda, Gregory M. Green
Figure caption: Comparisons of various one-dimensional projections of the accelerations ﹩{\boldsymbol{a}}=-{\rm{\nabla }}{\rm{\Phi }}({\boldsymbol{x}})﹩ for the full model (in gray) and a simple analytic model fit (in red). The gray background in each panel shows the amount of volume that has a given acceleration (or circular velocity) as a function of a single spatial coordinate. The left panel shows azimuthal acceleration vs. azimuth, the middle panel shows circular velocity ﹩\sqrt{-R{a}_{R}}﹩ vs. R, and the right panel shows vertical acceleration vs. height above the Sun. The analytic model is made up of three Miyamoto–Nagai disks and a Navarro–Frenk–White profile for the halo and is evaluated along the respective axes, passing through the Sun’s location.
Paper: Deep Potential: Recovering the Gravitational Potential and Local Pattern Speed in the Solar Neighborhood with GDR3 Using Normalizing Flows
Authors: Taavet Kalda, Gregory M. Green
Figure caption: Mean completeness of the 56,17,061 sources in the 1000 pc data set with full six-dimensional phase-space kinematics in Gaia DR3 that pass the quality cuts specified in Section 3.1 as a function of sky position (top subplot), apparent magnitude G, color G − GRP, and extinction E from G. Edenhofer et al. (2024) of the source (bottom row). Each panel in the bottom row additionally includes a histogram of the sources in the data set (light gray). The completeness represents the probability that a source with the given physical parameters is in the data set and is estimated by calculating the ratio of the number of sources in the data set to the number of sources within the parent Gaia DR3 catalog with G < 15. We observe the dependence of completeness on crowding (especially at Baade’s window), apparent magnitude, color, and a weak dependence on the scanning pattern of Gaia.
Paper: Deep Potential: Recovering the Gravitational Potential and Local Pattern Speed in the Solar Neighborhood with GDR3 Using Normalizing Flows
Authors: Taavet Kalda, Gregory M. Green
Figure caption: Histogram of extinction-corrected absolute magnitude vs. distance from the Sun for the 5,617,061 sources in the final data set (in color) compared to the stars excluded from the data set that have full six-dimensional phase-space measurements and pass our quality cuts (in gray). Along each axis, we additionally show one-dimensional histograms of all sources with six-dimensional phase-space measurements. The stars with six-dimensional phase-space measurements are nearly uniformly complete in the range of apparent magnitudes 5 < G < 15. The final data set is selected to have spatially uniform completeness for all sources within the chosen range of extinction-corrected absolute magnitudes, resulting in a box that extends from rin = 100 pc to rout = 1000 pc in the above diagram.
Paper: Deep Potential: Recovering the Gravitational Potential and Local Pattern Speed in the Solar Neighborhood with GDR3 Using Normalizing Flows
Authors: Taavet Kalda, Gregory M. Green
Figure caption: Maximum distance ﹩{r}_{{\rm{\max }}}﹩ within which >95% of the 1 kpc Gaia data set stars would pass the apparent magnitude limit, G < 14.8. The crowded Baade’s window near the Galactic center is handled separately with a brightness limit of G < 13.
Paper: Persistent Behavior of Low-energy Solar Energetic Particles Observed by Parker Solar Probe During Orbit 15
Authors: N. V. Sarlis, G. Livadiotis, D. J. McComas, M. E. Cuesta, L. Y. Khoo, M. M. Shen, J. S. Rankin, N. A. Schwadron
Figure caption: The same as Figure 10, but for events 14–16 marked in each case with the large blue numbers.
Paper: Persistent Behavior of Low-energy Solar Energetic Particles Observed by Parker Solar Probe During Orbit 15
Authors: N. V. Sarlis, G. Livadiotis, D. J. McComas, M. E. Cuesta, L. Y. Khoo, M. M. Shen, J. S. Rankin, N. A. Schwadron
Figure caption: The maximum value of flux ﹩{J}_{{\rm{\max }}}﹩ depicted in band I with red squares (A) and in band II with magenta circles (B) vs. min(ΔS300) estimated (see Section 2.7) on the basis of OTHurst and OTFNT, respectively. The thick red (magenta) straight lines correspond to a least squares fit (with the thinner lines indicating the ±1 asymptotic standard error of the slope) and have been drawn as a guide to the eye to show the exponential relation between min(ΔS300) and ﹩{J}_{{\rm{\max }}}﹩.
Paper: Persistent Behavior of Low-energy Solar Energetic Particles Observed by Parker Solar Probe During Orbit 15
Authors: N. V. Sarlis, G. Livadiotis, D. J. McComas, M. E. Cuesta, L. Y. Khoo, M. M. Shen, J. S. Rankin, N. A. Schwadron
Figure caption: The same as Figure 10, but for events 3 and 4. In the lowest panel, we also depict the period from 30,000 to 40,000 minutes, which does not include any false alarm, as mentioned in the main text.
Paper: Persistent Behavior of Low-energy Solar Energetic Particles Observed by Parker Solar Probe During Orbit 15
Authors: N. V. Sarlis, G. Livadiotis, D. J. McComas, M. E. Cuesta, L. Y. Khoo, M. M. Shen, J. S. Rankin, N. A. Schwadron
Figure caption: The same as Figure 10, but for the events 5–13 marked in each case with the large blue numbers.
Paper: Classifying Cool Dwarfs: Comprehensive Spectral Typing of Field and Peculiar Dwarfs Using Machine Learning
Authors: Tianxing Zhou, Christopher A. Theissen, S. Jean Feeser, William M. J. Best, Adam J. Burgasser, Kelle L. Cruz, Lexu Zhao
Figure caption: Confusion Matrix for the RF model in classifying classes. All field and peculiar classes are included. The values are normalized across each row (True label), i.e., each row sums to 1. This graph demonstrates the predicted dwarf label by the RF model versus the actual label defined in the data set. The RF model performs reasonably well for metallicity and INT-G classes, but struggles more with the VL-G class.
Paper: Classifying Cool Dwarfs: Comprehensive Spectral Typing of Field and Peculiar Dwarfs Using Machine Learning
Authors: Tianxing Zhou, Christopher A. Theissen, S. Jean Feeser, William M. J. Best, Adam J. Burgasser, Kelle L. Cruz, Lexu Zhao
Figure caption: Same as Figure 11 for the SVM model.
Paper: Classifying Cool Dwarfs: Comprehensive Spectral Typing of Field and Peculiar Dwarfs Using Machine Learning
Authors: Tianxing Zhou, Christopher A. Theissen, S. Jean Feeser, William M. J. Best, Adam J. Burgasser, Kelle L. Cruz, Lexu Zhao
Figure caption: Top: all M, L, and T field spectral standards plotted together in order. All spectra are normalized around 1.27 μm, and different spectral types are separated by an offset. The shaded region is the telluric absorption that was masked. The normalization region (1.27–1.28 μm) used to scale each spectrum is indicated by the pair of solid vertical lines and the hatched band. Bottom: the relative feature importance score. This feature importance is an attribute specific to the RF classifier.