arXiv subjects

Yang-Ping Luo

Publications and source records attributed to Yang-Ping Luo.

7 recordsLinked to original sources

arXiv2025

Towards Understanding the Milky Way's Matter Field and Dynamical Accretion History based on AI-GS3 Hunter

We present GS3 Hunter (Galactic-Seismology Substructures and Streams Hunter), a novel deep-learning method that combines Siamese Neural Networks and K-means clustering to identify substructures and streams in stellar kinematic data. Applied to Gaia EDR3 and GALAH DR3, it recovers known groups (e.g., Thamnos, Helmi, GSE, Sequoia) and, with DESI dataset, reveals that GSE consists of four distinct components (GSH-GSH1 through GSE-GSH4), implying a multi-event accretion origin. Tests on LAMOST K-giants recover Sagittarius, Hercules-Aquila, and Virgo Overdensity, while also uncovering new substructures. Validation with FIRE simulations shows good agreement with previous results. GS3 Hunter thus offers a powerful tool to understand the Milky Way's halo assembly and tidal history.

astro-ph.GA↗

arXiv2024

The Structure, Populations and Kinematics of the Milky Way central and inner Bulge with OGLE, APOGEE and Gaia data

We present an analysis of the structure, kinematics, and chemo-dynamical properties of the Milky Way bulge using RR Lyrae stars from OGLE, and giant stars from APOGEE and Gaia that have distances placing them in the inner Galaxy. Firstly, using a sample of 1,879 ab-type RR Lyrae stars (RRabs) from OGLE-IV, we identified three populations: central bulge RRabs, the inner bulge RRabs, and halo or disk interlopers, based on their apocenters derived from orbital integration. Inner bulge RRabs kinematically align with the Galactic bar, while central bulge RRabs show slower rotation with lower velocity dispersion. Higher velocity dispersion stars were identified as halo/disk interlopers. Then, orbital analysis of 28,188 APOGEE Red Clump and Red Giant Branch stars revealed kinematic properties consistent with RRabs, and the chemical abundance distribution displayed a bimodal stellar density pattern, suggesting complex star evolution histories and slightly different star formation histories for the inner bulge and central bulge. The differences in the density distribution on the $|\mathrm{Z}|_{\text{max}}$-eccentricity plane for the central bulge, inner bulge, and interlopers are clearly detected. It is found that the classification of bulge stars based on orbital parameters, rather than solely on metallicity, provides a more accurate population separation. As the inner bulge, which contains the highest fraction of stars, traces the bar formed by the instability of the Galactic disk, our results support that pseudo-bulge is the primary origin of the bulge. Furthermore, fitting the observed data to both the boxy and X-shaped bulge models indicated a preference for the boxy bulge.

astro-ph.GA↗

arXiv2024

Galactic-Seismology Substructures and Streams Hunter with LAMOST and Gaia. I. Methodology and Local Halo Results

We present a novel, deep-learning based method -- dubbed Galactic-Seismology Substructures and Streams Hunter, or GS$^{3}$ Hunter for short, to search for substructures and streams in stellar kinematics data. GS$^{3}$ Hunter relies on a combined application of Siamese Neural Networks to transform the phase space information and the K-means algorithm for the clustering. As a validation test, we apply GS$^{3}$ Hunter to a subset of the Feedback in Realistic Environments (FIRE) cosmological simulations. The stellar streams and substructures thus identified are in good agreement with corresponding results reported earlier by the FIRE team. In the same vein, we apply our method to a subset of local halo stars from the Gaia Early Data Release 3 and GALAH DR3 datasets, and recover several, previously known dynamical groups, such as Thamnos 1+2, Hot Thick Disk, ED-1, L-RL3, Helmi 1+2, and Gaia-Sausage-Enceladus, Sequoia, VRM, Cronus, Nereus. Finally, we apply our method without fine-tuning to a subset of K-giant stars located in the inner halo region, obtained from the LAMOST Data Release 5 (DR5) dataset. We recover three, previously known structures (Sagittarius, Hercules-Aquila Cloud, and the Virgo Overdensity), but we also discover a number of new substructures. We anticipate that GS$^{3}$ Hunter will become a useful tool for the community dedicated to the search of stellar streams and structures in the Milky Way (MW) and the Local group, thus helping advance our understanding of the stellar inner and outer halos, and of the assembly and tidal stripping history in and around the MW.

astro-ph.GA↗

arXiv2024

Asymmetric Drift Map of the Milky Way disk Populations between 8$-$16 kpc with LAMOST and Gaia datasets

The application of asymmetric drift (AD) tomography across different populations provides valuable insights into the kinematics, dynamics, and rotation curves of the Galactic disk. By leveraging common stars identified in both the LAMOST and Gaia surveys, alongside Gaia DR3's circular velocity curve, we conducted a qualitative exploration of asymmetric drift distributions within the Galactic disk spanning distances from 8 to 16 kpc. In the R-Z plane, we observed that the asymmetric drift is minimal near the mid-plane of the Galactic disk and gradually increases with vertical distance, resulting in a distinctive ``horn" shape. Additionally, our analysis revealed that populations with higher [$α$/Fe] ratios exhibit greater asymmetric drift compared to those with lower [$α$/Fe] ratios. Specifically, we found the asymmetric drift around the solar location to be approximately 6 km s$^{-1}$, with a median value of 16 km s$^{-1}$ across the entire sample. Notably, the median asymmetric drift in the northern region of the Galactic disk (20 km s$^{-1}$) surpasses that in the southern region (13 km s$^{-1}$), with errors remaining within 2 km s$^{-1}$. Furthermore, our investigation into mono-age stellar populations unveiled that older stellar populations tend to exhibit larger asymmetric drift and velocity dispersion, aligning closely with predictions from previous numerical models. Finally, based on chemical compositions, we observed that the median asymmetric drift of the thick disk significantly exceeds that of the thin disk and found that star formation within the thick disk primarily occurred earlier than 8-10 billion years, whereas the thin disk's predominant star formation period spanned 6-8 billion years ago.

astro-ph.GA↗

arXiv2022

Kinematic-Chemical analysis and Time tagging for the Diagonal Ridge Structure of the Galactic Outer Disk with LAMOST Red Giant Branch Stars

We investigate the kinematic-chemical distribution of Red Giant Branch (RGB) stars from the LAMOST survey crossed matched with Gaia DR2 proper motions, and present time tagging for the well-known ridge structures (diagonal distributions for $V_R$ in the $R$, $V_ϕ$ plane) in the range of Galactocentric distance $R$ = 8 to 15 kpc. We detect six ridge structures, including five ridges apparent in the radial velocity distribution and three ridges apparent in the vertical velocity, the sensitive time of which to the perturbations are from young population (0$-$3 Gyr) to old population (9$-$14 Gyr). Based on an analysis of the evolution of angular momentum distribution, we find that four ridges are relatively stationary, while another is evolving with time, which is confirmed by the difference analysis at different populations and supporting that there might be two kinds of dynamical origins. Furthermore, ridge features are also vividly present in the chemical properties ([Fe/H], [$α$/Fe]). The comparison between the north and south hemispheres of the Galaxy does show some differences and the ridge features are asymmetrical. Moreover, we find that diagonal ridge structures may affect the shape of the rotation curve, which is manifested as fluctuations and undulations on top of a smooth profile. Finally we speculate that the bar dynamics should be not enough to explain all ridge properties including the break feature in the $V_Z$-$L_Z$ plane.

astro-ph.GA↗

arXiv2022

Evidence for Populations-dependent vertical motions and the Long-lived Non-Steady Lopsided Milky Way Warp

We present the Galactic disk vertical velocity analysis using OB type stars (OB), Red Clump stars (RC), and Main-Sequence-Turn-Off stars (MSTO) with different average age populations crossed matched with LAMOST DR5 and Gaia DR3. We reveal the vertical velocities of the three populations varies clearly with the Galactocentric distance ($R$) and the younger stellar population has stronger increasing trend in general. The bending and breathing modes indicated by the vertical motions are dependent on the populations and they are varying with spatial locations. These vertical motions may be due to the Galactic warp, or minor mergers, or non-equilibrium of the disk. Assuming the warp is the dominant component, we find that the warp amplitude ($γ$, $Z_ω$) for OB (younger population) is larger than that for RC (medium population) and the later one is also larger than that for MSTO (older population), which is in agreement with other independent analyses of stellar density distribution, and supports the warp is long-lived, non-steady structure and has time evolution. This conclusion is robust whether or not the line-of-nodes $ϕ_w$ is fixed or as a free parameter (with $ϕ_w$ is around 3$-$8.5$^{\circ}$ as best fit). Furthermore, we find that warp is lopsided with asymmetries along azimuthal angle ($ϕ$).

astro-ph.GA↗

arXiv2022

Mass and Age determination of the LAMOST data with different Machine Learning methods

We present a catalog of 948,216 stars with mass label and a catalog of 163,105 red clump (RC) stars with mass and age labels simultaneously. The training dataset is cross matched from the LAMOST (The Large Sky Area Multi-Object Fiber Spectroscopic Telescope) DR5 and high resolution asteroseismology data, mass and age are predicted by random forest method or convex hull algorithm. The stellar parameters with high correlation with mass and age are extracted and the test dataset shows that the median relative error of the prediction model for the mass of large sample is 3\% and meanwhile, the mass and age of red clump stars are 4\% and 7\%. We also compare the predicted age of red clump stars with the recent works and find that the final uncertainty of the RC sample could reach 18\% for age and 9\% for mass, in the meantime, final precision of the mass for large sample with different type of stars could reach 13\% without considering systematics, all these are implying that this method could be widely used in the future. Moreover, we explore the performance of different machine learning methods for our sample, including bayesian linear regression (BYS), gradient boosting decision Tree (GBDT), multilayer perceptron (MLP), multiple linear regression (MLR), random forest (RF) and support vector regression (SVR). Finally we find that the performance of nonlinear model is generally better than that of linear model, and the GBDT and RF methods are relatively better.

astro-ph.GA↗