arXiv subjects

Chengqun Yang

Publications and source records attributed to Chengqun Yang.

At least 19 recordsLinked to original sources

arXiv2026

Spatiotemporally Decoupled Autoregressive Diffusion Model for Human Motion Generation

Text-driven human motion synthesis has made substantial development with two core modules of motion representation and generative architecture. For representation, Vector Quantization (VQ)-based methods compress motion data into discrete tokens while latent-based models operate directly in continuous space. However, both of these representations exhibit significant limitations. VQ-based methods suffer from inherent information loss, which compromises the quality, diversity, and generalization of generated motions, while continuous representation on holistic whole-body motion hinders part-level flexibility. For architecture, diffusion and autoregressive diffusion models have demonstrated their superiority, yet the fine-grained controllability over individual body parts is also limited. Thus, we propose a unified spatiotemporally decoupled framework named DeMoDiff, which jointly redesigns representation and architecture. To enhance representation extraction capabilities and offer greater part-level controllability, we present a spatial-temporal VAE that encodes each body joint rather than compressing the whole-body motion into a single latent space. Then, we incorporate spatial-temporal masking and attention mechanisms into an autoregressive diffusion generator, achieving both generative capability and controllable editability. Extensive experiments on the HumanML3D and KIT-ML datasets demonstrate that our model achieves state-of-the-art reconstruction performance and compelling motion generation results. Moreover, our framework demonstrates strong temporal and spatial editing capabilities, further validating its effectiveness. Our project page: https://rex0191.github.io/DeMoDiff/

cs.CV↗

arXiv2026

Inter-X++: A Comprehensive Benchmark for Multimodal Human-Human Interaction Analysis

The capability to perceive and synthesize human-human interactions is fundamental to developing intelligent digital human systems. However, existing datasets and modeling approaches are fundamentally constrained by low-fidelity kinematics, the omission of dexterous hand gestures and a severe lack of rich multimodal annotations. Furthermore, fragmented interaction representations and inconsistent evaluation protocols also impede fair and rigorous benchmarking. To systematically address these bottlenecks, we present Inter-X++, a comprehensive and large-scale benchmark designed to empower versatile HHI analysis. Captured via a novel hybrid motion capture system, Inter-X++ provides 11,388 high-fidelity interaction sequences and over 8.1M frames, featuring precise whole-body movements and detailed finger articulations. Meanwhile, we enrich the data foundation with multifaceted annotations, including hierarchical fine-grained textual descriptions, interaction categories, causal interaction orders, the relationship and personality of the subjects, as well as vertex-level contact maps and physically regularized constraints. Leveraging these elaborate annotations, we formulate a unified testing ground comprising four categories of downstream tasks that symmetrically span both generative and perceptive paradigms. To eliminate benchmarking ambiguities, we systematically standardize the interaction representations and evaluation protocols. Finally, we go beyond dataset construction to propose OpenHHI, a single and unified HHI representation and modeling framework that jointly optimizes interaction reconstruction and semantic understanding. Extensive experiments reveal that OpenHHI achieves state-of-the-art performance on both generation and perception tasks. This definitively proves that our unified representation successfully bridges interaction understanding and generation simultaneously.

cs.CV↗

arXiv2026

MRBench: A Comprehensive Benchmark for Human Motion-Text Retrieval

Human motion-text retrieval provides a rigorous means of assessing cross-modal alignment. Prevailing benchmarks are dominated by homogeneous indoor motions, imbalanced motion distributions, and oversimplified, repetitive texts, which hinder the reliable measurement of cross-domain and cross-granularity alignment. We thus introduce MRBench, a comprehensive motion-text retrieval benchmark featuring heterogeneous motions, broad and balanced category coverage, and reliable, discriminative, multi-granular descriptions. MRBench is constructed through a meticulously designed multi-stage data curation pipeline, which filters and balances candidates, verifies unambiguous semantic alignment, and generates motion-grounded descriptions at multiple granularities. The resulting benchmark contains 3,390 motions drawn from motion capture, in-the-wild videos, synthetic videos, and motion generative models, covering 118 fine-grained categories. Each motion is paired with concise, standard, and fine-grained descriptions, yielding 10,170 captions. Extensive evaluations of representative retrieval baselines on MRBench reveal a substantial cross-dataset generalization gap and pronounced sensitivity to query granularity. We propose a lightweight granularity-aware model anchored at a frozen standard-caption-aligned retrieval model. LLM-based concise and fine-grained captions provide pseudo-supervision for extra-branch granularity-specific motion extractors and text adapters. For inference, granularity-aware score fusion integrates global and adapted similarities while strictly maintaining score comparability across all description levels. The resulting model improves mixed-granularity retrieval without compromising standard-caption performance. We believe that our MRBench provides a comprehensive testbed for advancing motion-language alignment evaluation.

cs.CV↗

arXiv2026

Enfold: Folding World Model Imagination into Predictive Representations for Ultra-Efficient Embodied Control

World generative models are typically used through what they produce: a rendered future, a video-conditioned action, or latent context computed by a costly generative branch. We argue that their more reusable asset is the computation that constructs a future. As a generator transforms a corrupted future into a coherent trajectory, its intermediate states organize appearance, spatial layout, and interaction across levels of abstraction. Can this future-generative computation be internalized in a representation inferred from the present alone? We present Enfold, which transfers this computation into a representation predicted from the current visual context and language instruction. During training, multi-level states exposed as the generator processes the observed future supervise a current-only encoder. The learned representation is fed back to condition future generation and is read by task heads without allowing task gradients to reshape the encoder. At deployment, action prediction no longer executes the generator. Across LIBERO, RoboTwin2.0, and real-robot tasks, Enfold supports strong control while reducing action latency by $3.7\times$ relative to Fast--WAM, Enfold-Flash reaches $10.1\times$. Representation analyses show that it suppresses nuisance variation and preferentially captures changes that emerge over longer horizons. When the current scene is altered by human intervention, both the generated continuation and the executed actions adapt, which is inconsistent with fixed trajectory replay. These results recast a world generator as a source of predictive control representations: its future need not be materialized at every step if its internal structure can be enfolded into the present.

cs.RO↗

arXiv2026

The multiple corrugations in the Galactic disk derived from the LAMOST and Gaia survey data

Large spectroscopic and astrometric surveys have revealed complex wave-like features in the Milky Way disk, suggesting that its kinematic and chemical structures are shaped by time-dependent perturbations. Recent studies have reported oscillatory patterns in the Rg-Vphi-VR space, hinting at a possible structural transition in the outer disk. We aim to characterise the transition between the inner and outer Galactic thin disk and to investigate whether radial corrugations can provide a plausible physical interpretation of the observed features. We analysed two large stellar samples from LAMOST DR8 and Gaia DR3, combining spatial, kinematic, and chemical diagnostics. A simplified corrugation model consisting of two radial waves propagating in opposite directions was constructed and fitted to the observed VR pattern. We further validated the model using N-body simulations. Both LAMOST and Gaia samples reproduce the previously reported wave-like pattern in the Rg-Vphi-VR plane. We identify a clear transition between the inner and outer disks via the variations in rotational velocity and metallicities. The corrugation model naturally reproduces the periodic variation of VR with galactocentric radius, and the superposition of the inward and outward propagating modes gives rise to a comparable oscillatory pattern in both observations and simulations. Our modelling suggests that radial corrugations can provide a plausible interpretation of the observed kinematic signatures. The results highlight the complex, multi-perturber nature of the Galactic disk and motivate further investigation with upcoming surveys.

astro-ph.GA↗

arXiv2025

POLAR: A Portrait OLAT Dataset and Generative Framework for Illumination-Aware Face Modeling

Face relighting aims to synthesize realistic portraits under novel illumination while preserving identity and geometry. However, progress remains constrained by the limited availability of large-scale, physically consistent illumination data. To address this, we introduce POLAR, a large-scale and physically calibrated One-Light-at-a-Time (OLAT) dataset containing over 200 subjects captured under 156 lighting directions, multiple views, and diverse expressions. Building upon POLAR, we develop a flow-based generative model POLARNet that predicts per-light OLAT responses from a single portrait, capturing fine-grained and direction-aware illumination effects while preserving facial identity. Unlike diffusion or background-conditioned methods that rely on statistical or contextual cues, our formulation models illumination as a continuous, physically interpretable transformation between lighting states, enabling scalable and controllable relighting. Together, POLAR and POLARNet form a unified illumination learning framework that links real data, generative synthesis, and physically grounded relighting, establishing a self-sustaining "chicken-and-egg" cycle for scalable and reproducible portrait illumination. Our project page: https://rex0191.github.io/POLAR/.

cs.CV↗

arXiv2025

A Vertically Orientated Dark Matter Halo Marks a Flip of the Galactic Disk

Unveiling the 3D shape of the Milky Way's dark-matter halo is critical to understanding its formation history. We created an innovative dynamical model with minimal assumptions on the internal dynamical structures and accommodates a highly flexible triaxial DM halo. By applying the method to 6D phase-space data of K-giant stars from LAMOST + Gaia, we robustly determine the 3D dark-matter distribution of the Milky Way out to approximately $50$ kpc. We discover a triaxial, nearly oblate dark-matter halo with $q_{\rm DM} = Z/X= 0.92\pm0.08$, $p_{\rm DM} = Y/X= 0.8\pm0.2$ averagely within 50 kpc, where $Z$ axis is defined perpendicular to the stellar disk. The axes ratio $q_{\rm DM} > p_{\rm DM}$ is strongly preferred; the long-intermediate axis plane of the dark-matter halo is unexpectedly vertical to the Galactic disk, yet aligned with the `plane of satellites'. This striking configuration suggests that the Galactic disk (and the inner halo) has flipped, likely torqued by minor mergers, from an original alignment with the outer dark-matter halo and satellite plane, as supported by Milky Way analogues from Auriga and TNG50. By allowing $q_{\rm DM}(r)$ and $p_{\rm DM}(r)$ vary with radii, we find tentative evidence that the dark-matter halo is twisted, that it agrees alignment with the disk in the inner regions and transitions to a vertical orientation at $r\gtrsim 20$ kpc, supporting the disk flip scenario prediction. Such disk reorientation is non-trivial yet its physical mechanism is straightforward to comprehend and naturally originates a vertical satellite plane. Our findings offer a unified framework that links dark-matter halo orientation, satellite alignment, and disk evolution, reinforcing the internal consistency of the Milky Way in $\Lambda$CDM model.

astro-ph.GA↗

arXiv2025

Perceiving and Acting in First-Person: A Dataset and Benchmark for Egocentric Human-Object-Human Interactions

Learning action models from real-world human-centric interaction datasets is important towards building general-purpose intelligent assistants with efficiency. However, most existing datasets only offer specialist interaction category and ignore that AI assistants perceive and act based on first-person acquisition. We urge that both the generalist interaction knowledge and egocentric modality are indispensable. In this paper, we embed the manual-assisted task into a vision-language-action framework, where the assistant provides services to the instructor following egocentric vision and commands. With our hybrid RGB-MoCap system, pairs of assistants and instructors engage with multiple objects and the scene following GPT-generated scripts. Under this setting, we accomplish InterVLA, the first large-scale human-object-human interaction dataset with 11.4 hours and 1.2M frames of multimodal data, spanning 2 egocentric and 5 exocentric videos, accurate human/object motions and verbal commands. Furthermore, we establish novel benchmarks on egocentric human motion estimation, interaction synthesis, and interaction prediction with comprehensive analysis. We believe that our InterVLA testbed and the benchmarks will foster future works on building AI agents in the physical world.

cs.CV↗

arXiv2025

Uncover 3D Dark Matter Distribution of the Milky Way by an Empirical Triaxial Orbit-Superposition Model: Method Validation

We introduce a novel dynamical model, named empirical triaxial orbit-superposition model, for the Milky Way halo. This model relies on minimal physical assumptions that the system is stationary, meaning the distribution function in 6D phase-space does not change when the stars orbiting in the correct gravitational potential. We validate our method by applying it to mock datasets that mimic the observations of the Milky Way halo from LAMOST + Gaia with stars' 3D position and 3D velocity observed. By removing the stellar disk and substructures, correcting the selection function, we obtain a sample of smooth halo stars considered as stationary and complete. We construct a gravitational potential including a highly flexible triaxial dark matter halo with adaptable parameters. Within each specified gravitational potential, we integrate orbits of these halo stars, and build a model by superposing the orbits together taking the weights of stars derived from the selection function correction. The goodness of the models are evaluated by comparing the density distributions as well as 3D velocity distributions numerically represented in the model to that in the data. The shape and radial density distribution of the underlying dark matter halo can be constrained well simultaneously. We apply it to three mock galaxies with different intrinsic shapes of their dark matter halos and achieved accurate recovery of the 3D dark matter density distributions for all.

astro-ph.GA↗

arXiv2025

The Shape and Mass of the Galactic Dark Matter Halo from the Axisymmetric Jeans Model

We explore the density profile, shape, and virial mass of the Milky Way's dark matter halo using K giants (KG) from LAMOST and SDSS/SEGUE, as well as blue horizontal branch (BHB) stars from SDSS. Incorporating Gaia DR3 proper motions, we first investigate the velocity ellipsoid distribution within the $(R, |z|)$ space. The ellipsoids projected onto the $(v_R, v_z)$ plane exhibit near-spherical alignment. We then probe the underlying dark matter distribution using the axisymmetric Jeans equations with multi-Gaussian expansion (MGE) and the spherically aligned Jeans anisotropic modelling (JAM${\rm sph}$), allowing for different flattened dark matter density models. For each model, we apply two fitting approaches: fitting the KGs and BHBs separately or fit them simultaneously as two dynamical tracers in one gravitational potential. We find consistent results on the dark matter density profiles, $r_{200}$, and $M_{200}$ within a 1-$\sigma$ confidence region for models constrained by KGs, BHBs, and both. We find the strongest consistency between KGs and BHBs in constraining dark matter profiles for models incorporating radially varying halo flattening ($q(r_{\rm gc})$), which suggests the Milky Way's dark matter halo shape evolves with Galactocentric distance ($r_{\rm gc}$). Specifically, the halo flattening parameter $q_h$ decreases within $r_{\rm gc} < 20$ kpc and increases for $r_{\rm gc} > 20$ kpc. In this model, $M_{\rm tot} (< 60~{\rm kpc}) = 0.533^{+0.061}_{-0.054} \times 10^{12}$ $M_{\odot}$, $r_{200}$ is $188\pm15$ kpc, with $M_{200}$ estimated at $0.820^{+0.210}_{-0.186} \times 10^{12} M_{\odot}$.

astro-ph.GA↗

arXiv2024

Identifying the Galactic Substructures in 5D Space Using All-sky RR Lyrae Stars in Gaia DR3

Motivated by the vast gap between photometric and spectroscopic data volumes, there is great potential in using 5D kinematic information to identify and study substructures of the Milky Way. We identify substructures in the Galactic halo using 46,575 RR Lyrae stars (RRLs) from Gaia DR3 with the photometric metallicities and distances newly estimated by Li et al. (2023). Assuming a Gaussian prior distribution of radial velocity, we calculate the orbital distribution characterized by the integrals of motion for each RRL based on its 3D positions, proper motions and corresponding errors, and then apply the friends-of-friends algorithm to identify groups moving along similar orbits. We have identified several known substructures, including Sagittarius (Sgr) Stream, Hercules-Aquila Cloud (HAC), Virgo Overdensity (VOD), Gaia-Enceladus-Sausage (GES), Orphan-Chenab stream, Cetus-Palca, Helmi Streams, Sequoia, Wukong and Large Magellanic Cloud (LMC) leading arm, along with 18 unknown groups. Our findings indicate that HAC and VOD have kinematic and chemical properties remarkably similar to GES, with most HAC and VOD members exhibiting eccentricity as high as GES, suggesting that they may share a common origin with GES. The ability to identify the low mass and spatially dispersed substructures further demonstrates the potential of our method, which breaks the limit of spectroscopic survey and is competent to probe the substructures in the whole Galaxy. Finally, we have also identified 18 unknown groups with good spatial clustering and proper motion consistency, suggesting more excavation of Milky Way substructures in the future with only 5D data.

astro-ph.GA↗

arXiv2024

Investigating the vertical distribution of the disk as a function of radial action: Results from simulations

Previous research has established a relationship between radial action and scale height in Galactic disks, unveiling a correlation between radial and vertical heating. This finding poses a challenge to our existing comprehension of heating theories and consequently encodes crucial insights into the formation and heating history of Galactic disks. In this study, we perform N-body simulations with the aim of verifying the existence of this correlation between radial action and scale height, thereby enhancing our comprehension of the heating history of Galactic disks. We find that the relationship between radial action and scale height in our simulations can be described by the same functional form observed in previous work. Furthermore, the relationships derived from our simulations align well with those of the Galactic thin disk. However, they do not coincide with the inner thick disk but exhibit a rough correspondence with the outer thick disk, suggesting the possibility that additional heating mechanisms may be required to explain the inner thick disk. We also find that the mean radial action and scale height undergo rapid increases during the initial stages of the simulation, yet remain relatively unchanged as the disk evolves further. By tracing example particles, we uncover a correlation between radial and vertical heating in our simulation: as a particle in the disk gains or loses radial action, its vertical motion tends to oscillate on a more or less extended orbit, accompanied by a tendency to migrate outward or inward, respectively. The massive, long-lasting particles in our simulation contribute to disk heating by solely enhancing the rate of increase in scale height with radial action, while maintaining the functional form that describes the relationship between these two variables.

astro-ph.GA↗

arXiv2023

Two long-period giant planets around two giant stars: HD 112570 and HD 154391

We present the discoveries of two giant planets orbiting the red giant branch (RGB) star HD 112570 and the red clump (RC) star HD 154391, based on the radial velocity (RV) measurements from Xinglong station and Okayama Astrophysical Observatory (OAO). Spectroscopic and asteroseismic analyses suggest that HD 112570 has a mass of $1.15\pm0.12\,M_{\odot}$, a radius of $9.85\pm0.23\,R_{\odot}$, a metallicity [Fe/H] of $-0.46\pm0.1$ and a ${\rm log}\,g$ of $2.47\pm0.1$. With the joint analysis of RV and Hipparcos-Gaia astrometry, we obtain a dynamical mass of $M_{\rm p}={3.42}_{-0.84}^{+1.4}\ M_{\rm Jup}$, a period of $P={2615}_{-77}^{+85}$ days and a moderate eccentricity of $e={0.20}_{-0.14}^{+0.16}$ for the Jovian planet HD 112570 b. For HD 154391, it has a mass of $2.07\pm0.03\,M_{\odot}$, a radius of $8.56\pm0.05\,R_{\odot}$, a metallicity [Fe/H] of $0.07\pm0.1$ and a ${\rm log}\,g$ of $2.86\pm0.1$. The super-Jupiter HD 154391 b has a mass of $M_{\rm p}={9.1}_{-1.9}^{+2.8}\ M_{\rm Jup}$, a period of $P={5163}_{-57}^{+60}$ days and an eccentricity of $e={0.20}_{-0.04}^{+0.04}$. We found HD 154391 b has one of the longest orbital period among those ever discovered orbiting evolved stars, which may provide a valuable case in our understanding of planetary formation at wider orbits. Moreover, while a mass gap at $4\,M_{\rm Jup}$ seems to be present in the population of giant stars, there appears to be no significant differences in the distribution of metallicity among giant planets with masses above or below this threshold. Finally, The origin of the abnormal accumulation near 2 au for planets around large evolved stars ($R_{\star}>21\,R_{\odot}$), remains unclear.

astro-ph.EP↗

arXiv2023

Detection of multiple phase space overdensities of GSE stars by orbit integration

In N-body simulations, nearly radial mergers can form shell-like overdensities in the sky position and phase space ($r-v_r$) due to the combination of dynamical friction and tidal stripping. The merger event of Gaia-Sausage-Enceladus has provided a unique opportunity to study the shells in the phase space. To search for them, we integrate the orbits of 5949 GSE-related halo K giants from the LAMOST survey and record their positions at all time intervals in $r-v_r$ diagram. After the subtraction of a smoothed background, we find six significant and complete thin chevron-like overdensities. The apocenters $r_\mathrm{apo}$ of stars in the six chevrons are around 6.75, 12.75, 18.75, 25.25, 27.25, and 30.25 kpc. These chevrons reveal the multiple pile-ups of GSE stars at different apocenters. The application of a different Milky Way mass $M_\mathrm{vir}$ will change the opening angles of these chevrons, while leave their apocenters almost unchanged. By comparing with a recent study of the phase space overdensities of local halo stars from Gaia RVS survey, our results are more inclined to a medium $M_\mathrm{vir}$ of $10^{12}\,M_\odot$. The application of a non-axisymmetric Galactic potential with a steadily rotating bar has a blurring effect on the appearance of these chevron-like overdensities, especially for the chevrons with $r_\mathrm{apo} > 20$ kpc.

astro-ph.GA↗

arXiv2023

A Catalog of Distance Determinations for the LAMOST DR8 K Giants in the Galactic Halo

We present a catalog of distances for 19544 K giants drawn from LAMOST DR8. Most of them are located in the halo of the Milky Way up to ~120~kpc. There are 15% K giants without SDSS photometry, for which we supplements with Pan-STARRS1 (PS1) photometry calibrated to SDSS photometric system. The possible contamination of the red clumps/horizontal branch are removed according to metallicities and colors before the distance determination. Combining the LAMOST spectroscopic metallicities with the SDSS/PS1 photometry, we estimate the absolute magnitudes in SDSS $r-$band, the distance moduli, and the corresponding uncertainties through an Bayesian approach devised by Xue et al. (2014) for the SEGUE halo K-giants. The typical distance precision is about 11%. The stars in the catalog lie in a region of 4-126 kpc from the Galactic center, of which with 6, 320 stars beyond 20 kpc and 273 stars beyond 50 kpc, forming the largest spectroscopic sample of distant tracers in the Milky Way halo so far.

astro-ph.GA↗

arXiv2022

Constructing the Milky Way Stellar Halo in the Galactic Center by Direct Orbit Integration

The halo stars on highly radial orbits should inevitably pass the center regions of the Milky Way. Under the assumption that the stellar halo is in dynamical equilibrium and axisymmetric, we integrate the orbits of $\sim 10,000$ halo K-giants at $5\leq r \leq 50$ kpc cross-matched from LAMOST DR5 and $Gaia$ DR3. By carefully considering the selection function, we construct the stellar halo distribution at the entire regions of $r \leq 50$ kpc. We find that a double-broken power-law function well describes the stellar halo density distribution with shallower slopes in the inner regions and the two breaks at $r=10$ kpc and $r=25$ kpc, respectively. The stellar halo becomes flatter from outer to inner regions but has $q\sim 0.5$ at $r \lesssim 5$ kpc. The stellar halo becomes isotropic with a slight prograde rotation in the inner 5 kpc, and reaches velocity dispersions of $\sim 250\rm \ km\ s^{-1}$. We get a weak negative metallicity gradient of $-0.005$ dex kpc$^{-1}$ at $5\leq r \leq 50$ kpc, while there is an excess of relative metal-rich stars with [Fe/H]$>-1$ in the inner 10 kpc. The halo interlopers at $r \leq 5$ kpc from integration of our sample has a mass of $\sim1.2 \times 10^8\ M_{\odot}$ ($\sim 4.7 \times 10^7\ M_{\odot}$ at [Fe/H]$<-1.5$), which can explain 50-100% of the metal-poor stars with [Fe/H]$<-1.5$ directly observed in the Galactic central regions.

astro-ph.GA↗

arXiv2022

Milky Way Mass with K Giants and BHB Stars Using LAMOST, SDSS/SEGUE, and Gaia: 3D Spherical Jeans Equation and Tracer Mass Estimator

We measure the enclosed Milky Way mass profile to Galactocentric distances of $\sim70$ and $\sim50$ kpc using the smooth, diffuse stellar halo samples of Bird et al. The samples are LAMOST and SDSS/SEGUE K giants (KG) and SDSS/SEGUE blue horizontal branch (BHB) stars with accurate metallicities. The 3D kinematics are available through LAMOST and SDSS/SEGUE distances and radial velocities and {\it Gaia} DR2 proper motions. Two methods are used to estimate the enclosed mass: 3D spherical Jeans equation and Evans et al. tracer mass estimator (TME). We remove substructure via the Xue et al. method based on integrals of motion. We evaluate the uncertainties on our estimates due to random sampling noise, systematic distance errors, the adopted density profile, and non-virialization and non-spherical effects of the halo. The tracer density profile remains a limiting systematic in our mass estimates, although within these limits we find reasonable agreement across the different samples and the methods applied. Out to $\sim70$ and $\sim50$ kpc, the Jeans method yields total enclosed masses of $4.3\pm0.95$ (random) $\pm0.6$ (systematic) $\times10^{11}$ M$_\odot$ and $4.1\pm1.2$ (random) $\pm0.6$ (systematic) $\times10^{11}$ M$_\odot$ for the KG and BHB stars, respectively. For the KG and BHB samples we find a dark matter virial mass of $M_{200}=0.55^{+0.15}_{-0.11}$ (random) $\pm0.083$ (systematic) $\times10^{12}$ M$_\odot$ and $M_{200}=1.00^{+0.67}_{-0.33}$ (random) $\pm0.15$ (systematic) $\times10^{12}$ M$_\odot$, respectively.

astro-ph.GA↗

arXiv2022

Influence of the Gaia-Sausage-Enceladus on the density shape of the Galactic stellar halo revealed by halo K giants from the LAMOST survey

We present a study of the influence of the Gaia-Sausage-Enceladus (GSE) on the density shape of the Galactic stellar halo using 11624 K giants from the LAMOST survey. Every star is assigned a probability of being a member of the GSE based on its spherical velocities and metallicity by a Gaussian Mixture Model. We divide the stellar halo into two parts by the obtained probabilities, of which one is composed of the GSE members and defined as the GSE-related halo, and the other one is referred to as the GSE-removed halo. Using a non-parametric method, the radial number density profiles of the two stellar halos can be well described by a single power law with a variable flattening $q$ ($r = \sqrt{R^2+[(Z/q(r))]^2}, ν= {ν_0}r^{-α}$). The index $α$ is $4.92\pm0.12$ for the GSE-related halo and $4.25\pm0.14$ for the GSE-removed halo. Both the two stellar halos are vertically flattened at smaller radii but become more spherical at larger radii. We find that the GSE-related halo is less vertically flattened than the GSE-removed halo, and the difference of $q$ between the two stellar halos ranges from 0.07 to 0.15. However, after the consideration of the bias, it is thought to be within 0.08 at most of the radii. Finally, we compare our results with two Milky Way analogues which experience a significant major merger in the TNG50 simulation. The study of the two analogues also shows that the major merger-related stellar halo has a smaller ellipticity than the major merger-removed stellar halo.

astro-ph.GA↗