Hao Tian

Precise astrometric data from Gaia DR3 enable the identification of substructures in the Galactic halo. By slicing the proper motions of stars in the northern Galactic hemisphere, we identify members of one of the most diffuse known substructures, the Virgo Overdensity (VOD). RR Lyrae stars in our sample place the substructure at heliocentric distances of 15–30 kpc. Cross-matching with LAMOST spectroscopy yields 72 stars that exhibit highly eccentric orbits concentrated near their apocenters, occupying a compact locus in the Lz-E plane consistent with the Gaia-Sausage-Enceladus system. Their chemical abundances show a bimodal distribution. These results suggest that the VOD traces shell-like debris associated with the GSE merger.

We report the discovery of long tidal tails associated with the globular cluster (GC) NGC 5024. A modified matched filter applied to Gaia DR3 data reveals a broad stellar stream spanning α ≈ 230°–175°. The stellar stream overlaps on the sky with the LMS-1 and with the simulated stream of the GC NGC 5053, and all three share similar proper motions and metallicity. Our member candidates may therefore be a mixture of stars from NGC 5024, NGC 5053, and LMS-1. Nevertheless, the trailing tail extends roughly 20° beyond the known LMS-1 stream. Furthermore, the radial velocity (RV) as a function of α is used to distinguish the genuine stream candidates of NGC 5024 from the streams of NGC 5053 and LMS-1. Among the sources in common with DESI DR1, we found two distinct sequences in the RV– α plane, corresponding to the stellar streams of NGC 5024 and NGC 5053 (or LMS-1), respectively. This constitutes the first strong evidence for the existence of extensive tidal tails around NGC 5024.

We use a crossmatched sample of 3.7 million stars from Gaia Data Release 3 and LAMOST Data Release 7 to investigate the velocity substructures in the Milky Way disk. The median radial velocity V _R as a function of guiding-center radius R _g exhibits alternating positive and negative stripes, which are strongly correlated with known moving groups. By examining the V _R distribution in the Z – V _Z phase space, we find that the D1, P2, D2, and P3 V _R stripes display clear two-armed spirals. Among the moving groups embedded in these V _R stripes, the Coma Berenices moving group in the P3 stripe exhibits the most pronounced two-armed spiral and serves as the primary contributor to the left arm of the overall P3 spiral. The angular momentum, eccentricities, orbital frequencies, and frequency ratios of its stars are consistent with either the corotation resonance of the spiral arms or the m = 4 inner Lindblad resonance of the bar. Test-particle simulations confirm that a bar with a varying pattern speed, together with static or transient spiral arms, can produce such two-armed spirals.

Aims . We constrain the dynamical perturbations of the spiral arms in the Milky Way disk, based on the non-axisymmetric streaming motions of RGB stars revealed by APOGEE and Gaia . Methods . We developed a revised steady-state radial-velocity response model that incorporates both the V _{R ,sin} and the dynamically important V _{R ,cos} components for a two-armed logarithmic spiral potential. The model was validated using orbit integrations with AGAMA and Bayesian parameter recovery with dynesty, and was applied to the smoothed two-dimensional radial-velocity field of RGB stars while accounting for Lindblad and corotation resonances. Results . The revised model reproduces the phase and amplitude of the mock radial-velocity field to the ∼2% level, substantially improving upon earlier V _{R ,sin} -only formulations. Applied to the observational data, it yields a robust pitch angle of p ≃ 10° and a local surface density contrast of ξ ≃ 5-18% at the solar radius. The radial scale length is less well constrained ( h _{R ,1} ≃ 40-50 kpc) due to intrinsic parameter covariance. Resonance effects strongly shape the velocity field, affecting the fitting: the radial velocity becomes extremely large near the Lindblad resonances, whereas it vanishes close to the corotation resonance. Conclusions . Our results demonstrate that including both the V _{R ,sin} and V _{R ,cos} terms is essential for a physically consistent interpretation of stellar streaming motions induced by a spiral potential. The observed kinematics constrain the spiral pattern speed to Ω _p ≈ 10-20 km s ⁻¹ kpc ⁻¹ .

The phenomenon of multiple stellar populations (MPs), characterised by star-to-star variations in light-element abundances, is a ubiquitous feature of globular clusters (GCs). While spectroscopic surveys have directly revealed these abundance anomalies, photometric studies, particularly with the Hubble Space Telescope (HST), have been instrumental in characterizing MP sequences across the colour-magnitude diagrams (CMDs). However, the narrow field of view of the HST has restricted these studies to only a small portion of the star clusters, leaving the vast majority of the clusters’ space unexplored. The upcoming Chinese Space Station Survey Telescope (CSST), with its wide field of view and both UV and optical capabilities, will provide new opportunities for systematic MP studies. We aim to quantify the capability of the CSST wide-field camera in detecting and characterizing MPs in GCs through virtual simulations that closely mimic real observations. We performed comprehensive simulations using synthetic stellar population models incorporating different helium abundances (Δ Y ) and carbon-nitrogen-oxygen (CNO) variations. We simulated CSST observations for GCs at distances of about 9.6 kpc and 20 kpc with different exposure times. We evaluated the detection efficiency of MPs based on the CMDs constructed from the seven different bands of the CSST, as well as the pseudo color–color diagrams (chromosome diagrams) designed with the UV-optical combination. For a GC at a distance of approximately 9.6 kpc, the NUV − u color index of the CSST is highly sensitive to stellar populations with different Δ Y and CNO abundances in the CMD, providing color separations of Δ( NUV − u ) ≈ 0.16 (for red giants) to 0.44 mag (for dwarfs). We find that when the total exposure time in the UV band exceeds ∼1000 s and the exposure time in the optical band exceeds ∼300 s, the main survey camera of CSST is sufficient to resolve these MPs. Furthermore, extending the simulations to 20 kpc, a distance encompassing ∼80% of Galactic GCs, we demonstrate that the SCam retains significant diagnostic power, resolving stellar populations with Δ Y ≥ 0.06 dex and δ [N/Fe] ≥ 0.64 dex, and distinguishing MPs in the CMD down to i ∼ 19.5 mag for clusters with substantial chemical dispersions. The combination of NUV – u – g filters provides diagnostic capabilities comparable to HST’s F275W–F336W–F438W system. CSST will be a powerful facility for MP studies, capable of efficiently surveying the entire spatial extent of several hundred star clusters in the local group. Its wide field of view and multi-band capabilities will enable the first homogeneous MP census spanning the entire Milky Way and its neighbouring galaxies, significantly advancing our understanding of star clusters’ formation and chemical evolution.

The Chinese Space Station Survey Telescope (CSST) aims to map the universe across an unprecedented dynamic range of stellar densities, spanning from extragalactic voids to the crowded Galactic center (e.g. a few stars and galaxies in the voids and >10 ⁵ stars per detector in Galactic center). However, processing such heterogeneous data with a general source extraction pipeline introduces significant systematic uncertainties, standard algorithms exhibit poor accuracy in crowded fields and suffer from increased astrometric uncertainty in void regions. To mitigate these systematics, we propose a hierarchical, two-stage deep learning model for adaptive data reduction. The first stage (“classification”) employs a ResNet-34 model to classify images into six discrete density categories, achieving 98.83% in global accuracy. This classification acts as a critical decision gate, ensuring high calibration accuracy in the crowded fields. In the second stage (“regression”), a ResNet-50 regression model predicts the bright stars (<23.5 mag) in the field, which is essential for astrometric calibration, achieving a mean absolute error of 0.0824 dex. By decoupling density characterization from source extraction, our model ensures that photometric and astrometric algorithms are optimally matched to the stellar density environment, thereby enhancing the fidelity and homogeneity of CSST as well as future large sky survey data products.

Improving the accuracy of photometric redshifts (photo- z ) is essential for reliable statistical studies of cosmology and galaxy evolution. However, missing photometric bands are a common observational challenge that can significantly degrade photo- z estimation accuracy. In this work, we present a systematic evaluation of data imputation methods aimed at improving photo- z performance. We benchmark a range of representative machine learning and deep learning architectures, identifying k-nearest neighbors (KNN) and the attentionbased SAITS model as the leading performers. These models are then applied to China Space Station Survey Telescope mock data to assess their performance under realistic observational conditions. Our results show that KNN yields the highest accuracy under idealized missing completely at random (MCAR) conditions with complete training sets, whereas robustness tests reveal that SAITS significantly outperforms KNN when training data are incomplete or when applied to realistic mixed-mechanism scenarios. We find that domain consistency between training and testing missingness patterns is a prerequisite for optimal performance, highlighting the risks of domain shift in supervised regression tasks. Furthermore, our analysis demonstrates that while general imputation models are highly effective for MCAR and missing at random data, they are detrimental when applied to missing not at random data arising from flux limits, as statistical models fail to capture the physical information inherent in these nondetection. Consequently, we advocate for more sophisticated architectures capable of disentangling stochastic missingness from physical nondetection to address these distinct mechanisms individually.

Observations show that multiple stellar populations (MPs) are ubiquitous in globular clusters. The Hubble Space Telescope (HST) has been a pivotal tool for previous photometric studies of MPs. The Chinese Space Station Survey Telescope (CSST) is a 2 m telescope scheduled for launch. One of its imaging instruments, the Survey Camera (SC), combines ultraviolet sensitivity comparable to that of HST with a significantly larger field of view, making it well-suited for conducting large-scale photometric surveys of MPs within extensive stellar stream structures. In this work, we perform mock observations of the stellar stream Palomar 5 to assess the feasibility of detecting MPs with the CSST/SC. The results indicate that the CSST/SC cannot resolve MPs in stellar streams at distances comparable to Palomar 5 (≳20 kpc) with one or 10 150 s exposures. This fundamental limitation arises from the absence of the precise proper motions required to disentangle stream members. We estimate that successful resolution would require the target stream to be ≲8 kpc under a 150 s exposure. Furthermore, using theoretical color–magnitude diagrams, we find that the CSST/SC g band provides an optimal balance between contamination rate and completeness rate for member identification in the cluster’s core. However, this approach fails in the stream due to severe field star contamination. Therefore, future CSST observations of Palomar 5 and its tidal tails will employ multiple epochs across several bands to obtain the deep photometry and proper motion data for a definitive MP analysis.

Tidal structures around globular clusters provide valuable insights into cluster evolution and the hierarchical assembly of the Milky Way. Using wide-field imaging data from the DESI Legacy Survey combined with a color-magnitude matched-filter technique, we performed a systematic analysis of extratidal features in 28 Galactic globular clusters of likely extragalactic origin, representing the largest homogeneous sample examined in this context to date. The clusters display diverse morphologies: 12 exhibit tidal tails, nine show diffuse envelopes, and seven reveal no clear extratidal features. Notably, we report the first detection of an extended tidal structure around the Sagittarius-associated cluster Terzan 7. To explore the underlying drivers, we compared intrinsic properties, orbital dynamics, and possible accretion associations across morphological groups. From the parameter distributions, complemented by Kolmogorov-Smirnov tests, we find that total mass, escape velocity, concentration, tidal filling factor, pericentric radius, eccentricity, and radial angle in action-angle coordinates are all likely group-sensitive parameters. These results suggest that both internal cluster properties and orbital configurations play important roles in shaping extratidal morphologies. In addition, the cluster’s accretion history shows no clear correlation with the presence of tidal features, indicating that it is not a direct driver of outer structure formation. Overall, the diversity of tidal structures is unlikely to be governed by a single factor. Instead, it reflects the interplay between internal dynamical evolution and the external Galactic environment. This study provides the most comprehensive constraints so far on the physical processes driving extratidal structures in accreted globular clusters.

The Chinese Space Station Survey Telescope (CSST) is a flagship space mission, supported by the China Manned Space Project, designed to carry out a large-area sky survey to explore the nature of dark matter and dark energy in the Universe. The onboard multi-band imaging and slitless spectroscopic modules will enable us to obtain photometric data for billions of galaxies and stars, as well as hundreds of millions of spectroscopic measurements, advancing various scientific analyses such as galaxy clustering and weak gravitational lensing. To support the image simulations for the main survey of the CSST mission, we present a mock catalog of stars and galaxies. For stars, the mock catalog is generated using either Galaxia or TRILEGAL , both of which provide a range of stellar properties to meet the requirements of CSST image simulations. For galaxies, we built a mock light-cone up to redshift z ∼ 3.5 from the cosmological N -body simulation and populated the mock galaxy catalog from the dark matter haloes using a semi-analytical galaxy formation model. We then performed a full-sky ray-tracing simulation of weak gravitational lensing to obtain lensing shear at the position of each galaxy in the light-cone. To support both multi-band imaging and slitless spectroscopic simulations, we computed the spectral energy distribution (SED) for each galaxy based on its star formation history using a supervised deep learning model and determined the magnitudes in each band using the CSST throughputs. Finally, the properties of our mock galaxies include positions, redshifts, stellar masses, shapes, sizes, SEDs, lensing shears and magnifications. We have validated our mock catalog against observational data and theoretical models, with results showing good overall agreement. The catalog provides a flexible data set for the development of CSST image processing and can support a wide range of cosmological analyses within the CSST mission.