Jing Li

In this work, we employ a multilayer perceptron to derive atmospheric parameters (effective temperature T _eff , surface gravity log g , and metallicity [M/H]) for a large sample of cool giants from LAMOST DR9 low-resolution spectra. For spectra with a median signal-to-noise ratio ∼ 105, the achieved internal precisions are 36 K, 0.22 dex, and 0.16 dex for T _eff , log g , and [M/H], respectively. By integrating these parameters with multiband photometry from the Two Micron All Sky Survey and the Wide-field Infrared Survey Explorer, we estimate stellar distances within a Bayesian framework incorporating 3D extinction maps. Our distance estimates exhibit a remarkably stable fractional internal uncertainty ( σ _D / D ) of ∼8.4% across the sampled parameter space. External validations against Gaia EDR3 Bayesian geometric distances show a median distance bias of only 0.1%, with a 16th–84th percentile dispersion of −21.8%–28.9%. Furthermore, comparisons with open cluster distances confirm that systematic deviations remain within 10% for stars with T _eff ∼ 3700–4100 K. Applying these high-precision distances and velocities, we characterize the spatial distribution and kinematics of the Galactic disk. Our results demonstrate that cool giants are powerful tracers for the outer disk, effectively revealing its complex asymmetric structures and kinematic substructures.

Spectroscopic surveys have identified significant numbers of metal-poor nitrogen-rich (N-rich) field stars. These stars are strong candidates for escapees from globular clusters (GCs), as their distinctive nitrogen enhancement mirrors the chemical patterns observed in some of the members of GCs. As part of the effort to characterize their chemodynamical properties, we derived abundances for up to 25 elements in a sample of 33 N-rich field giant stars (18 of them are studied for the first time) using high-resolution optical spectroscopy. We confirm their elevated abundances of N, Na, and Al, strongly supporting a GC origin. Given that Galactic GCs themselves formed within diverse progenitor galaxies, we sought to identify the ancestral systems of these N-rich field stars. By analyzing their dynamical parameters, we separated the sample into high-energy (HE) and low-energy (LE) groups. The HE group exhibits lower [ α /Fe] and enhanced r -process abundances compared to the LE group. This indicates that the HE stars likely escaped from GCs accreted from massive dwarf galaxies (e.g., Gaia-Sausage-Enceladus), while the LE stars probably originated from in situ GCs. We also find that the chemical pattern of these N-rich stars with [Fe/H] ⪅ −1.0 is similar to that of the high-redshift “N-emitters.” Furthermore, orbital integrations revealed a close encounter between one N-rich field star and the GC NGC 6235. Our work demonstrates the potential of using chemodynamical analyses to trace Galactic assembly through chemical peculiar stars, while highlighting that larger samples and more precise data in the future are crucial to establish definitive origins.

Slitless spectroscopy eliminates the need for slits, allowing light to pass directly through a prism or grism to generate a spectral dispersion image that encompasses all celestial objects within a specified area. This technique enables highly efficient spectral acquisition. However, when processing China Space Station Telescope (CSST) slitless spectroscopy data, the unique design of its focal plane introduces a challenge: photometric and slitless spectroscopic images do not have a one-to-one correspondence. As a result, it becomes essential to first identify and count the sources in the slitless spectroscopic images before extracting spectra. To address this challenge, we employed the You Only Look Once object detection algorithm to develop a model for detecting targets in slitless spectroscopy images. This model was trained on 1560 simulated CSST slitless spectroscopic images. These simulations were generated from the CSST Cycle 6 and Cycle 9 main survey data products, representing the Galactic and nearby galaxy regions and the high galactic latitude regions, respectively. On the validation set, the model achieved a precision of 88.6% and recall of 90.4% for spectral lines, and 87.0% and 80.8% for zeroth-order images. In testing, it maintained a detection rate >80% for targets brighter than 21 mag (medium-density regions) and 20 mag (low-density regions) in the Galactic and nearby galaxies regions, and >70% for targets brighter than 18 mag in high galactic latitude regions.

Slitless spectroscopy eliminates the need for slits, allowing light to pass directly through a prism or grism to generate a spectral dispersion image that encompasses all celestial objects within a specified area. This technique enables highly efficient spectral acquisition. However, when processing CSST slitless spectroscopy data, the unique design of its focal plane introduces a challenge: photometric and slitless spectroscopic images do not have a one-to-one correspondence. As a result, it becomes essential to first identify and count the sources in the slitless spectroscopic images before extracting spectra. To address this challenge, we employed the You Only Look Once (YOLO) object detection algorithm to develop a model for detecting targets in slitless spectroscopy images. This model was trained on 1,560 simulated CSST slitless spectroscopic images. These simulations were generated from the CSST Cycle 6 and Cycle 9 main survey data products, representing the Galactic and nearby galaxy regions and the high galactic latitude regions, respectively. On the validation set, the model achieved a precision of 88.6% and recall of 90.4% for spectral lines, and 87.0% and 80.8% for zeroth-order images. In testing, it maintained a detection rate >80% for targets brighter than 21 mag (medium-density regions) and 20 mag (low-density regions) in the Galactic and nearby galaxies regions, and >70% for targets brighter than 18 mag in high galactic latitude regions.

With the rapid increase in the number of artificial satellites, astronomical imaging is experiencing growing interference. When these satellites reflect sunlight, they produce streak-like artifacts in photometry images. Such satellite trails can introduce false sources and cause significant photometric errors. As a result, accurately identifying the positions of satellite trails in observational data has become essential. In this work, we propose a satellite trail detection model that combines the U-Net deep neural network for image segmentation with the Line Segment Detector (LSD) algorithm. The model is trained on 375 simulated images of satellite trails, generated using data from the Mini-SiTian Array. Experimental results show that for trails with a signal-to-noise ratio (SNR) greater than 3, the detection rate exceeds 99. Additionally, when applied to real observational data from the Mini-SiTian Array, the model achieves a recall of 79.57 and a precision of 74.56.

Based on the updated M giant star catalog selected from LAMOST DR9, we iden-tify substructures within the integrals-of-motion space through Friends-of-Friends cluster-ing algorithm. We obtain members belonging to several known substructures: the Sagittarius stream, Galactic Anticenter Substructure (GASS), Gaia-Enceladus-Sausage (GES), Splash, and the high-α disk. Furthermore, we also identify two groups which cannot be clearly asso-ciated with previously known substructures. Our findings confirm the existence of metal-rich constituents within the GES, representing newly formed stars that originated from the metal-enriched gas delivered during the GES merger event and subsequently evolved. Additionally, this study further expands the sample of GASS, high-α disk, and Splash stars. Analysis of these metal-rich M giant stars as members of the GES, Splash, and high-α disk compo-nents supports an evolution scenario for the early Milky Way, as proposed by previous stud-ies. In this scenario, stars initially formed in a high-α primordial disk were dynamically heated by the massive accretion event (GES). This process redistributed stellar orbits, creat-ing the Splash population, while the undisturbed portion of the primordial disk persisted as the present-day high-α disk component.

With the rapid increase in the number of artificial satellites, astronomical imaging is experiencing growing interference. When these satellites reflect sunlight, they produce streak-like artifacts in photometry images. Such satellite trails can introduce false sources and cause significant photometric errors. As a result, accurately identifying the positions of satellite trails in observational data has become essential. In this work, we propose a satellite trail detection model that combines the U-Net deep neural network for image segmentation with the Line Segment Detector algorithm. The model is trained on 375 simulated images of satellite trails, generated using data from the Mini-SiTian Array. Experimental results show that for trails with a signal-to-noise ratio greater than 3, the detection rate exceeds 99%. Additionally, when applied to real observational data from the Mini-SiTian Array, the model achieves a recall of 79.57% and a precision of 74.56%.

Based on the updated M giant star catalog selected from LAMOST DR9, we identify substructures within the integrals-of-motion space through Friends-of-Friends clustering algorithm. We obtain members belonging to several known substructures: the Sagittarius stream, Galactic Anticenter Substructure (GASS), Gaia-Enceladus-Sausage (GES), Splash, and the high-α disk. Furthermore, we also identify two groups which cannot be clearly associated with previously known substructures. Our findings confirm the existence of metal-rich constituents within the GES, representing newly formed stars that originated from the metal-enriched gas delivered during the GES merger event and subsequently evolved. Additionally, this study further expands the sample of GASS, high-α disk, and Splash stars. Analysis of these metal-rich M giant stars as members of the GES, Splash, and high-α disk components supports an evolution scenario for the early Milky Way, as proposed by previous studies. In this scenario, stars initially formed in a high-α primordial disk were dynamically heated by the massive accretion event (GES). This process redistributed stellar orbits, creating the Splash population, while the undisturbed portion of the primordial disk persisted as the present-day high-α disk component.

M giants, with their distinctive properties such as high luminosity, serve as excellent indicators for mapping the structure of the Milky Way. The distance to distant M giants can be determined by using the color-magnitude relation (CMR), which is derived from color-magnitude diagrams of specific systems in previous studies. In this work, we aimed to achieve more accurate distance determination for M giants by focusing on open clusters (OCs) with a large number of member stars and thus improve the CMR. For the first time, we compiled a census of OCs harboring M giants using Gaia Data Release 3 (DR3) and Large Sky Area Multi-Object Fiber Spectroscopic Telescope Data Release 9. We identified 58 M giants associated with 43 OCs and obtained their astrometric and photometric parameters from Gaia DR3. Using the distances of these OCs, we derived the CMR for M giants as a linear correlation, expressed as $M_{Ks}=3.85-8.26(J-K_s$). This linear relation proved superior to the empirical distance relation in characterizing the CMR of M giants. The photometric distances of M giants derived from the CMR are consistent with the parallax distances from Gaia and known spectroscopic distances, with median deviations of 1.5% and 2.3%, respectively. Using the distances of M giants derived from the CMR, we computed their radial velocity ($V_R$), azimuthal velocity ($Vφ$), and vertical velocity ($V_Z$), respectively. The distributions of these velocities revealed key features of the Galactic disk, including oscillation, north-south rotational asymmetry, and warp. These findings are consistent with previous studies and further validate the reliability of the derived CMR.