Star Identification Research Articles

Deep learning interpretability analysis for carbon star identification in Gaia DR3

A large fraction of asymptotic giant branch (AGB) stars develop carbon-rich atmospheres during their evolution. Based on their color and luminosity, these carbon stars can easily be distinguished from many other kinds of stars. However, numerous G, K, and M giants also occupy the same region as carbon stars on the HR diagram. Despite this fact, their spectra exhibit differences, especially in the prominent CN molecular bands. We aim to distinguish carbon stars from other kinds of stars using Gaia's XP spectra while providing attributional interpretations of key features that are necessary for identification and even discovering new key spectral features. We propose a classification model named "GaiaNet," an improved one-dimensional convolutional neural network specifically designed for handling Gaia's XP spectra. We utilized SHapley Additive exPlanations (SHAP), an approach for interpretability based on game theoretic, to determine SHAP values for each feature in a spectrum, enabling us to explain the output of the GaiaNet model and provide further meaningful analysis. Compared to four traditional machine learning methods, the GaiaNet model exhibits an average classification accuracy improvement of approximately 0.3% on the validation set, with the highest accuracy reaching 100%. Utilizing the SHAP model, we present a clear spectroscopic heatmap highlighting molecular band absorption features primarily distributed around CN_ and CN_ and we summarize five key feature regions for carbon star identification. Upon applying the trained classification model to the CSTAR sample with Gaia "xp_sampled_mean" spectra, we obtained 451 new candidate carbon stars as a by-product. Our algorithm is capable of discerning subtle feature differences from low-resolution spectra of Gaia, thereby assisting us in effectively identifying carbon stars with typically higher temperatures and weaker CN features while providing compelling attributive explanations. The interpretability analysis of deep learning holds significant potential in spectral identification.

Generation of guide star catalog for the all-time three-FOV star sensor applied on airborne platforms

The guide star catalog plays a crucial role in the star sensor, impacting the system’s star identification speed and attitude measurement accuracy. Currently, the generation method for the guide star catalog is well developed for star sensors used on satellites. However, the inter-FOV guide star catalog for the all-time three-FOV star sensor still faces challenges such as excessive storage requirements, non-uniform distribution of guide stars, and incompleteness. Aiming at these problems, this paper proposes a generation method for the inter-FOV guide star catalog. Initially, a method for estimating instrument magnitudes in the short-wave infrared (SWIR) band is proposed and validated. Then, a theoretical model of the all-time three-FOV star sensor is established, and its extreme detection magnitudes are analyzed. Subsequently, the guide stars are initially selected using the improved spherical spiral method (ISSM) based on their uniformity and brightness, followed by the combination of the generalized regression neural network (GRNN) and the Monte Carlo method to generate the guide star catalog. Finally, it is verified by simulation experiments that the proposed method improves the uniformity and completeness while reducing the storage capacity.

Double-lined Spectroscopic Binaries from the LAMOST Low-resolution Survey

We report on a data-driven spectral model that we have developed for the identification of double-lined spectroscopic binary stars (SB2s) in the Large Sky Area Multi-Object fiber Spectroscopic Telescope low-resolution survey (R ∼ 1800). Employing simultaneous fitting with both single-star and binary-star models, we detected over 4800 SB2 candidates, where both components are detectably contributing to the spectrum, from an initial pool of 2.6 million objects. Tests show that our model favors FGK-type main-sequence binaries with high mass ratio (q ≥ 0.7) and large radial velocity (RV) separation (ΔRV ≥ 100 km s−1). Almost all of these candidates are positioned above the main sequence in the color–magnitude diagram, indicating their binary nature. Additionally, we utilized various observational data, including spectroscopy, photometry, parallax, and extinction, to determine multiple physical parameters such as the effective temperature, age, metallicity, RV, mass, mass ratio, stellar radius, along with their associated uncertainties for these SB2 candidates. For the 44 candidates with seven or more observational epochs, we provide complete orbital solutions. We make available catalogs containing various stellar parameters for identified SB2 systems.

No Maunder Minimum phase in HD 4915

Context. The long-term solar magnetic activity and its cyclical behaviour, which is maintained by a dynamo mechanism, are both still challenging problems for astrophysics. In particular, an atypical event occurred between 1645 and 1715, when the solar activity was remarkably decreased and the number of sunspots was extremely reduced. However, the exact events that unfolded during the solar cycle remain unclear. The discovery of longer activity minima in cool stars may shed light on the nature of the complex mechanisms involved in the long-term behaviour of the solar-stellar dynamo. Aims. Our aim is to explore whether the G5V solar-like star HD 4915, which showed a striking chromospheric activity pattern in a previous study performed with HIRES data, might be considered a bona fide Maunder Minimum (MM) candidate. Methods. We analysed over 380 spectra acquired between 2003 and 2022 using the HARPS and HIRES spectrographs. We carried out a detailed search for activity signatures in HD 4915 by using the Mount Wilson and the Balmer Hα activity indexes. This task was performed by means of the generalised Lomb-Scargle periodogram. Results. The new HARPS data show that the chromospheric activity of HD 4915 is not decreasing. In fact, the increases in the activity after the broad minimum in three years reaches the level of activity before this phase, suggesting that it is not entering an MM phase. We also calculate a rotation period of 23.4 ± 0.2 d, which has not been reported before. Conclusions. HD 4915 shows a distinctive activity behaviour that was initially attributed to a possible and incipient MM phase. Additional HARPS data allowed us to discard an MM in the star. Our analysis shows that the complex activity pattern of HD 4915 might be ruled by a multiple activity cycle, in which a shorter cycle of 4.8 yr is modulated by a potential longer cycle. More activity surveys with extensive records and suitable cadence are crucial for an accurate identification of stars in magnetic grand minima.

The impact of UV spectra on searches for extremely metal-poor stars: A study for future CSST observations

Extremely metal-poor (EMPs, [Fe/H] < −3.0) and carbon-enhanced EMP (CE-EMP, [Fe/H] < −3.0 and [C/Fe]> + 1.0) stars are crucial for understanding the chemical evolution and early formation of the galaxies. Current research on EMP stars is limited by small samples, and ultraviolet (UV) band spectra are lacking. The China Space Station Telescope (CSST) will provide high-quality, low-resolution spectra across wavelengths from near-ultraviolet to near-infrared, with a limiting magnitude of about 21 mag. These will help in identifying EMPs and CE-EMP candidates in the distant Milky Way and nearby galaxies. The present study first uses the simulated CSST spectra to quantitatively evaluate the contribution of UV band spectra in predicting [Fe/H], [α/Fe], and [C/Fe]. The results indicate that UV band spectra reduce the mean absolute error by 0.04, 0.04, and 0.03, respectively, with a σ of 0.02, 0.03, and 0.04, respectively. Further spectral analysis reveals that the EMP stars show unique spectral-line features in the UV band, with significant differences compared to stars of higher metallicity, which could help in identifying EMP stars. Additionally, we tested the impact of UV band spectra on identifying EMP and CE-EMP stars at different noise levels and find that models including UV band spectra improve the identification of EMP and CE-EMP stars in terms of accuracy, recall, and F1 score. At low signal-to-noise ratio (S/N), the model including the UV band achieves a recall of 0.94, significantly higher than the model without the UV band (Recall = 0.48), doubling the capability to identify EMP stars. As the S/N increases, the inclusion of the UV band maintains high recall. This suggests that UV spectra in future large surveys could reduce the risk of missing potentially interesting stellar candidates, ensuring a more comprehensive identification of all possible EMP and CE-EMP star candidates.

Origin of LAMOST J1010+2358 Revisited

Signature from Pop III massive stars of 140– that end their lives as pair-instability supernovae (PISNe) are expected to be seen in very metal-poor (VMP) stars of . Although thousands of VMP stars have been discovered, the identification of a VMP star with a PISN signature has been elusive. Recently, the VMP star LAMOST J1010+2358 was claimed to be the first star with a clear PISN signature. A subsequent study showed that ejecta from low-mass core-collapse supernovae (CCSNe) can also fit the abundance pattern equally well and additional elements such as C and Al are required to differentiate the two sources. Follow-up observations of LAMOST J1010+2358 by two independent groups were able to detect both C and Al. Additionally, key odd elements such as Na and Sc were also detected whose abundances were found to be higher than the upper limits found in the original detection. We perform a detailed analysis of the newly observed abundance patterns by exploring various possible formation channels for VMP stars. We find that purely low-mass CCSN ejecta as well as the combination of CCSN and Type 1a SN ejecta can provide an excellent fit to the newly observed abundance pattern. Our results confirm earlier analysis that the newly observed abundance pattern is peculiar but has no signatures of PISN.

MEASNet. I. A Model for Barium Star Identification and s-process Abundance Estimation from LAMOST DR10 Low-resolution Survey

Barium stars are peculiar stars with enhanced slow neutron capture process (s-process) elements. Abundance analysis of them aids in better understanding the chemical evolution of the Milky Way. In this paper, we introduce a data-driven method named the memory-enhanced adaptive spectral network (MEASNet) to search for barium candidates in the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) low-resolution survey (LRS) and estimate the abundance of five s-process elements: Sr, Y, Ba, Ce, and Nd. MEASNet, trained using spectra from common stars in both LAMOST and the Galactic Archaeology with HERMES survey, showcases notable performance: for the classification task, precision = 98.22% and recall = 94.12%; in prediction, the mean absolute error for the seven elements range between 0.07 and 0.15 dex. After training, we apply the model to 4,083,003 stellar spectra from LAMOST DR10 LRS, successfully identifying 1,803,670 spectra of barium candidates ([Ba/Fe] ≥ 0.25 dex) along with their five s-process elemental abundances. The catalog enlarges the sample size, providing a wealth of data for further statistical analysis of the formation and evolution of barium stars. Meanwhile, this work highlights the potential value of MEASNet in star classification and abundance estimation, offering a strong reference for future data-driven models.

The JWST Resolved Stellar Populations Early Release Science Program. VI. Identifying Evolved Stars in Nearby Galaxies

We present an investigation of evolved stars in the nearby star-forming galaxy Wolf–Lundmark–Melotte (WLM), using Near-Infrared Camera (NIRCam) imaging from the JWST Resolved Stellar Populations Early Release Science program. We find that various combinations of the F090W, F150W, F250M, and F430M filters can effectively isolate red supergiants and thermally pulsing asymptotic giant branch (TP-AGB) stars from one another, while also providing a reasonable separation of the primary TP-AGB subtypes: carbon-rich C-type stars and oxygen-rich M-type stars. The classification scheme we present here agrees very well with the well-established Hubble Space Telescope (HST) medium-band filter technique. The ratio of C to M-type stars is 0.8 ± 0.1 for both the new JWST and the HST classifications, which is within 1σ of empirical predictions from optical narrowband CN and TiO filters. The evolved star colors show good agreement with the predictions from the PARSEC + COLIBRI stellar evolutionary models, and the models indicate a strong metallicity dependence that makes stellar identification even more effective at higher metallicity. However, the models also indicate that evolved star identification with NIRCam may be more difficult at lower metallicities. We test every combination of NIRCam filters using the models and present additional filters that are also useful for evolved star studies. We also find that ≈90% of the dusty evolved stars are carbon rich, suggesting that carbonaceous dust dominates the present-day dust production in WLM, similar to the findings in the Magellanic Clouds. These results demonstrate the usefulness of NIRCam in identifying and classifying dust-producing stars without the need for mid-infrared data.

Unveiling ophiuroid biodiversity across North Atlantic habitats via an integrative perspective

The depths of the North Atlantic Ocean host a species-rich fauna providing heterogeneous habitats from thermal vent fields to cold-water coral reefs. With the increasing threat of destruction of deep-sea habitats due to human impacts, such as demersal fishing and the beginning of deep-sea mining, an analysis of the diversity and distribution of species is crucial for conservation efforts. Brittle stars occur in high biomasses, contributing to the biodiversity of the seafloor. Specimens were collected during several scientific expeditions to gain a more detailed insight into the brittle star diversity in the North Atlantic Ocean. An integrative approach to identify the species with DNA barcoding (mtCOI) in combination with morphological studies revealed 24 species. Most species have been previously identified in the North Atlantic, but sequences for 13 species are newly added to public repositories. Additionally, the MALDI-TOF-MS proteomic analysis was successfully applied for 197 specimens with known COI barcodes. Results are congruent with other molecular species delimitations demonstrating the functionality of proteomics for the identification of brittle stars. This dataset significantly expands our understanding of the taxonomic and genetic diversity of brittle stars and contributes to publicly available data. It emphasizes the importance of considering habitat heterogeneity for large scale patterns of biodiversity.

A Robust Star Identification Algorithm for Resident Space Object Surveillance

Star identification algorithms can be applied to resident space object (RSO) surveillance, which includes a large number of stars and false stars. This paper proposes an efficient, robust star identification algorithm for RSO surveillance based on a neural network. First, a feature called equal-frequency binning radial feature (EFB-RF) is proposed for guide stars, and a superficial neural network is constructed for feature classification. Then the training set is generated based on EFB-RF. Finally, the remaining stars are identified using a residual star matching method. The simulation experiment and results show that the identification rate of our algorithm can reach 99.82% under 1 pixel position noise, and it can reach 99.54% under 5% false stars. When the percentage of missing stars is 15%, it can reach 99.40%. The algorithm is verified by RSO surveillance.

Revealing the Fate of Exoplanet Systems: Asteroseismic Identification of Host Star in the Red Clump or Red Giant Branch

Determining the evolutionary stage of stars is crucial for understanding the evolution of exoplanetary systems. In this context, red giant branch (RGB) and red clump (RC) stars, which formed at stages in the later evolution of stars situated before and after the helium flash, harbor critical clues to unveiling the evolution of planets. The first step in revealing these clues is to confirm the evolutionary stage of the host stars through asteroseismology. However, up to now, host stars confirmed to be RGB or RC stars are extremely rare. In this investigation, we present a comprehensive asteroseismic analysis of two evolved stars, HD 120084 and HD 29399, known to harbor exoplanets, using data from the Transiting Exoplanet Survey Satellite. We have discovered for the first time that HD 120084 is an RC star in the helium-core-burning phase, and confirmed that HD 29399 is an RGB star in the hydrogen-shell burning phase. Through the precise measurement of asteroseismic parameters such as νmax , Δν, and ΔΠ1, we have determined the evolutionary states of these stars and derived their fundamental stellar parameters. The significance of this study lies in the application of automated techniques to measure asymptotic period spacings in red giants, which provides critical insights into the evolutionary outcomes of exoplanet systems. We demonstrate that asteroseismology is a potent tool for probing the internal structures of stars, thereby offering a window into the past and future dynamics of planetary orbits. The presence of a long-period giant planet orbiting HD 120084, in particular, raises intriguing questions about the potential engulfment of inner planets during the host star’s expansion, a hypothesis that warrants further investigation.

Periodic variable A-F spectral type stars in the southern TESS continuous viewing zone

Aims. Our primary objective is to accurately identify and classify the variability of A-F stars in the southern continuous viewing zone of the TESS satellite. The brightness limit was set to 10 mag to ensure the utmost reliability of our results and allow for spectroscopic follow-up observations using small telescopes. We aim to compare our findings with existing catalogues of variable stars. Methods. The light curves from TESS and their Fourier transform were used to manually classify stars in our sample. Cross-matching with other catalogues was performed to identify contaminants and false positives. Results. We have identified 1171 variable stars (51% of the sample). Among these variable stars, 67% have clear classifications, which includes δ Sct and γ Dor pulsating stars and their hybrids, rotationally variables, and eclipsing binaries. We have provided examples of the typical representatives of variable stars and discussed the ambiguous cases. We found 20 pairs of stars with the same frequencies and identified the correct source of the variations. Additionally, we found that the variations in 12 other stars are caused by contamination from the light of faint nearby large-amplitude variable stars. To compare our sample with other variable star catalogues, we have defined two parameters reflecting the agreement in identification of variable stars and their classification. This comparison reveals intriguing disagreements in classification ranging from 52 to 100%. However, if we assume that stars without specific types are only marked as variable, then the agreement is relatively good, ranging from 57 to 85% (disagreement 15–43%). We have demonstrated that the TESS classification is superior to the classification based on other photometric surveys. Conclusions. The classification of stellar variability is complex and requires careful consideration. Caution should be exercised when using catalogue classifications.

Determination of dark matter distribution in Ursa Major III and constraints on dark matter annihilation* *Supported by the National Natural Science Foundation of China (11947005, 12175248, 12205388, 12227804), the Science & Technology Development Fund of Tianjin Education Commission for Higher Education (2020KJ003), and the PhD Research Startup Foundation of Tianjin Normal University (52XB1912)

The recently discovered satellite dwarf galaxy Ursa Major III provides a promising opportunity to explore the signatures resulting from dark matter (DM) annihilation owing to its proximity and large J-factor. Given the absence of an excess of γ-ray signatures originating from Ursa Major III, observations of γ-rays, such as those from Fermi-LAT, can be utilized to set constraints on the DM annihilation cross-section. In this study, we determined the DM density profile and considered the relationship between DM density and velocity dispersion at different locations within Ursa Major III through Jeans analysis. We calculated the J-factor of Ursa Major III for s-wave annihilation along with the effective J-factors for p-wave and Sommerfeld enhanced annihilation scenarios. Employing these derived J-factors, we set stringent constraints on DM annihilation cross-sections in three scenarios. Given the substantial impact of member star identification on the J-factor of Ursa Major III, we further calculated J-factors under the exclusion of the largest velocity outlier. Our analysis reveals a notable reduction in the median value and an increase in the deviation of J-factors, thereby leading to considerably weaker constraints.

Classification of LAMOST spectra of B-type and hot subdwarf stars using kernel support vector machine

Machine learning has emerged as a leading field in artificial intelligence, demonstrating expert-level performance in various domains. Astronomy has benefited from machine learning techniques, particularly in classifying and identifying stars based on their features. This study focuses on the spectra-based classification of 11,408 B-type and 2422 hot subdwarf stars. The study employs baseline correction using Asymmetric Least Squares (ALS) to enhance classification accuracy. It applies the Pan-Core concept to identify 500 unique patterns or ranges for both types of stars. These patterns are the foundation for creating Support Vector Machine (SVM) models, including the linear (L-SVM), polynomial (P-SVM), and radial basis (R-SVM) kernels. Parameter tuning for the SVM models is achieved through cross-validation. Evaluation of the SVM models on test data reveals that the linear kernel SVM achieves the highest accuracy (87.0%), surpassing the polynomial kernel SVM (84.1%) and radial kernel SVM (80.1%). The average calibrated accuracy falls within the range of 90–95%. These results demonstrate the potential of using spectrum-based classification to aid astronomers in improving and expanding their understanding of stars, with a specific focus on the identification of hot subdwarf stars. This study presents a valuable investigation for astronomers, as it enables the classification of stars based on their spectra, leveraging machine learning techniques to enhance their knowledge and insights in astronomy.

Results of a long-term optical variability study of 11 quasars and VRI photometry of comparison stars

ABSTRACT We present the results of a 15-yr long-term optical monitoring of 11 quasars conducted with the 2-m Ritchey–Chretien–Coude and the 50/70 cm Schmidt telescopes at the Rozhen National Astronomical Observatory, Bulgaria. Our observations are performed with standard Johnson-Cousins $VRI$ band filters and for each quasar we present a set of comparison standard stars that can be used for monitoring of objects in these fields (including finding charts for the stars identification). The variability and periodicity of each quasar are analysed individually and discussed. The physical properties of each quasar, such as their classification, redshift, and radio structures, are also discussed based on previous literature. Damped random walk model shows the best or the second best fit to the light curves of all objects. However, in six cases periodic models provide comparably good fits and make these six objects a valuable addition to the growing sample of quasars with periodic flux variation. They will be suitable for further investigation of the hitherto unclear mechanisms that give rise to this variability pattern. Our results provide important insights into the long-term variability and physical properties of quasars, which can further deepen our understanding of the nature and evolution of active galaxy nuclei.

Self-Calibration for Star Sensors.

Aiming to address the chicken-and-egg problem in star identification and the intrinsic parameter determination processes of on-orbit star sensors, this study proposes an on-orbit self-calibration method for star sensors that does not depend on star identification. First, the self-calibration equations of a star sensor are derived based on the invariance of the interstar angle of a star pair between image frames, without any requirements for the true value of the interstar angle of the star pair. Then, a constant constraint of the optical path from the star spot to the center of the star sensor optical system is defined to reduce the biased estimation in self-calibration. Finally, a scaled nonlinear least square method is developed to solve the self-calibration equations, thus accelerating iteration convergence. Our simulation and analysis results show that the bias of the focal length estimation in on-orbit self-calibration with a constraint is two orders of magnitude smaller than that in on-orbit self-calibration without a constraint. In addition, it is shown that convergence can be achieved in 10 iterations when the scaled nonlinear least square method is used to solve the self-calibration equations. The calibrated intrinsic parameters obtained by the proposed method can be directly used in traditional star map identification methods.

Detection of Accretion Shelves Out to the Virial Radius of a Low-mass Galaxy with JWST

We report the serendipitous discovery of an extended stellar halo surrounding the low-mass galaxy Ark 227 (M * = 5 × 109 M ⊙; d = 35 Mpc) in deep JWST NIRCam imaging from the Blue Jay Survey. The F200W–F444W color provides robust star–galaxy separation, enabling the identification of stars at very low density. By combining resolved stars at large galactocentric distances with diffuse emission from NIRCam and Dragonfly imaging at smaller distances, we trace the surface-brightness and color profiles of this galaxy over the entire extent of its predicted dark matter halo, from 0.1 to 100 kpc. Controlled N-body simulations have predicted that minor mergers create “accretion shelves” in the surface-brightness profile at large radius. We observe such a feature in Ark 227 at 10–20 kpc, which, according to models, could be caused by a merger with total mass ratio 1:10. The metallicity declines over this radial range, further supporting the minor merger scenario. There is tentative evidence of a second shelf at μ V ≈ 35 mag arcsec−2 extending from 50 to 100 kpc, along with a corresponding drop in metallicity. The stellar mass in this outermost envelope is ≈107 M ⊙. These results suggest that Ark 227 experienced multiple mergers with a spectrum of lower-mass galaxies—a scenario that is broadly consistent with the hierarchical growth of structure in a cold-dark-matter-dominated universe. Finally, we identify an ultra-faint dwarf associated with Ark 227 with M * ≈ 105 M ⊙ and μ V,e = 28.1 mag arcsec−2, demonstrating that JWST is capable of detecting very-low-mass dwarfs to distances of at least ∼30 Mpc.

Accurate Pointing Determination of Space Targets under the Stars Background

Abstract. This study proposes a method for determining the direction of space targets using existing satellite constellations. Usually, when optical satellites point towards deep space for imaging, stars inevitably appear in the image. This study conducts all targets extraction, star identification, camera calibration. Ultimately obtaining high-precision space targets longitude and latitude information. One of the Jilin-1 GF06A series satellite was used to conduct observation experiments on space targets of this study. Each scene has an exposure time of 10 seconds, and a total of 90 seconds of observation data was obtained. This study analysed the results of star extraction and the identification result which indicate that GF06A can capture a maximum magnitude of 15.6. After using the extraction star coordinate information for calibration, the interior accuracy of camera was better than 0.48 arcseconds. Then, the precise longitude and latitude information of 5 space targets were obtained. Finally, cataloguing information of these stars is provided, which is beneficial for determining the orbit of the target in the future.

A new star identification using patterns in the form of Gaussian mixture models

This paper presents an innovative star identification algorithm specifically designed for lost-in-space scenarios in satellite missions. The algorithm introduces a unique approach by utilizing singular values to generate Gaussian mixture models as distinctive features for star identification. By employing a matching process involving candidate filtering, similarity comparison, and validation, the algorithm demonstrates remarkable performance in precisely identifying stars while minimizing false identifications even in demanding environmental conditions. Extensive simulations are conducted to evaluate the algorithm’s effectiveness under various scenarios with positional error and the presence of false stars. The algorithm exhibits reliable and consistent performance, making it highly suitable for dynamic scenarios and contributing to the reduction of hardware burden in star sensor systems. Furthermore, the proposed algorithm offers prominent performance across varying field of view sizes, thereby enhancing its practicality and usability in a wide range of missions.

Robust and adaptive star identification algorithm based on linear assignment for multiple large field of view visual imaging systems

The integration of the visual imaging system and the self-attitude determination system in on-orbit space projects necessitates robust star identification algorithms. However, disturbances in the on-orbit environment pose a challenge to the accuracy and efficiency of star identification algorithms. This paper introduces a novel star identification algorithm, to the best of our knowledge, designed for multiple large field of view (FOV) visual imaging systems, providing stability in the presence of the noise types, including position noise, lost-star noise, and fake-star noise. We employ the dynamic simulated star images generation method to construct simulated star image libraries suitable for various cameras with different FOV angles. Our algorithm comprises two parts: the star edge matching for coarse matching of interstellar angular distances based on linear assignment, and the star point registration for precise matching of star vectors. This innovative combination of local edge generation and global matching approach achieves an impressive 97.83% identification accuracy, maintaining this performance even with a standard deviation of one pixel in image plane errors and up to five missing stars. A comprehensive approach involving both simulated and empirical experiments was employed to validate the algorithm’s effectiveness. This integration of the visual imaging system and the self-attitude determination system offers potential benefits such as reduced space equipment weight, simplified satellite launch processes, and decreased maintenance costs.