Star Identification Research Articles

Cloud Identification and Reconstruction from All-sky Camera Images Based on Star Photometry Estimation

Cloud cover significantly influences ground-based optical astronomical observations, with nighttime astronomy often relying on visible light all-sky cameras for cloud detection. However, existing algorithms for processing all-sky cloud images typically require extensive manual intervention, posing challenges in identifying clouds with pronounced extinction characteristics. Furthermore, there is a lack of effective means for detailed visualization of cloud cover. To address these issues, this paper proposes a method that reconstructs the cloud distribution and thickness from all-sky images through star identification and photometry. Specifically, a high-precision star coordinate to the pixel position imaging model calibration method based on the star recognition for fisheye lenses is investigated, resulting in an all-sky rms error of less than 0.87 pixels. Based on the comprehensive reference star catalog, an optimized star extraction method based on SExtractor is developed to handle the difficulty of image source detection in all-sky cloud images. The optical thickness and distribution of cloud layers is calculated through star matching and extinction measurements. Finally, contingent upon the capability of camera and catalog star density, seven cloud layer reconstruction methods are proposed based on meshing and machine learning techniques, achieving a reconstruction accuracy of up to 1.°8. The processing results from real observed images indicate that the proposed method offers a straightforward calibration process and delivers excellent cloud cover extraction and reconstruction outcomes, thereby providing practical value in telescope dynamic scheduling, site characterization and the development of observation strategies.

A Star Identification Graph Algorithm Based on Angular Distance Matching Score Transfer

A Star Identification Graph Algorithm Based on Angular Distance Matching Score Transfer

ABYSS. II. Identification of Young Stars in Optical SDSS Spectra and Their Properties

We developed a tool that measures equivalent widths of various lines in low-resolution optical spectra, and it was applied to stellar spectra obtained as part of SDSS-V and LAMOST programs. These lines, such as Li i, which directly indicates stellar youth, or optical H i and Ca ii, which in emission indicate activity associated with stellar youth, are commonly seen in YSOs. We observe several notable differences in the properties of these lines between YSOs and the field stars. Using these data, we devise a set of criteria through which it is possible to confirm the youth of stars that have been observed by the ABYSS program, as well as to identify likely young stars that have serendipitously been observed by other programs. We examine the decrement of H lines seen in emission in CTTSs, and estimate the properties of the accretion stream that is responsible for the production of these lines. Finally, we examine the evolution of Li i as a function of age, and characterize the scatter in its abundance that appears to be intrinsic in young M dwarfs.

A lost-in-space star identification algorithm based on regularized pattern recognition

Accuracy in spacecraft attitude determination has been improved continuously in parallel to scientific payloads requiring higher accuracy. Star trackers aim to maintain attitude determination by matching observations to the reference stars. They provide very high accuracy at any point in space at cost of higher computational complexity. This study presents an algorithm of lost-in-space star identification with high accuracy and acceptable computational complexity. This task is achieved by means of a novel dictionary-based star matching method by applying nearest neighbor classification through a binary search of Euclidean distances and regularization sequentially, accompanied by use of unconventional feature vectors representing each observation individually. The proposed method yields an estimated direction of the boresight vector orthogonal to the camera plane with an estimated axial rotation. The Hipparcos catalog is used for database generation, and the sensor parameters are taken from the project SharjahSat-1. The performance is evaluated in consideration of noise including position, brightness and false stars. By means of complexity analysis of database size and average run time, it is shown that the proposed method achieves better accuracy than other methods and maintains very high robustness to position noise and brightness noise. A high level of accuracy is achieved with an amount of database size and average run time that are competitive in the recent literature. It is demonstrated that the levels of pointing accuracy offered by state-of-the-art ADCS models are achieved by the proposed method with the given probabilities.

Identification and suppression of stars under large range of star field density variation

Identification and suppression of stars under large range of star field density variation

Multi-FOV Star Identification Based on Wedge-Shaped Triangle Pattern

Multi-FOV Star Identification Based on Wedge-Shaped Triangle Pattern

Identification of Blue Horizontal-branch Stars from LAMOST DR5

We construct a new catalog of the blue horizontal-branch (BHB) stars from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) DR5 data set, which contains 5355 + 81 BHB stars at high Galactic latitude (∣Glat∣ > 20°). We combine the spectral line indices with a set of Balmer line profile selection criteria to identify the BHB stars. During the selection process, we use the line index of Ca ii K to exclude the metal-rich A-type dwarfs. We obtain their atmospheric parameters by crossmatching our BHB stars with the catalog provided by Xiang et al. The results show that our sample is consistent with the theoretical T eff–log g evolutionary tracks of the BHB stars, indicating that our method is robust for identifying BHB stars from the LAMOST spectra. Their spatial distribution indicates that most of our BHB stars are located in the inner halo or the disk of the Milky Way. Combined with other BHB samples from the literature, the BHB stars can cover a large Galactic volume, which makes them a better probe for studying the kinematics, dynamics, and structural characteristics of the Milky Way.

Analysis and improvement of star identification algorithm based on singular value decomposition for star sensor

Analysis and improvement of star identification algorithm based on singular value decomposition for star sensor

On the Identification of N-rich Metal-poor Field Stars with Future Chinese Space Station Telescope

During the long term evolution of globular clusters (GCs), some member stars are lost to the field. The recently found nitrogen-rich (N-rich) metal-poor field stars are promising candidates of these GC escapees, since N enhancement is the fingerprint of chemically enhanced populations in GCs. In this work, we discuss the possibility of identifying N-rich metal-poor field stars with the upcoming Chinese Space Station Telescope (CSST). We focus on the main survey camera with NUV, u, g, r, i, z, y filters and slitless spectrograph with a resolution about 200. The combination of UV sensitive equipment and prominent N-related molecular lines in the UV band bodes well for the identification: the color–color diagram of (u − g) versus (g − r) is capable of separating N-rich field stars from normal halo stars, if metallicity can be estimated without using the information on u-band photometry. Besides, the synthetic spectra show that a signal-to-noise ratio of 10 is sufficient to identify N-rich field stars. In the near future, a large sample of N-rich field stars found by CSST, combined with state-of-the-art N-body simulations will be crucial to deciphering GC-Galaxy co-evolution.

A star identification algorithm for rolling shutter exposure based on Hough transform

The star identification algorithm usually identifies stars by angular distance matching. However, under high dynamic conditions, the rolling shutter effect distorts the angular distances between the measured and true star positions, leading to plethoric false matches and requiring complex and time-consuming verification for star identification. Low identification rate hinders the application of low-noise and cost-effective rolling shutter image sensors. In this work, we first study a rolling shutter distortion model of angular distances between stars, and then propose a novel three-stage star identification algorithm to identify distorted star images captured by the rolling shutter star sensor. The first stage uses a modified grid algorithm with adaptive error tolerance and an expanded pattern database to efficiently eliminate spurious matches. The second stage performs angular velocity estimation based on Hough transform to verify the matches that follow the same distortion pattern. The third stage applies a rolling shutter error correction method for further verification. Both the simulation and night sky image test demonstrate the effectiveness and efficiency of our algorithm under high dynamic conditions. The accuracy of angular velocity estimation method by Hough transform is evaluated and the root mean square error is below 0.5 (°)/s. Our algorithm achieves a 95.7% identification rate at an angular velocity of 10 (°)/s, which is much higher than traditional algorithms.

The positional probability and true host star identification of TESS exoplanet candidates

Abstract We present a method for deriving a probabilistic estimate of the true source of a detected TESS transiting event. Our method relies on comparing the observed photometric centroid offset for the target star with models of the offset that would occur if the event was either on the target or any of the Gaia identified nearby sources. The comparison is done probabilistically, allowing us to incorporate the uncertainties of the observed and modelled offsets in our result. The method was developed for TESS Full Frame Image lightcurves produced from the SPOC pipeline, but could be easily adapted to lightcurves from other sources. We applied the method on 3226 TESS Objects of Interest (TOIs), with a released lightcurve from SPOC. The method correctly identified 96.5% of 655 known exoplanet hosts as the most likely source of the eclipse. For 142 confirmed Nearby Eclipsing Binaries (NEBs) and Nearby Planet Candidates (NPCs), a nearby source was found to be the most likely in 96.5% of the cases. For 40 NEBs and NPCs where the true source is known, it was correctly designated as the most likely in 38 of those. Finally, for 2365 active planet candidates, the method suggests that 2072 are most likely on-target and 293 on a nearby source. The method forms a part of an in-development vetting and validation pipeline, called RAVEN, and is released as a standalone tool.

프라이빗 메시지 서비스의 이용 동기가 만족과 충성도에 미치는 영향 연구

This study is focused on 'Private Message Service', a monthly subscription service that allows users to receive and reply to messages sent by artists. This study extracted the motivation for using private message service(pseudo feeling of love, entertainment and rest, friendliness, star identification, information seeking, idol support, and reference group conformance desire) and examined how the motivation for using private message service affects satisfaction and loyalty. This study conducted a survey of 354 Chinese female K-Pop fans who had experiences using the private message service. It was found that entertainment and rest, friendliness, star identification, and information seeking motivation have positive effects on satisfaction, while psuedo feeling of love was found to have negative effects on satisfaction. Among the motivational factors for using private message service, entertainment and rest, friendliness, star identification, and information seeking motivation was found to have positive effects on loyalty. And satisfaction was found to have positive effects on loyalty. In addition, there is a complete mediating effect of satisfaction between entertainment and rest, friendliness, star identification, information-seeking motivation, respectively with loyalty. It is hoped that findings of this study would contribute to enhancing the quality of the private message services.

SPYGLASS. IV. New Stellar Survey of Recent Star Formation within 1 kpc

Young stellar populations provide a powerful record that traces millions of years of star formation history in the solar neighborhood. Using a revised form of the SPYGLASS young star identification methodology, we produce an expanded census of nearby young stars (age < 50 Myr). We then use the HDBSCAN clustering algorithm to produce a new SPYGLASS Catalog of Young Associations, which reveals 116 young associations within 1 kpc. More than 25% of these groups are largely new discoveries, as 20 are substantively different from any previous definition, and 10 have no equivalent in the literature. The new associations reveal a yet undiscovered demographic of small associations with little connection to larger structures. Some of the groups we identify are especially unique for their high transverse velocities, which can differ from the solar velocity by 30–50 km s−1, and for their positions, which can reach up to 300 pc above the galactic plane. These features may suggest a unique origin, matching existing evidence of infalling gas parcels interacting with the disk interstellar medium. Our clustering also suggests links between often-separated populations, hinting to direct structural connections between Orion Complex and Perseus OB2, and between the subregions of Vela. The ∼30 Myr old Cepheus-Hercules association is another emerging large-scale structure, with a size and population comparable to Sco-Cen. Cep-Her and other similarly aged structures are also found clustered along extended structures perpendicular to known spiral arm structure, suggesting that arm-aligned star formation patterns have only recently become dominant in the solar neighborhood.

Space Surveillance payload camera breadboard: Star tracking and debris detection algorithms

Space debris threatens space activities, demanding continuous observation and tracking by the Space Surveillance Network (SSN) to secure the Earth’s orbits. However, SST efforts are limited by the size and brightness of the debris, detecting only a small amount of the total. Seeking to overcome such limitations, this study proposes an alternative payload camera capable of extracting the attitude of a satellite in orbit and detecting under-catalogued debris. This work is a sequential study of previous research, where the camera breadboard was designed and implemented. The contribution of this paper is the evaluation of star tracking and space debris algorithms to be implemented in the payload camera, the development and implementation of a new hybrid algorithm and the elaboration of performance metrics for comparison between the algorithms. The observation data of the previous research was used as input for the algorithms’ tests. For star identification and, consequently, attitude extraction, the chosen algorithm was Tetra. The results were compared to the standard star identification software, Astrometry.net, to assess the attitude accuracy. For debris detection, ASTRiDE and the Hybrid time-index image algorithms were used. A comparison of the results was made to establish a performance evaluation metric in terms of detectability, time, and computational cost.

An Evenly Spaced LSST Cadence for Rapidly Variable Stars

Abstract Stars exhibit a bewildering variety of rapidly variable behaviors ranging from explosive magnetic flares to stochastically changing accretion to periodic pulsations or rotation. The principal Rubin Observatory Legacy Survey of Space and Time (LSST) surveys will have cadences too sparse and irregular to capture many of these phenomena. We propose here an LSST microsurvey to observe a single Galactic field, rich in unobscured stars, in a continuous sequence of 30 s exposures for one long winter night in a single photometric band. The result will be a unique data set of ∼1 million regularly spaced stellar light curves. The light curves will constitute a comprehensive collection of late-type stellar flaring, but also other classes like short-period binary systems and cataclysmic variables, young stellar objects, and ultrashort-period exoplanets. An unknown variety of anomalous solar system, Galactic, and extragalactic variables and transients may also be present. A powerful array of statistical procedures can be applied to individual light curves from the long-standing fields of time series analysis, signal processing, and econometrics. Dozens of “features” describing the variability can be extracted and the ensemble of light curves can be subject to advanced machine-learning clustering procedures. This will give a unique, authoritative, objective taxonomy of the rapidly variable sky derived from identically cadenced light curves. This microsurvey is best performed early in the Rubin Observatory program, and the results can inform the wider community on the best approaches to variable star identification and classification from the sparse, irregular cadences that dominate the planned surveys.

Star Identification of High Dynamic Star Sensor Under Rolling Shutter Exposure Mode

Star Identification of High Dynamic Star Sensor Under Rolling Shutter Exposure Mode

Adaptive Absolute Attitude Determination Algorithm for a Fine Guidance Sensor

In order to ensure the attitude determination accuracy and speed of a fine guidance sensor (FGS) in a space telescope with limited onboard hardware computing resources, an adaptive absolute attitude determination algorithm was proposed. The more stars involved in the attitude determination, the higher the attitude accuracy, but more hardware resources will be consumed. By analyzing the relationship between the attitude determination accuracy and the number of stars (NOS) in the field of view (FOV), and the relationship between the detector exposure time and the NOS, an adaptive method of adjusting the NOS in the FOV was proposed to keep the number of observed stars in the FOV of the detector at a target value. The star map recognition algorithm based on improved log-polar transformation has a higher recognition speed than the traditional algorithm but cannot accurately identify and match the corresponding guide star when the number of observed stars is less than the number of guide stars. Thus, a comparison-AND star identification algorithm based on polar coordinates was proposed. In the case of a given line-of-sight pointing and 100-frame image simulation calculation, the root mean square (RMS) value of the line-of-sight pointing error was less than 37 mas in the direction of a right ascension, and less than 25 mas in the direction of declination, as concluded from the experimental simulation.

Identification of Blue Horizontal Branch Stars with Multimodal Fusion

Blue Horizontal Branch stars (BHBs) are ideal tracers to probe the global structure of the milky Way (MW), and the increased size of the BHB star sample could be helpful to accurately calculate the MW’s enclosed mass and kinematics. Large survey telescopes have produced an increasing number of astronomical images and spectra. However, traditional methods of identifying BHBs are limited in dealing with the large scale of astronomical data. A fast and efficient way of identifying BHBs can provide a more significant sample for further analysis and research. Therefore, in order to fully use the various data observed and further improve the identification accuracy of BHBs, we have innovatively proposed and implemented a Bi-level attention mechanism-based Transformer multimodal fusion model, called Bi-level Attention in the Transformer with Multimodality (BATMM). The model consists of a spectrum encoder, an image encoder, and a Transformer multimodal fusion module. The Transformer enables the effective fusion of data from two modalities, namely image and spectrum, by using the proposed Bi-level attention mechanism, including cross-attention and self-attention. As a result, the information from the different modalities complements each other, thus improving the accuracy of the identification of BHBs. The experimental results show that the F1 score of the proposed BATMM is 94.78%, which is 21.77% and 2.76% higher than the image and spectral unimodality, respectively. It is therefore demonstrated that higher identification accuracy of BHBs can be achieved by means of using data from multiple modalities and employing an efficient data fusion strategy.

Breaking from Orbit: Star Identification Drift Following Perceived Status Gain

Breaking from Orbit: Star Identification Drift Following Perceived Status Gain

Optimal uniform guide star catalog using a geneticalgorithm.

To achieve optimal and reliable star sensors and overcome some onboard hardware and software limitations, this study aimed to make an optimal uniform guide star catalog. For this purpose, the objective function was defined by the field of view (FOV) and magnitude threshold, and then design variables were optimized. The optimal uniform guide star catalog was obtained by a genetic algorithm alongside the Latinized stratified sampling method and by a novel, to the best of our knowledge, spherical density determination algorithm based on the minimum number of stars required for a star identification algorithm. Finally, Monte Carlo simulation was used to validate the results, which indicate a dramatic improvement, including a reduction in the number of stars in the uniform catalog and an increase in the probability of observing the minimum required stars for the star identification algorithm (at least 5 stars) in 98.34% of all possible optimal FOVs (about 12°).