Guide Star Catalog Research Articles

A Partial Near-infrared Guide Star Catalog for Thirty Meter Telescope Operations

At first light, the Thirty Meter Telescope (TMT) near-infrared (NIR) instruments will be fed by a multiconjugate adaptive optics instrument known as the Narrow Field Infrared Adaptive Optics System (NFIRAOS). NFIRAOS will use six laser guide stars to sense atmospheric turbulence in a volume corresponding to a field of view of 2′, but natural guide stars (NGSs) will be required to sense tip/tilt and focus. To achieve high sky coverage (50% at the north Galactic pole), the NFIRAOS client instruments use NIR on-instrument wave front sensors that take advantage of the sharpening of the stars by NFIRAOS. A catalog of guide stars with NIR magnitudes as faint as 22 mag in the J band (Vega system), covering the TMT-observable sky, will be a critical resource for the efficient operation of NFIRAOS, and no such catalog currently exists. Hence, it is essential to develop such a catalog by computing the expected NIR magnitudes of stellar sources identified in deep optical sky surveys using their optical magnitudes. This paper discusses the generation of a partial NIR Guide Star Catalog (IRGSC), similar to the final IRGSC for TMT operations. The partial catalog is generated by applying stellar atmospheric models to the optical data of stellar sources from the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) optical data and then computing their expected NIR magnitudes. We validated the computed NIR magnitudes of the sources in some fields by using the available NIR data for those fields. We identified the remaining challenges of this approach. We outlined the path for producing the final IRGSC using the Pan-STARRS data. We have named the Python code to generate the IRGSC as irgsctool, which generates a list of NGS for a field using optical data from the Pan-STARRS 3pi survey and also a list of NGSs having observed NIR data from the UKIRT Infrared Deep Sky Survey if they are available. irgsctool is available in the public domain on this GitHub public repository (https://github.com/sshah1502/irgsc), while the generated and validated IRGSC for the 20 test fields and additional Pan-STARRS Medium Deep Survey fields can be found on Zenodo.

All-sky Guide Star Catalog for CSST

The China Space Station Telescope (CSST) is a two-meter space telescope with multiple back-end instruments. The Fine Guidance Sensor (FGS) is an essential subsystem of the CSST Precision Image Stability System to ensure the required absolute pointing accuracy and line-of-sight stabilization. In this study, we construct the Main Guide Star Catalog for FGS. To accomplish this, we utilize the information about the FGS and object information from the Gaia Data Release 3. We provide an FGS instrument magnitude and exclude variables, binaries, and high proper motion stars from the catalog to ensure uniform FGS guidance capabilities. Subsequently, we generate a HEALPix index, which provides a hierarchical tessellation of the celestial sphere, and employ the Voronoi algorithm to achieve a homogeneous distribution of stars across the catalog. This distribution ensures adequate coverage and sampling of the sky. The performance of the CSST guide star catalog was assessed by simulating the field of view of the FGS according to the CSST mock survey strategy catalog. The analysis of the results indicates that this catalog provides adequate coverage and accuracy. The catalog's performance meets the FGS requirements, ensuring the functioning of the FGS and its guidance capabilities.

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°).

ISSM-ELM - a guide star selection for a small-FOV star sensor based on the improved SSM and extreme learning machine.

The construction of a guide star catalog is crucial for a star sensor to achieve accurate star map recognition and attitude determination. At present, the methods of a guide star catalog for a large field of view (FOV) star sensor have been relatively mature. However, for a small-FOV star sensor, there are still certain problems, such as a large storage capacity of a guide star catalog, uneven distribution of stars, and easy occurrence of voids. To address these problems, we propose a construction method of a small-FOV star sensor guide star catalog based on the combination of the improved spherical spiral method (ISSM) and extreme learning machine (ELM) named the ISSM-ELM. First, a spiral reference point is used as an optical axis pointing of the star sensor, and the guide stars are preliminarily screened based on the star-diagonal distance between the star and the reference point, and the star-density and magnitude characteristics of the guide star. Then the ELM is used to supplement the guide star empty sky area to construct an integrity guide star catalog. The experimental results demonstrate that the proposed method can reduce the storage capacity of the guide star catalog, and improve its uniformity, integrity, and average brightness.

Construction of the Guide Star Catalog for Double Fine Guidance Sensors Based on SSBK Clustering.

In the Chinese Survey Space Telescope (CSST), the Fine Guidance Sensor (FGS) is required to provide high-precision attitude information of the space telescope. The fine star guide catalog is an essential part of the FGS. It is not only the basis for star identification and attitude determination but also the key to determining the absolute attitude of the space telescope. However, the capacity and uniformity of the fine guide star catalog will affect the performance of the FGS. To build a guide star catalog with uniform distribution of guide stars and catalog capacity that is as small as possible, and to effectively improve the speed of star identification and the accuracy of attitude determination, the spherical spiral binary K-means clustering algorithm (SSBK) is proposed. Based on the selection criteria, firstly, the spherical spiral reference point method is used for global uniform division, and then, the K-means clustering algorithm in machine learning is introduced to divide the stars into several disjoint subsets through the use of angular distance and dichotomy so that the guide stars are uniformly distributed. We assume that the field of view (FOV) is 0.2° × 0.2°, the magnitude range is 9∼15 mag, and the threshold for the number of stars (NOS) in the FOV is 9. The simulation shows that compared with the magnitude filtering method (MFM) and the spherical spiral reference point brightness optimization algorithm (SSRP), the guide star catalog based on the SSBK algorithm has the lowest standard deviation of the NOS in the FOV, and the probability of 5∼15 stars is the highest (over 99.4%), which can ensure a higher identification probability and attitude determination accuracy.

Guide Triangle Catalog Generation Based on Triangle Density and Utilization for Star Sensors

The guide star catalog is an important component of star identification algorithms for star sensors that indirectly determines their identification rate and other performance characteristics. Currently, star identification algorithms based on triangle features have high identification rates and strong anti-interference abilities. However, this type of method usually requires a large-size guide triangle catalog. Without proper construction, many triangles in the catalog are barely used in the pattern recognition process. In this paper, a guide triangle catalog generation method for star identification based on triangle density and utilization is proposed. First, a model of the star density and triangle density distribution in the celestial sphere is established. Then, considering factors such as the sizes and shapes of the triangles and the brightness levels of the stars, a triangle utilization model is established. Based on these models, a high-triangle-utilization, low-size guide triangle catalog is generated by eliminating low-utilization triangles to the greatest extent possible under the premise of ensuring a minimum triangle density. Some experiments were conducted to prove the performance of the proposed method. Compared with the existing methods under the same conditions, the size of the catalog constructed by the proposed method can be reduced by 70%–90%. Because the selected triangles are different, the proposed catalog exhibits a higher identification rate than other catalogs of the same size.

High-Accuracy Guide Star Catalogue Generation with a Machine Learning Classification Algorithm.

In order to detect gravitational waves and characterise their sources, three laser links were constructed with three identical satellites, such that interferometric measurements for scientific experiments can be carried out. The attitude of the spacecraft in the initial phase of laser link docking is provided by a star sensor (SSR) onboard the satellite. If the attitude measurement capacity of the SSR is improved, the efficiency of establishing laser linking can be elevated. An important technology for satellite attitude determination using SSRs is star identification. At present, a guide star catalogue (GSC) is the only basis for realising this. Hence, a method for improving the GSC, in terms of storage, completeness, and uniformity, is studied in this paper. First, the relationship between star numbers in the field of view (FOV) of a staring SSR, together with the noise equivalent angle (NEA) of the SSR—which determines the accuracy of the SSR—is discussed. Then, according to the relationship between the number of stars (NOS) in the FOV, the brightness of the stars, and the size of the FOV, two constraints are used to select stars in the SAO GSC. Finally, the performance of the GSCs generated by Decision Trees (DC), K-Nearest Neighbours (KNN), Support Vector Machine (SVM), the Magnitude Filter Method (MFM), Gradient Boosting (GB), a Neural Network (NN), Random Forest (RF), and Stochastic Gradient Descent (SGD) is assessed. The results show that the GSC generated by the KNN method is better than those of other methods, in terms of storage, uniformity, and completeness. The KNN-generated GSC is suitable for high-accuracy spacecraft applications, such as gravitational detection satellites.

Research on Partitioning Algorithm Based on Dynamic Star Simulator Guide Star Catalog

The simulated star map generated by the dynamic star simulator cannot meet the current demand for calibration of the star map recognition rate of the star sensor due to the huge amount of guide star catalog data and the slow retrieval process. This paper proposes the guide star equipartition method, which makes the constant number of each sub-region of the guide star catalog basically the same. This method first divides the declination at equal intervals through the field of view of the dynamic star simulator, and then determines the interval of the right ascension by the average number of stars with magnitude Mv that can be photographed in the field of view of the star sensor. The SAO (Smithsonian Astrophysical Observatory) catalog containing 5103 stars in the whole celestial sphere is divided into 452 sub-areas by the average number of guide stars. After the division, the guide star retrieval time is about 8ms, and the refresh rate of the simulated star map is increased by more than three times. The real-time requirements of the dynamic star simulator have been improved. Based on this, a star map simulation software is designed, which can realize the division of star catalogs with different fields of view and different magnitude thresholds.

Guide star catalog generation of star sensors for interstellar navigation

In interstellar travel, parallax and stellar aberration have a non-negligible influence on the star identification of star sensors. Through the analysis of the working principle of star sensors, only the guide star catalog generation can solve this problem. The influence mechanism of parallax and stellar aberration is synthetically analyzed by four-dimensional time-space diagrams, and a generation algorithm of the guide star catalog for the interstellar star identification is presented. The update cycle of the catalog is studied, and a block update method based on the distance distribution is given. The Hipparcos catalog is adopted as the fundamental catalog. On the premise of meeting the accuracy requirement of star identification, the maximum travel distance supported by the catalog is about 0.3 light-years. In order to balance the star identification and the influence of parallax, eliminating 30% to 40% of the nearby guide stars is an optimal criterion for the guide star selection. The analysis of the catalog distribution shows that the parallax and the stellar aberration have opposite effects on the distribution of guide stars. The methods and conclusions provide a theoretical basis for interstellar celestial navigation.

Stellar Instrument Magnitude Estimation in Infinite-Dimensional Space

The guide star catalog generation is based on the estimation of stellar instrument magnitudes and the performance and the reliability of star identification algorithms can also be improved by this estimation. This task is critical for the design of advanced star sensors. In this study, a novel method for estimating stellar instrument magnitudes is presented. In this method, the functions of the star illuminance and spectral responsivity of detectors are considered infinite-dimensional vectors in Hilbert space, where the model between star illuminance and magnitudes in each band is established. All feasible solutions for star illuminance that meet the corresponding magnitudes in a band are considered curves in infinite-dimensional space. Accordingly, the solution for star illuminance as the intersection of curves originating from magnitudes in multiple bands can be calculated by solving the corresponding redundant equations. Then, stellar instrument magnitudes can be obtained. An optimization algorithm with limited step lengths and directions that can accurately and quickly solve the problem is proposed and applied to effectively solve the aforementioned equations. An error model is also established in infinite-dimensional space to minimize estimation error, and an optimal band selection rule used in the redundant equations is provided. A simulation experiment is conducted, and its RMSE is 11% lower than the best existing approach. In addition, the night sky experiment performed in this study demonstrates that the proposed method cannot only guarantee accuracy but can also adapt to a wide spectral range.

Optimization of guide star catalog for daytime star sensors

Daytime star sensor provides accuracy navigation information to air vehicles near the ground in the daytime by observing stars. It has been an important development of modern star sensors. In order to achieve a high signal-to-noise ratio, daytime star sensors work in the infrared band to avoid interferences from sky background. Daytime star sensors output accurate attitudes by identifying the observed stars in the field of view (FOV) according to the loaded guide star catalog. Guide stars are usually required to be distributed uniformly on the celestial sphere to improve the performance of star pattern identification. The parameters including limiting magnitude and FOV are determined by processing the 2MASS star catalog as the original star data and performing star distribution statistics. After constellation features are discussed, the idea of distributing stars in the local FOV to constellations is put forward by using the star pair angular separations. An optimization algorithm to build the guide star catalog for daytime stars is proposed to achieve evenly distributed guide stars. The guide star catalog is established and analyzed, proving that the proposed algorithm has simple calculation and easy realization. The Boltzmann entropy of obtained guide star catalog drops two orders of magnitude. Guide stars are distributed more uniformly.

Characteristics study and robustness comparison of various star pattern recognition algorithm for star sensor using digital simulation

The last few years the use of star sensor, which is the most accurate sensor on satellites, began to penetrate into micro satellites and nano satellites, previously used only on high-end satellites. Hence we need to develop star sensor according to the specific needs of microsatellite and nano satellites. One of star sensor’s aspect is star pattern recognition algorithm. A good star pattern recognition algorithm must have a high level of matching success when there are a lot of disturbances (robust) and fast processing time. There are different types of star pattern recognition algorithms, which have their respective advantages and disadvantages, so it is necessary to characterize and compare the various algorithms. In this paper, the characterization and comparison of robustness in different types of star pattern recognition algorithm are done using digital simulation. The simulation results show that the success rate does not always rise with the number of stars used for matching because it depends on how the algorithm works. In addition, the more the number of stars are used, the longer processing time, and vice versa. The best algorithm in terms of success rate is an algorithm that uses a combination pattern of all possible triangles, in other words most robust, but has the longest processing time. This long processing time can be overcome by optimizing GSC (Guide Star Catalogue). From the results the multitriangles 2 algorithm has the best success rate compared to the other algorithms, with success rate for 5, 6, and 7 stars configuration are 97.8%, 98.7%, and 99.8% respectively.

Guide Star Selection for the Three-FOV Daytime Star Sensor.

To realize the application of the star sensor in the all-day carrier platform, a three-field-of-view (three-FOV) star sensor in short-wave infrared (SWIR) band is considered. This new prototype employs new techniques that can improve the detection capability of the star sensor, when the huge size of star identification feature database becomes a big obstacle. Hence, a way to thin the guide star catalog for three-FOV daytime star sensor is studied. Firstly, an introduction of three-FOV star sensor and an example of three-FOV daytime star sensor with narrow FOV are presented. According to this model and the requirement of triangular star identification method, two constraints based on the number and the brightness of the stars in FOV are put forward for guide star selection. Then on the basis of these constraints, the improved spherical spiral method (ISSM) is proposed and the optimal number of reference points of ISSM is discussed. Finally, to demonstrate the performance of the ISSM, guide star catalogs are generated by ISSM, magnitude filter method (MFM), 1st order self-organizing guide star selection method (1st-SOPM) and the spherical spiral method (SSM), respectively. The results show that the guide star catalog generated by ISSM has the smallest size and the number and brightness characteristics of its guide stars are better than the other methods. ISSM is effective for the guide star selection in the three-FOV daytime star sensor.

Novel guide star optimal selection algorithm for star sensors based on star clustering

Modern space vehicles face the challenges to obtain more and more accurate attitudes in order to complete the demanding tasks. Onboard star sensors which identify the observed stars in the field of view according to the loaded guide star catalog and output accurate attitude have attracted most interests. Guide stars are usually required to distribute uniformly on the celestial sphere to improve the performance of the star pattern identification. An optimal selection algorithm is proposed to achieve an even distribution of guide stars in this paper. Constellation features are discussed. The mean shift algorithm is analyzed. The idea that distributes stars in the local field of view to constellations is proposed by using the star pair angular separations according to the star positions in the inertial coordinate system. The optimal selection algorithm of guide stars based on star clustering is developed. Its detailed implement procedures are introduced completely. The guide star optimal selection experiment in visible band by using SAO star catalog as the original star data is implemented. It proves that the proposed algorithm has the virtue of simple calculation and easy realization. The obtained guide star distribution is superior to the regression selection algorithm and the magnitude weighted method.

Guide star catalog generation for short-wave infrared (SWIR) All-Time star sensor.

As an important part of All-Time star sensor design, the generation of the short-wave infrared (SWIR) guide star catalog is crucial to the system performance. The generation process needs estimation of the instrument magnitude and the guide star selection. Different from the commonly used star sensors, since the SWIR band is far away from the visual band and the detectable magnitude limit of the All-Time star sensor is dynamically changing as the observation conditions vary, the current methods of estimating the instrument magnitude cannot be directly applied and the catalog obtained through the current reduction methods that mainly aimed at improving the distribution uniformity cannot ensure enough stars measured in the field of view under strong sky background radiation. To solve the problems, we propose a method for guide star catalog generation for the All-Time star sensor. First, through the specific analysis of the spectral response curves of the SWIR detector and 2MASS detection bandpasses, the method of estimating instrument magnitudes for the All-Time sensor is determined. Subsequently, dynamic detectable magnitude limits are determined through the signal-to-noise model and the atmospheric background radiation intensity analysis. Based on the dynamic detectable magnitude limits, a reduction method is proposed. The simulation experiment results indicate that the RMS error of the estimation of instrument magnitude is 0.075. Compared to the magnitude filtering method, the guide star catalog obtained through our method can guarantee the completeness, besides, the global distribution uniformity increases by 2.2 times and the local distribution uniformity increases by 10.7 times.

Generation of Guide Star Catalog for Star Trackers

In this paper, a novel guide star catalog generation algorithm is presented, which is a crucial part of an advanced star tracker design since the performance and reliability of star identification and attitude determination depend on the guide star selection. First, we propose an analytical method to estimate the stellar instrument magnitude and characterize the associated errors. Those stars whose instrument magnitudes are higher than the star tracker sensitivity constitute the original catalog. Then, the probability model of each star in the original catalog for attitude determination is established as the function of the field of view (FOV), the brightness accuracy, and the mean number of stars in FOV based on the brightness and uniformity principles, and the stars with larger probability are selected as guide stars. The accuracy of photometry method is validated by estimating the Hipparcos catalog instrument magnitude and comparing them with actual data. The efficiency of guide star selection algorithm is demonstrated by generating guide star catalog for a star tracker with a $16 {^{\circ }}\times 16 {^{\circ }}$ FOV.

Optimisation of JWST operations with the help of Gaia

AbstractThe James Webb Space Telescope (JWST) is scheduled for launch in 2018. To operate and observe efficiently, JWST will rely on various external astrometric and photometric catalogues, in particular the HST Guide Star Catalog (GSC), for instance to locate sources accurately on the sky. The incorporation of the Gaia astrometric catalog will improve the absolute astrometry of the GSC and is therefore relevant for JWST operations. We outline how the JWST Science and Operations Center hosted at the Space Telescope Science Institute (STScI) intends to use the Gaia survey results to improve upon operational aspects such as the guiding and the geometric focal plane characterisation of JWST.

Construction and evaluation of 16 mv fine guide star catalog

Construction and evaluation of 16 mv fine guide star catalog

A Road Map for the Generation of a Near-Infrared Guide Star Catalog for Thirty Meter Telescope Observations

The near-infrared instruments in the upcoming Thirty Meter Telescope (TMT) will be assisted by a multi conjugate Adaptive Optics (AO) system. For the efficient operation of the AO system, during observations, a near-infrared guide star catalog which goes as faint as 22 mag in JVega band is essential and such a catalog does not exist. A methodology, based on stellar atmospheric models, to compute the expected near-infrared magnitudes of stellar sources from their optical magnitudes is developed. The method is applied and validated in JHKs bands for a magnitude range of JVega 16–22 mag. The methodology is also applied and validated using the reference catalog of PAN STARRS. We verified that the properties of the final PAN STARRS optical catalog will satisfy the requirements of TMT IRGSC and will be one of the potential sources for the generation of the final catalog. In a broader context, this methodology is applicable for the generation of a guide star catalog for any existing/upcoming near-infrared telescopes.

ABSOLUTE PROPER MOTIONS OUTSIDE THE PLANE (APOP)—A STEP TOWARD THE GSC2.4

We present a new catalog of absolute proper motions and updated positions derived from the same Space Telescope Science Institute digitized Schmidt survey plates utilized for the construction of Guide Star Catalog II. As special attention was devoted to the absolutization process and the removal of position, magnitude, and color dependent systematic errors through the use of both stars and galaxies, this release is solely based on plate data outside the galactic plane, i.e., vertical bar b vertical bar >= 27 degrees. The resulting global zero point error is less than 0.6 mas yr(-1), and the precision is better than 4.0 mas yr(-1) for objects brighter than R-F = 18.5, rising to 9.0 mas yr(-1) for objects with magnitudes in the range 18.5 < R-F < 20.0. The catalog covers 22,525 square degrees and lists 100,774,153 objects to the limiting magnitude of R-F similar to 20.8. Alignment with the International Celestial Reference System was made using 1288 objects common to the second realization of the International Celestial Reference Frame (ICRF2) at radio wavelengths. As a result, the coordinate axes realized by our astrometric data are believed to be aligned with the extragalactic radio frame to within +/- 0.2 mas at the reference epoch J2000.0. This makes our compilation one of the deepest and densest ICRF-registered astrometric catalogs outside the galactic plane. Although the Gaia mission is poised to set the new standard in catalog astronomy and will in many ways supersede this catalog, the methods and procedures reported here will prove useful to remove astrometric magnitude-and color-dependent systematic errors from the next generation of ground-based surveys reaching significantly deeper than the Gaia catalog.