This paper proposes a novel self-calibration method for a large-FoV(Field-of-View)camera using a real star image.First,based on the classic equisolid-angle projection model and polynomial distortion model,the inclinat...This paper proposes a novel self-calibration method for a large-FoV(Field-of-View)camera using a real star image.First,based on the classic equisolid-angle projection model and polynomial distortion model,the inclination of the optical axis is thoroughly considered with respect to the image plane,and a rigorous imaging model including 8 unknown intrinsic parameters is built.Second,the basic calibration equation based on star vector observations is presented.Third,the partial derivative expressions of all 11 camera parameters for linearizing the calibration equation are deduced in detail,and an iterative solution using the least squares method is given.Furtherly,simulation experiment is designed,results of which shows the new model has a better performance than the old model.At last,three experiments were conducted at night in central China and 671 valid star images were collected.The results indicate that the new method obtains a mean magnitude of reprojection error of 0.251 pixels at a 120°FoV,which improves the calibration accuracy by 38.6%compared with the old calibration model(not considering the inclination of the optical axis).When the FoV drops below 20°,the mean magnitude of the reprojection error decreases to 0.15 pixels for both the new model and the old model.Since stars instead of manual control points are used,the new method can realize self-calibration,which might be significant for the long-duration navigation of vehicles in some unfamiliar or extreme environments,such as those of Mars or Earth’s moon.展开更多
Daytime star images captured by dedicated near-space star sensors are characterized by short exposures,high noise,and low Signal-to-Noise Ratios(SNRs).Such imaging is also affected by atmospheric turbulence,causing op...Daytime star images captured by dedicated near-space star sensors are characterized by short exposures,high noise,and low Signal-to-Noise Ratios(SNRs).Such imaging is also affected by atmospheric turbulence,causing optical phenomena,such as scintillation,distortion,and jitter.This causes difficulty in recording high-precision star images during the daytime.This study proposes an adaptive star point extraction method based on dynamically predicting stars'positions.First,it predicts the approximate position of stars based on the star catalog,sensor attitude,observation time,and other information,improving the extraction accuracy.Second,it employs a regional SNR sorting method that adaptively selects star images with higher SNRs,suppressing the scintillation effect and enhancing the SNR of star images.Third,depending on the star's motion trajectory characteristics on the image plane,it utilizes the centroid smoothing method for extraction,thus overcoming the impact of star drift.Field experiments demonstrate that the proposed method can effectively overcome star scintillation,drift,and irregular imaging caused by atmospheric turbulence,achieving a 100%success rate.Moreover,the extraction accuracy improves by more than 80%compared to traditional adaptive methods,attaining a value of 0.05 pixels(0.5"),thereby meeting the requirements of daytime astronomical attitude determination and positioning.展开更多
Taking a large number of images,the Cassini Imaging Science Subsystem(ISS)has been routinely used in astrometry.In ISS images,disk-resolved objects often lead to false detection of stars that disturb the camera pointi...Taking a large number of images,the Cassini Imaging Science Subsystem(ISS)has been routinely used in astrometry.In ISS images,disk-resolved objects often lead to false detection of stars that disturb the camera pointing correction.The aim of this study was to develop an automated processing method to remove the false image stars in disk-resolved objects in ISS images.The method included the following steps:extracting edges,segmenting boundary arcs,fitting circles and excluding false image stars.The proposed method was tested using 200 ISS images.Preliminary experimental results show that it can remove the false image stars in more than 95%of ISS images with disk-resolved objects in a fully automatic manner,i.e.,outperforming the traditional circle detection based on Circular Hough Transform(CHT)by 17%.In addition,its speed is more than twice as fast as that of the CHT method.It is also more robust(no manual parameter tuning is needed)when compared with CHT.The proposed method was also applied to a set of ISS images of Rhea to eliminate the mismatch in pointing correction in automatic procedure.Experiment results showed that the precision of final astrometry results can be improve by roughly 2 times that of automatic procedure without the method.It proved that the proposed method is helpful in the astrometry of ISS images in a fully automatic manner.展开更多
The scientific satellite SST (Space Solar Telescope) is an important research project strongly supported by the Chinese Academy of Sciences. Every day, SST acquires 50 GB of data (after processing) but only 10GB can b...The scientific satellite SST (Space Solar Telescope) is an important research project strongly supported by the Chinese Academy of Sciences. Every day, SST acquires 50 GB of data (after processing) but only 10GB can be transmitted to the ground because of limited time of satellite passage and limited channel volume. Therefore, the data must be compressed before transmission. Wavelets analysis is a new technique developed over the last 10 years, with great potential of application. We start with a brief introduction to the essential principles of wavelet analysis, and then describe the main idea of embedded zerotree wavelet coding, used for compressing the SST images. The results show that this coding is adequate for the job.展开更多
By employing the previous Voronoi approach and replacing its nearest neighbor approx- imation with Drizzle in iterative signal extraction, we develop a fast iterative Drizzle algorithm, namedfiDrizzle, to reconstruct ...By employing the previous Voronoi approach and replacing its nearest neighbor approx- imation with Drizzle in iterative signal extraction, we develop a fast iterative Drizzle algorithm, namedfiDrizzle, to reconstruct the underlying band-limited image from undersampled dithered frames. Compared with the existing iDrizzle, the new algorithm improves rate of convergence and accelerates the computational speed. Moreover, under the same conditions （e.g. the same number of dithers and iterations）, fiDrizzle can make a better quality reconstruction than iDrizzle, due to the newly discov- ered High Sampling caused Decelerating Convergence （HSDC） effect in the iterative signal extraction process.fiDrizzle demonstrates its powerful ability to perform image deconvolution from undersampled dithers.展开更多
Obtaining high precision is an important consideration for astrometric studies using images from the Narrow Angle Camera(NAC)of the Cassini Imaging Science Subsystem(ISS).Selecting the best centering algorithm is key ...Obtaining high precision is an important consideration for astrometric studies using images from the Narrow Angle Camera(NAC)of the Cassini Imaging Science Subsystem(ISS).Selecting the best centering algorithm is key to enhancing astrometric accuracy.In this study,we compared the accuracy of five centering algorithms:Gaussian fitting,the modified moments method,and three point-spread function(PSF)fitting methods(effective PSF(ePSF),PSFEx,and extended PSF(x PSF)from the Cassini Imaging Central Laboratory for Operations(CICLOPS)).We assessed these algorithms using 70 ISS NAC star field images taken with CL1 and CL2 filters across different stellar magnitudes.The ePSF method consistently demonstrated the highest accuracy,achieving precision below 0.03 pixels for stars of magnitude 8-9.Compared to the previously considered best,the modified moments method,the e PSF method improved overall accuracy by about 10%and 21%in the sample and line directions,respectively.Surprisingly,the xPSF model provided by CICLOPS had lower precision than the ePSF.Conversely,the ePSF exhibits an improvement in measurement precision of 23%and 17%in the sample and line directions,respectively,over the xPSF.This discrepancy might be attributed to the xPSF focusing on photometry rather than astrometry.These findings highlight the necessity of constructing PSF models specifically tailored for astrometric purposes in NAC images and provide guidance for enhancing astrometric measurements using these ISS NAC images.展开更多
A new method of light curve inversion with bipartite regularization(LIBR),which is complementary to the previous treatments by Bonomo and Lanza and Estrela and Valio,is used to reconstruct the physical properties of s...A new method of light curve inversion with bipartite regularization(LIBR),which is complementary to the previous treatments by Bonomo and Lanza and Estrela and Valio,is used to reconstruct the physical properties of star spots on the solar-type star Kepler-17 by using the full Q1-Q17 data set.The Markov Chain Monte Carlo(MCMC)method was applied to find the best profile of the reconstructed surface.The known value of the rotation inclination of Kepler-17 allows the generation of a star spot model in a sequence of stellar rotation with a period of 12.26 d.Because of the nature of the light curve inversion,the spot model is limited to the equatorial region.We also investigated the starspot lifetimes of Kepler-17 utilizing the MCMC method.Combined with the LIBR inversion results,it was found that the star spots typically last from one to several stellar rotations.From the time evolution of the spot size,a magnetic cycle period of 437 d can be derived.This value is comparatively shorter than the solar cycle which might be a consequence of the younger age(~1.78 Gyr)of Kepler-17.The light curve of Kepler-17 is characterized by the presence of large-amplitude variation caused by star spots but no superflare activity.An interesting possibility is that the magnetic energy stored in the star spot regions could have been constantly dissipated by electrodynamic interaction between the central star and the hot Jupiter,Kepler-17 b,via a lower-level energy release process.展开更多
Most existing star-galaxy classifiers depend on the reduced information from catalogs,necessitating careful data processing and feature extraction.In this study,we employ a supervised machine learning method(GoogLeNet...Most existing star-galaxy classifiers depend on the reduced information from catalogs,necessitating careful data processing and feature extraction.In this study,we employ a supervised machine learning method(GoogLeNet)to automatically classify stars and galaxies in the COSMOS field.Unlike traditional machine learning methods,we introduce several preprocessing techniques,including noise reduction and the unwrapping of denoised images in polar coordinates,applied to our carefully selected samples of stars and galaxies.By dividing the selected samples into training and validation sets in an 8:2 ratio,we evaluate the performance of the GoogLeNet model in distinguishing between stars and galaxies.The results indicate that the GoogLeNet model is highly effective,achieving accuracies of 99.6% and 99.9% for stars and galaxies,respectively.Furthermore,by comparing the results with and without preprocessing,we find that preprocessing can significantly improve classification accuracy(by approximately 2.0% to 6.0%)when the images are rotated.In preparation for the future launch of the China Space Station Telescope(CSST),we also evaluate the performance of the GoogLeNet model on the CSST simulation data.These results demonstrate a high level of accuracy(approximately 99.8%),indicating that this model can be effectively utilized for future observations with the CSST.展开更多
The wide field of the Schmidt telescope implies a greater chance of the field containing bright objects, and the presence of a corrector lens produces a certain type of ghost images. We summarize and confirm the featu...The wide field of the Schmidt telescope implies a greater chance of the field containing bright objects, and the presence of a corrector lens produces a certain type of ghost images. We summarize and confirm the features of such ghost images in Schmidt CCD photometry. The ghost images could be star-like under special observational conditions. The zenith distance of the telescope, among other factors, is found to correlate with different patterns of the ghost images. Some relevant issues are discussed and possible applications of our results are suggested.展开更多
Accessing local dynamics within a single macromolecule is the key to understand the physical origin of the viscoelasticity and especially the glass transition. In order to extract specific information on the dynamics ...Accessing local dynamics within a single macromolecule is the key to understand the physical origin of the viscoelasticity and especially the glass transition. In order to extract specific information on the dynamics of the branch point of a star polymer around its glass transition temperature, four-arm star poly （n-butyl methacrylate） with a fluorescent core was synthesized using perylene diimide as initiator and polymerization conducted via atom transfer radical polymerization. The process is found to be effective in positioning the fluorophore at the branch point with the fluorophore intact, which allows the successful application of single molecule fluorescence defocus imaging in examining the local site- sensitive dynamics. The power spectra of rotation trajectories, the population of rotating fluorophores as well as the distribution of angular displacement were used to revel the difference in local dynamics between branch point and the arm＇s end. It is discovered that the local dynamics at the core of the star polymer is much less activated than that at the arm＇s end. The results demonstrate the strong effect dues to the topological constrain at the branch point and the more free space at the arm＇s end.展开更多
Stellar classification is a fundamental task in astronomical data analysis.Photometric data offer a significant advantage over spectral data in terms of data volume.Their lower acquisition cost and broader coverage ma...Stellar classification is a fundamental task in astronomical data analysis.Photometric data offer a significant advantage over spectral data in terms of data volume.Their lower acquisition cost and broader coverage make them more suitable for stellar classification applications.This study selects photometric data from the SDSS DR18.Instead of using traditional RGB image formats,a series of preprocessing steps were applied to generate five-channel Numpy files as the data set.To enhance stellar classification performance,we propose a deep learning model based on photometric feature fusion–Stellar Photometric Features Fusion Network.Additionally,we introduce the Dynamic Enhanced Stellar Squeeze-and-Excitation module,designed to optimize the weight allocation of different photometric bands in the classification task,and investigate the impact of each band's features on classification performance.Ultimately,we found that the information from the r and z bands played a more crucial role in the stellar classification task,achieving a final classification accuracy of 87.47%,thereby demonstrating the effectiveness of photometric data in stellar classification.展开更多
The portable adaptive optics(PAO)device is a low-cost and compact system,designed for 4-meter class telescopes that have no adaptive optics(AO)system,because of the physical space limitation at the Nasmyth or Cassegra...The portable adaptive optics(PAO)device is a low-cost and compact system,designed for 4-meter class telescopes that have no adaptive optics(AO)system,because of the physical space limitation at the Nasmyth or Cassegrain focus and the historically high cost of conventional AO.The initial scientific observations of the PAO are focused on the direct imaging of exoplanets and sub-stellar companions.This paper discusses the concept of PAO and the associated high-contrast imaging performance in our recent observational runs.PAO deliver a Strehl ratio better than 60%in H band under median seeing conditions of 1".Combined with our dedicated image rotation and subtraction(IRS)technique and the optimized IRS(O-IRS)algorithm,the averaged contrast ratio for a 5≤V_(mag)≤9 primary star is 1.3×10^(-5)and3.3×10^(-6)at angular distance of 0.36"with exposure time of 7 minutes and 2 hours,respectively.PAO has successfully revealed the known exoplanet ofκAnd b in our recent observation with the 3.5-meter ARC telescope at Apache Point Observatory.We have performed the associated astrometry and photometry analysis of the recoveredκAnd b planet,which gives a projected separation of 0.98"±0.05",a position angle of 51.1°±0.5°and a mass of 10.15_(-1.255)^(+2.19) MJup.These results demonstrate that PAO can be used for direct imaging of exoplanets with medium-sized telescopes.展开更多
Here,we study the temperature structure of flaring and non-flaring coronal loops,using extracted loops from images taken in six extreme ultraviolet channels recorded by Atmospheric Imaging Assembly/Solar Dynamics Obse...Here,we study the temperature structure of flaring and non-flaring coronal loops,using extracted loops from images taken in six extreme ultraviolet channels recorded by Atmospheric Imaging Assembly/Solar Dynamics Observatory.We use data for loops of an X2.1-class-flaring active region(AR 11283)during 22:10 UT until 23:00 UT,on 2011 September 6;and a non-flaring active region(AR 12194)during 08:00:00 UT until 09:00:00 UT on2014 October 26.By using the spatially synthesized Gaussian differential emission measure(DEM)forward-fitting method,we calculate the peak temperatures for each strip of the loops.We apply the Lomb–Scargle method to compute the oscillation periods for the temperature series of each strip.The periods of the temperature oscillations for the flaring loops ranged from 7 to 28.4 minutes.These temperature oscillations show very close behavior to the slow-mode oscillation.We observe that the temperature oscillations in the flaring loops started at least around10 minutes before the transverse oscillations and continue for a long time duration even after the transverse oscillations ended.The temperature amplitudes increased during the flaring time(20 minutes)in the flaring loops.The periods of the temperatures obtained for the non-flaring loops ranged from 8.5 to 30 minutes,but their significances are less(below 0.5)in comparison with the flaring ones(near to one).Hence the detected temperature periods for the non-flaring loops’strips are less probable in comparison with the flaring ones,and maybe they are just fluctuations.Based on our confined observations,it seems that the flaring loops’periods show more diversity and their temperatures have wider ranges of variation than the non-flaring ones.More accurate commentary in this respect requires more extensive statistical research and broader observations.展开更多
Optical telescopes are an important tool for acquiring optical information about distant objects,and resolution is an important indicator that measures the ability to observe object details.However,due to the effects ...Optical telescopes are an important tool for acquiring optical information about distant objects,and resolution is an important indicator that measures the ability to observe object details.However,due to the effects of system aberration,atmospheric seeing,and other factors,the observed image of ground-based telescopes is often degraded,resulting in reduced resolution.This paper proposes an optical-neural network joint optimization method to improve the resolution of the observed image by co-optimizing the point-spread function(PSF)of the telescopic system and the image super-resolution(SR)network.To improve the speed of image reconstruction,we designed a generative adversarial net(LCR-GAN)with light parameters,which is much faster than the latest unsupervised networks.To reconstruct the PSF trained by the network in the optical path,a phase mask is introduced.It improves the image reconstruction effect of LCR-GAN by reconstructing the PSF that best matches the network.The results of simulation and verification experiments show that compared with the pure deep learning method,the SR image reconstructed by this method is rich in detail and it is easier to distinguish stars or stripes.展开更多
Subpixel centroid estimation is the most important star image location method of star tracker. This paper presents a theoretical analysis of the systematic error of subpixel centroid estimation algorithm utilizing fre...Subpixel centroid estimation is the most important star image location method of star tracker. This paper presents a theoretical analysis of the systematic error of subpixel centroid estimation algorithm utilizing frequency domain analysis under the con-sideration of sampling frequency limitation and sampling window limitation. Explicit expression of systematic error of cen-troid estimation is obtained, and the dependence of systematic error on Gaussian width of star image, actual star centroid loca-tion and the number of sampling pixels is derived. A systematic error compensation algorithm for star centroid estimation is proposed based on the result of theoretical analysis. Simulation results show that after compensation, the residual systematic errors of 3-pixel-and 5-pixel-windows’ centroid estimation are less than 2×10-3 pixels and 2×10-4 pixels respectively.展开更多
基金co-supported by the National Natural Science Foundation of China(Nos.42074013 and 41704006)。
文摘This paper proposes a novel self-calibration method for a large-FoV(Field-of-View)camera using a real star image.First,based on the classic equisolid-angle projection model and polynomial distortion model,the inclination of the optical axis is thoroughly considered with respect to the image plane,and a rigorous imaging model including 8 unknown intrinsic parameters is built.Second,the basic calibration equation based on star vector observations is presented.Third,the partial derivative expressions of all 11 camera parameters for linearizing the calibration equation are deduced in detail,and an iterative solution using the least squares method is given.Furtherly,simulation experiment is designed,results of which shows the new model has a better performance than the old model.At last,three experiments were conducted at night in central China and 671 valid star images were collected.The results indicate that the new method obtains a mean magnitude of reprojection error of 0.251 pixels at a 120°FoV,which improves the calibration accuracy by 38.6%compared with the old calibration model(not considering the inclination of the optical axis).When the FoV drops below 20°,the mean magnitude of the reprojection error decreases to 0.15 pixels for both the new model and the old model.Since stars instead of manual control points are used,the new method can realize self-calibration,which might be significant for the long-duration navigation of vehicles in some unfamiliar or extreme environments,such as those of Mars or Earth’s moon.
基金funded by the National Natural Science Foundation of China(Nos.42374011,42074013)through the Natural Science Foundation’s Outstanding Youth Fund Program of Henan Province,China(Nos.242300421150,242300421151)。
文摘Daytime star images captured by dedicated near-space star sensors are characterized by short exposures,high noise,and low Signal-to-Noise Ratios(SNRs).Such imaging is also affected by atmospheric turbulence,causing optical phenomena,such as scintillation,distortion,and jitter.This causes difficulty in recording high-precision star images during the daytime.This study proposes an adaptive star point extraction method based on dynamically predicting stars'positions.First,it predicts the approximate position of stars based on the star catalog,sensor attitude,observation time,and other information,improving the extraction accuracy.Second,it employs a regional SNR sorting method that adaptively selects star images with higher SNRs,suppressing the scintillation effect and enhancing the SNR of star images.Third,depending on the star's motion trajectory characteristics on the image plane,it utilizes the centroid smoothing method for extraction,thus overcoming the impact of star drift.Field experiments demonstrate that the proposed method can effectively overcome star scintillation,drift,and irregular imaging caused by atmospheric turbulence,achieving a 100%success rate.Moreover,the extraction accuracy improves by more than 80%compared to traditional adaptive methods,attaining a value of 0.05 pixels(0.5"),thereby meeting the requirements of daytime astronomical attitude determination and positioning.
基金supported by the National Natural Science Foundation of China(Grant Nos.11873026 and U1431227)the Natural Science Foundation of Guangdong Province,China(Grant No.2016A030313092)+1 种基金the National Key Research and Development Project of China(Grant No.2019YFC0120102)the Fundamental Research Funds for the Central Universities(Grant No.21619413)。
文摘Taking a large number of images,the Cassini Imaging Science Subsystem(ISS)has been routinely used in astrometry.In ISS images,disk-resolved objects often lead to false detection of stars that disturb the camera pointing correction.The aim of this study was to develop an automated processing method to remove the false image stars in disk-resolved objects in ISS images.The method included the following steps:extracting edges,segmenting boundary arcs,fitting circles and excluding false image stars.The proposed method was tested using 200 ISS images.Preliminary experimental results show that it can remove the false image stars in more than 95%of ISS images with disk-resolved objects in a fully automatic manner,i.e.,outperforming the traditional circle detection based on Circular Hough Transform(CHT)by 17%.In addition,its speed is more than twice as fast as that of the CHT method.It is also more robust(no manual parameter tuning is needed)when compared with CHT.The proposed method was also applied to a set of ISS images of Rhea to eliminate the mismatch in pointing correction in automatic procedure.Experiment results showed that the precision of final astrometry results can be improve by roughly 2 times that of automatic procedure without the method.It proved that the proposed method is helpful in the astrometry of ISS images in a fully automatic manner.
基金supported by the National 863 Foundation under grant 863-2.5.1.25.
文摘The scientific satellite SST (Space Solar Telescope) is an important research project strongly supported by the Chinese Academy of Sciences. Every day, SST acquires 50 GB of data (after processing) but only 10GB can be transmitted to the ground because of limited time of satellite passage and limited channel volume. Therefore, the data must be compressed before transmission. Wavelets analysis is a new technique developed over the last 10 years, with great potential of application. We start with a brief introduction to the essential principles of wavelet analysis, and then describe the main idea of embedded zerotree wavelet coding, used for compressing the SST images. The results show that this coding is adequate for the job.
基金supported by the National Basic Research Program of China (973 program, Nos. 2015CB857000 and 2013CB834900)the Foundation for Distinguished Young Scholars of Jiangsu Province (No. BK20140050)+1 种基金the ‘Strategic Priority Research Program the Emergence of Cosmological Structure’ of the CAS (No. XDB09010000)the National Natural Science Foundation of China (Nos. 11333008, 11233005, 11273061 and 11673065)
文摘By employing the previous Voronoi approach and replacing its nearest neighbor approx- imation with Drizzle in iterative signal extraction, we develop a fast iterative Drizzle algorithm, namedfiDrizzle, to reconstruct the underlying band-limited image from undersampled dithered frames. Compared with the existing iDrizzle, the new algorithm improves rate of convergence and accelerates the computational speed. Moreover, under the same conditions （e.g. the same number of dithers and iterations）, fiDrizzle can make a better quality reconstruction than iDrizzle, due to the newly discov- ered High Sampling caused Decelerating Convergence （HSDC） effect in the iterative signal extraction process.fiDrizzle demonstrates its powerful ability to perform image deconvolution from undersampled dithers.
基金supported by the National Natural Science Foundation of China(No.12373073,U2031104,No.12173015)Guangdong Basic and Applied Basic Research Foundation(No.2023A1515011340)。
文摘Obtaining high precision is an important consideration for astrometric studies using images from the Narrow Angle Camera(NAC)of the Cassini Imaging Science Subsystem(ISS).Selecting the best centering algorithm is key to enhancing astrometric accuracy.In this study,we compared the accuracy of five centering algorithms:Gaussian fitting,the modified moments method,and three point-spread function(PSF)fitting methods(effective PSF(ePSF),PSFEx,and extended PSF(x PSF)from the Cassini Imaging Central Laboratory for Operations(CICLOPS)).We assessed these algorithms using 70 ISS NAC star field images taken with CL1 and CL2 filters across different stellar magnitudes.The ePSF method consistently demonstrated the highest accuracy,achieving precision below 0.03 pixels for stars of magnitude 8-9.Compared to the previously considered best,the modified moments method,the e PSF method improved overall accuracy by about 10%and 21%in the sample and line directions,respectively.Surprisingly,the xPSF model provided by CICLOPS had lower precision than the ePSF.Conversely,the ePSF exhibits an improvement in measurement precision of 23%and 17%in the sample and line directions,respectively,over the xPSF.This discrepancy might be attributed to the xPSF focusing on photometry rather than astrometry.These findings highlight the necessity of constructing PSF models specifically tailored for astrometric purposes in NAC images and provide guidance for enhancing astrometric measurements using these ISS NAC images.
基金financially funded by The Science and Technology Development Fund,Macao SAR(061/2017/A2,0007/2019/A)the Faculty Research Grants of Macao University of Science and Technology(project code:FRG-19-004-SSI)。
文摘A new method of light curve inversion with bipartite regularization(LIBR),which is complementary to the previous treatments by Bonomo and Lanza and Estrela and Valio,is used to reconstruct the physical properties of star spots on the solar-type star Kepler-17 by using the full Q1-Q17 data set.The Markov Chain Monte Carlo(MCMC)method was applied to find the best profile of the reconstructed surface.The known value of the rotation inclination of Kepler-17 allows the generation of a star spot model in a sequence of stellar rotation with a period of 12.26 d.Because of the nature of the light curve inversion,the spot model is limited to the equatorial region.We also investigated the starspot lifetimes of Kepler-17 utilizing the MCMC method.Combined with the LIBR inversion results,it was found that the star spots typically last from one to several stellar rotations.From the time evolution of the spot size,a magnetic cycle period of 437 d can be derived.This value is comparatively shorter than the solar cycle which might be a consequence of the younger age(~1.78 Gyr)of Kepler-17.The light curve of Kepler-17 is characterized by the presence of large-amplitude variation caused by star spots but no superflare activity.An interesting possibility is that the magnetic energy stored in the star spot regions could have been constantly dissipated by electrodynamic interaction between the central star and the hot Jupiter,Kepler-17 b,via a lower-level energy release process.
基金supported by the Strategic Priority Research Program of Chinese Academy of Sciences(grant No.XDB41000000)the National Natural Science Foundation of China(NSFC,Grant Nos.12233008 and 11973038)+2 种基金the China Manned Space Project(No.CMS-CSST-2021-A07)the Cyrus Chun Ying Tang Foundationsthe support from Hong Kong Innovation and Technology Fund through the Research Talent Hub program(GSP028)。
文摘Most existing star-galaxy classifiers depend on the reduced information from catalogs,necessitating careful data processing and feature extraction.In this study,we employ a supervised machine learning method(GoogLeNet)to automatically classify stars and galaxies in the COSMOS field.Unlike traditional machine learning methods,we introduce several preprocessing techniques,including noise reduction and the unwrapping of denoised images in polar coordinates,applied to our carefully selected samples of stars and galaxies.By dividing the selected samples into training and validation sets in an 8:2 ratio,we evaluate the performance of the GoogLeNet model in distinguishing between stars and galaxies.The results indicate that the GoogLeNet model is highly effective,achieving accuracies of 99.6% and 99.9% for stars and galaxies,respectively.Furthermore,by comparing the results with and without preprocessing,we find that preprocessing can significantly improve classification accuracy(by approximately 2.0% to 6.0%)when the images are rotated.In preparation for the future launch of the China Space Station Telescope(CSST),we also evaluate the performance of the GoogLeNet model on the CSST simulation data.These results demonstrate a high level of accuracy(approximately 99.8%),indicating that this model can be effectively utilized for future observations with the CSST.
基金The work is partially supported by the Chinese National Natural Science Foundation under the grant No. 10073012The operation of the NAOC Schmidt telescope is supported by the Chinese Academy of Sciencesthe Chinese National Natural Science Foundation
文摘The wide field of the Schmidt telescope implies a greater chance of the field containing bright objects, and the presence of a corrector lens produces a certain type of ghost images. We summarize and confirm the features of such ghost images in Schmidt CCD photometry. The ghost images could be star-like under special observational conditions. The zenith distance of the telescope, among other factors, is found to correlate with different patterns of the ghost images. Some relevant issues are discussed and possible applications of our results are suggested.
基金supported by National Basic Research Program of China(No. 2014CB643601)
文摘Accessing local dynamics within a single macromolecule is the key to understand the physical origin of the viscoelasticity and especially the glass transition. In order to extract specific information on the dynamics of the branch point of a star polymer around its glass transition temperature, four-arm star poly （n-butyl methacrylate） with a fluorescent core was synthesized using perylene diimide as initiator and polymerization conducted via atom transfer radical polymerization. The process is found to be effective in positioning the fluorophore at the branch point with the fluorophore intact, which allows the successful application of single molecule fluorescence defocus imaging in examining the local site- sensitive dynamics. The power spectra of rotation trajectories, the population of rotating fluorophores as well as the distribution of angular displacement were used to revel the difference in local dynamics between branch point and the arm＇s end. It is discovered that the local dynamics at the core of the star polymer is much less activated than that at the arm＇s end. The results demonstrate the strong effect dues to the topological constrain at the branch point and the more free space at the arm＇s end.
文摘Stellar classification is a fundamental task in astronomical data analysis.Photometric data offer a significant advantage over spectral data in terms of data volume.Their lower acquisition cost and broader coverage make them more suitable for stellar classification applications.This study selects photometric data from the SDSS DR18.Instead of using traditional RGB image formats,a series of preprocessing steps were applied to generate five-channel Numpy files as the data set.To enhance stellar classification performance,we propose a deep learning model based on photometric feature fusion–Stellar Photometric Features Fusion Network.Additionally,we introduce the Dynamic Enhanced Stellar Squeeze-and-Excitation module,designed to optimize the weight allocation of different photometric bands in the classification task,and investigate the impact of each band's features on classification performance.Ultimately,we found that the information from the r and z bands played a more crucial role in the stellar classification task,achieving a final classification accuracy of 87.47%,thereby demonstrating the effectiveness of photometric data in stellar classification.
基金supported by the National Natural Science Foundation of China(Grant Nos.11827804,U2031210)。
文摘The portable adaptive optics(PAO)device is a low-cost and compact system,designed for 4-meter class telescopes that have no adaptive optics(AO)system,because of the physical space limitation at the Nasmyth or Cassegrain focus and the historically high cost of conventional AO.The initial scientific observations of the PAO are focused on the direct imaging of exoplanets and sub-stellar companions.This paper discusses the concept of PAO and the associated high-contrast imaging performance in our recent observational runs.PAO deliver a Strehl ratio better than 60%in H band under median seeing conditions of 1".Combined with our dedicated image rotation and subtraction(IRS)technique and the optimized IRS(O-IRS)algorithm,the averaged contrast ratio for a 5≤V_(mag)≤9 primary star is 1.3×10^(-5)and3.3×10^(-6)at angular distance of 0.36"with exposure time of 7 minutes and 2 hours,respectively.PAO has successfully revealed the known exoplanet ofκAnd b in our recent observation with the 3.5-meter ARC telescope at Apache Point Observatory.We have performed the associated astrometry and photometry analysis of the recoveredκAnd b planet,which gives a projected separation of 0.98"±0.05",a position angle of 51.1°±0.5°and a mass of 10.15_(-1.255)^(+2.19) MJup.These results demonstrate that PAO can be used for direct imaging of exoplanets with medium-sized telescopes.
文摘Here,we study the temperature structure of flaring and non-flaring coronal loops,using extracted loops from images taken in six extreme ultraviolet channels recorded by Atmospheric Imaging Assembly/Solar Dynamics Observatory.We use data for loops of an X2.1-class-flaring active region(AR 11283)during 22:10 UT until 23:00 UT,on 2011 September 6;and a non-flaring active region(AR 12194)during 08:00:00 UT until 09:00:00 UT on2014 October 26.By using the spatially synthesized Gaussian differential emission measure(DEM)forward-fitting method,we calculate the peak temperatures for each strip of the loops.We apply the Lomb–Scargle method to compute the oscillation periods for the temperature series of each strip.The periods of the temperature oscillations for the flaring loops ranged from 7 to 28.4 minutes.These temperature oscillations show very close behavior to the slow-mode oscillation.We observe that the temperature oscillations in the flaring loops started at least around10 minutes before the transverse oscillations and continue for a long time duration even after the transverse oscillations ended.The temperature amplitudes increased during the flaring time(20 minutes)in the flaring loops.The periods of the temperatures obtained for the non-flaring loops ranged from 8.5 to 30 minutes,but their significances are less(below 0.5)in comparison with the flaring ones(near to one).Hence the detected temperature periods for the non-flaring loops’strips are less probable in comparison with the flaring ones,and maybe they are just fluctuations.Based on our confined observations,it seems that the flaring loops’periods show more diversity and their temperatures have wider ranges of variation than the non-flaring ones.More accurate commentary in this respect requires more extensive statistical research and broader observations.
基金Funding is provided by the National Natural Science Foundation of China(NSFC,Grant Nos.62375027 and 62127813)Natural Science Foundation of Chongqing Municipality(CSTB2023NSCQ-MSX0504)+1 种基金Natural Science Foundation of Jilin Provincial(YDZJ202201ZYTS411)Jilin Provincial Education Department Fund of China(JJKH20240920KJ)。
文摘Optical telescopes are an important tool for acquiring optical information about distant objects,and resolution is an important indicator that measures the ability to observe object details.However,due to the effects of system aberration,atmospheric seeing,and other factors,the observed image of ground-based telescopes is often degraded,resulting in reduced resolution.This paper proposes an optical-neural network joint optimization method to improve the resolution of the observed image by co-optimizing the point-spread function(PSF)of the telescopic system and the image super-resolution(SR)network.To improve the speed of image reconstruction,we designed a generative adversarial net(LCR-GAN)with light parameters,which is much faster than the latest unsupervised networks.To reconstruct the PSF trained by the network in the optical path,a phase mask is introduced.It improves the image reconstruction effect of LCR-GAN by reconstructing the PSF that best matches the network.The results of simulation and verification experiments show that compared with the pure deep learning method,the SR image reconstructed by this method is rich in detail and it is easier to distinguish stars or stripes.
文摘Subpixel centroid estimation is the most important star image location method of star tracker. This paper presents a theoretical analysis of the systematic error of subpixel centroid estimation algorithm utilizing frequency domain analysis under the con-sideration of sampling frequency limitation and sampling window limitation. Explicit expression of systematic error of cen-troid estimation is obtained, and the dependence of systematic error on Gaussian width of star image, actual star centroid loca-tion and the number of sampling pixels is derived. A systematic error compensation algorithm for star centroid estimation is proposed based on the result of theoretical analysis. Simulation results show that after compensation, the residual systematic errors of 3-pixel-and 5-pixel-windows’ centroid estimation are less than 2×10-3 pixels and 2×10-4 pixels respectively.
