The Spectral Imaging Corona Graph(SICG) serves as the optical observation equipment of E-corona in the Chinese Meridian Project Phase II, which aims at monitoring the initial source of solar activities. For the purpos...The Spectral Imaging Corona Graph(SICG) serves as the optical observation equipment of E-corona in the Chinese Meridian Project Phase II, which aims at monitoring the initial source of solar activities. For the purpose of indepth exploration and space weather forecast in the full chain of Sun–Earth space, SICG is designed to work at two wavelengths of 637.4 and 530.3 nm in the quasi-simultaneous observation mode. Thus, the photometric calibration is more challenging to guarantee accurate scientific data of SICG. Two solar photometers are specially developed to match the observing wavelengths and make the photoelectronic conversion traceable. Correspondingly, the calibration process selects the solar disk center as the brightness reference, which compensates for the photometric losses along the atmospheric transmission path. This study derives the calibration coefficients from the two photometers for the E-coronal brightness processing in real time. By modeling aerosol absorption and scattering and comparing with continuous flat-field observation, the photometric calibration of SICG is evaluated with deviations of 2.1% and 2.3% at 637.4 nm and 530.3 nm, respectively. Based on this, the evolution speed of a multitemperature coronal loop was analyzed, facilitating further research into the physical mechanisms of coronal mass ejections.展开更多
The standing waves existing in radio telescope data are primarily due to reflections among the instruments,which significantly impact the spectral quality of the Five-hundred-meter Aperture Spherical radio Telescope(F...The standing waves existing in radio telescope data are primarily due to reflections among the instruments,which significantly impact the spectral quality of the Five-hundred-meter Aperture Spherical radio Telescope(FAST).Eliminating these standing waves for FAST is challenging given the constant changes in their phases and amplitudes.Over a ten-second period,the phases shift by 18°while the amplitudes fluctuate by 6 mK.Thus,we developed the fast Fourier transform(FFT)filter method to eliminate these standing waves for every individual spectrum.The FFT filter can decrease the rms from 3.2 to 1.15 times the theoretical estimate.Compared to other methods such as sine fitting and running median,the FFT filter achieves a median rms of approximately 1.2 times the theoretical expectation and the smallest scatter at 12%.Additionally,the FFT filter method avoids the flux loss issue encountered with some other methods.The FFT is also efficient in detecting harmonic radio frequency interference(RFI).In the FAST data,we identified three distinct types of harmonic RFI,each with amplitudes exceeding 100 mK and intrinsic frequency periods of 8.1,0.5,and 0.37 MHz,respectively.The FFT filter,proven as the most effective method,is integrated into the H I data calibration and imaging pipeline for FAST(HiFAST,https://hifast.readthedocs.io).展开更多
To address the issues of low accuracy and high computational complexity in traditional channelization techniques for ultra-wideband signals,this paper proposes a novel rationally oversampled channelization method to e...To address the issues of low accuracy and high computational complexity in traditional channelization techniques for ultra-wideband signals,this paper proposes a novel rationally oversampled channelization method to enhance the accuracy and efficiency of signal processing.The proposed method is evaluated by implementing and comparing critically sampled and integer oversampled channelization algorithms.A detailed analysis of the impact of different oversampling factors and filter orders on performance is provided.The validity of the proposed algorithm is verified using baseband data from pulsar J0437−4715 observed by the Parkes telescope,demonstrating its effectiveness and correctness.展开更多
Near-Earth Asteroids posed a threat to human civilization,making their monitoring crucial.As the demand for asteroid detection technology increased,precise detection of these celestial bodies became an urgent task to ...Near-Earth Asteroids posed a threat to human civilization,making their monitoring crucial.As the demand for asteroid detection technology increased,precise detection of these celestial bodies became an urgent task to understand their characteristics and assess potential impact risks.To improve asteroid detection accuracy and efficiency,we proposed an advanced image processing method and a deep learning network for automatic asteroid detection.Specifically,we aligned star clusters and overlaid images to exploit asteroid motion rates,transforming them into object-like trajectories and improving the signal-to-noise ratio.This approach created the Asteroid Trajectory Image Data set under various conditions.We modified CenterNet2 network to develop AstroCenterNet by integrating Multi-channel Histogram Truncation for feature enhancement,using the SimAM attention mechanism to expand contextual information and suppress noise,and refining Feature Pyramid Network to improve low-level feature detection.Our results demonstrated a detection accuracy of 98.4%,a recall of 97.6%,a mean Average Precision of 94.01%,a false alarm rate of 1.6%,and a processing speed of approximately 17.86 frames per second,indicating that our method achieves high precision and efficiency.展开更多
Aperture photometry is a fundamental technique widely used to obtain high-precision light curves in optical survey projects like Tianyu.However,its effectiveness is limited in crowded fields,and the choice of aperture...Aperture photometry is a fundamental technique widely used to obtain high-precision light curves in optical survey projects like Tianyu.However,its effectiveness is limited in crowded fields,and the choice of aperture size critically impacts photometric precision.To address these challenges,we propose DeepAP,an efficient and accurate two-stage deep learning framework for aperture photometry.Specifically,for a given source,we first train a Vision Transformer(ViT)model to assess its feasibility of aperture photometry.We then train the Residual Neural Network(ResNet)to predict its optimal aperture size.For aperture photometry feasibility assessment,the ViT model yields an ROC AUC value of 0.96,and achieves a precision of 0.974,a recall of 0.930,and an F1 score of 0.952 on the test set.For aperture size prediction,the ResNet model effectively mitigates biases inherent in classical growth curve methods by adaptively selecting apertures appropriate for sources of varying brightness,thereby enhancing the signal-to-noise ratio(SNR)across a wide range of targets.Meanwhile,some samples in the test set have a higher SNR than those obtained by exhaustive aperture size enumeration because of the finer granularity of aperture size estimation.By integrating ResNet with the ViT network,the DeepAP framework achieves a median total processing time of 18 ms for a batch of 10 images,representing a speed-up of approximately 5.9×10^(4) times compared to exhaustive aperture size enumeration.This work paves the way for the automatic application of aperture photometry in future high-precision surveys such as Tianyu and Legacy Survey of Space and Time.The source code and model are available at https://github.com/ruiyicheng/DeepAP.展开更多
Measuring the transverse velocity field in high-resolution solar images is essential for understanding solar dynamics.This paper introduces an innovative unsupervised deep learning optical flow model designed to calcu...Measuring the transverse velocity field in high-resolution solar images is essential for understanding solar dynamics.This paper introduces an innovative unsupervised deep learning optical flow model designed to calculate the transverse velocity field,addressing the challenges of missing optical flow labels and the limited accuracy of velocity field measurements in high-resolution solar images.The proposed method converts the transverse velocity field computation problem into an optical flow computation problem,using two forward propagations of features to get rid of the reliance on optical flow labels.Additionally,it reduces the impact of the“Brightness Consistency”constraint on optical flow accuracy by identifying and handling optical flow outliers.We apply this method to compute the transverse velocity fields of high-resolution solar image sequences from the Hαand TiO bands,observed by the New Vacuum Solar Telescope.Comparative experiments with several wellestablished optical flow methods,including those based on supervised deep learning models,show that our approach outperforms the comparison methods according to key evaluation metrics such as Residual Map Mean,Residual Map Variance,Cross Correlation,and Structural Similarity Index Measure.Moreover,since optical flow captures the fundamental motion information in image sequences,the proposed method can be applied to a variety of research areas,including solar image registration,sequence alignment,image super-resolution,magnetic field calibration,and solar activity forecasting.The code is available at https://github.com/jackie-willianm/Transverse-Velocity-Field-Measurement-of-Solar-High-Resolution-Images.展开更多
The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) has become a crucial resource in astronomical research,offering a vast amount of spectral data for stars,galaxies,and quasars.This paper presents ...The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) has become a crucial resource in astronomical research,offering a vast amount of spectral data for stars,galaxies,and quasars.This paper presents the data processing methods used by LAMOST,focusing on the classification and redshift measurement of large spectral data sets through template matching,as well as the creation of data products.Additionally,this paper details the construction of the Multiple Epoch Catalogs by integrating LAMOST spectral data with photometric data from Gaia and Pan-STARRS,and explains the creation of both low-and medium-resolution data products.展开更多
Certain transients require regular observations over several days at intervals of hours or shorter,which cannot be accomplished by telescopes at a single site.The deployment of globally distributed telescopes at geogr...Certain transients require regular observations over several days at intervals of hours or shorter,which cannot be accomplished by telescopes at a single site.The deployment of globally distributed telescopes at geographic locations of different longitudes enables the periodic monitoring of transients through relay observation.However,the simultaneous relay observation of numerous targets requires a telescope array of multiple telescopes that can be efficiently coordinated,and an automated scheduler for the array.This paper proposes IPROS,an integer programming model relay observation scheduler for a telescope array,that accounts for the entire process of relay observation and is consistent with the practical scenarios.We introduce the integer programming mathematical model for the relay observation scheduling problem with the telescope array,upon which the scheduler is based.Additionally,we propose an algorithm to provide a comprehensive formulation of the optimization objective of minimizing cadence deviation in the model.Experimental results demonstrate that the relay observation scheduler based on the integer programming model can effectively address the telescope array relay observation problem.It shows superiority over a scheduler with non-specific consideration of relay observation in the modeling and a scheduler based on greedy thought.展开更多
Radio interferometry significantly improves the resolution of observed images, and the final result also relies heavily on data recovery. The Cotton-Schwab CLEAN(CS-Clean) deconvolution approach is a widely used recon...Radio interferometry significantly improves the resolution of observed images, and the final result also relies heavily on data recovery. The Cotton-Schwab CLEAN(CS-Clean) deconvolution approach is a widely used reconstruction algorithm in the field of radio synthesis imaging. However, parameter tuning for this algorithm has always been a difficult task. Here, its performance is improved by considering some internal characteristics of the data. From a mathematical point of view, a peak signal-to-noise-based(PSNRbased) method was introduced to optimize the step length of the steepest descent method in the recovery process. We also found that the loop gain curve in the new algorithm is a good indicator of parameter tuning.Tests show that the new algorithm can effectively solve the problem of oscillation for a large fixed loop gain and provides a more robust recovery.展开更多
A Synchronous Photometry Data Extraction(SPDE)program,performing indiscriminate monitoring of all stars appearing in the same field of view of an astronomical image,is developed by integrating several Astropy affiliat...A Synchronous Photometry Data Extraction(SPDE)program,performing indiscriminate monitoring of all stars appearing in the same field of view of an astronomical image,is developed by integrating several Astropy affiliated packages to make full use of time series observed by traditional small/medium aperture ground-based telescopes.The complete full-frame stellar photometry data reductions implemented for the two time series of cataclysmic variables:RX J2102.0+3359 and Paloma J0524+4244 produce 363 and 641 optimal light curves,respectively.A cross-identification with SIMBAD finds 23 known stars,of which 16 are red giant-/horizontal-branch stars,2 W UMa-type eclipsing variables,2 program stars,an X-ray source and 2 Asteroid Terrestrial-impact Last Alert System variables.Based on the data products from the SPDE program,a follow-up light curve analysis program identifies 32 potential variable light curves,of which 18 are from the time series of RX J2102.0+3359,and 14 are from that of Paloma J0524+4244.They are preliminarily separated into periodic,transient,and peculiar types.By querying for the 58 VizieR online data catalogs,their physical parameters and multi-band brightness spanning X-ray to radio are compiled for future analysis.展开更多
The Wide-field Infrared Survey Explorer(WISE)has detected hundreds of millions of sources over the entire sky.However,classifying them reliably is a great challenge due to degeneracies in WISE multicolor space and low...The Wide-field Infrared Survey Explorer(WISE)has detected hundreds of millions of sources over the entire sky.However,classifying them reliably is a great challenge due to degeneracies in WISE multicolor space and low detection levels in its two longest-wavelength bandpasses.In this paper,the deep learning classification network,IICnet(Infrared Image Classification network),is designed to classify sources from WISE images to achieve a more accurate classification goal.IICnet shows good ability on the feature extraction of the WISE sources.Experiments demonstrate that the classification results of IICnet are superior to some other methods;it has obtained 96.2%accuracy for galaxies,97.9%accuracy for quasars,and 96.4%accuracy for stars,and the Area Under Curve of the IICnet classifier can reach more than 99%.In addition,the superiority of IICnet in processing infrared images has been demonstrated in the comparisons with VGG16,GoogleNet,ResNet34,Mobile Net,EfficientNetV2,and RepVGG-fewer parameters and faster inference.The above proves that IICnet is an effective method to classify infrared sources.展开更多
Optical survey is an important means for observing resident space objects and space situational awareness.With the application of astronomical techniques and reduction method,wide field of view telescopes have made si...Optical survey is an important means for observing resident space objects and space situational awareness.With the application of astronomical techniques and reduction method,wide field of view telescopes have made significant contributions in discovering and identifying resident space objects.However,with the development of modern optical and electronic technology,the detection limit of instruments and infrastructure has been greatly extended,leading to an extensive number of raw images and many more sources in these images.Challenges arise when reducing these data in terms of traditional measurement and calibration.Based on the amount of data,it is particularly feasible and reliable to apply machine learning algorithms.Here an end-to-end deep learning framework is developed,it is trained with a priori information on raw detections and the automatic detection task is performed on the new data acquired.The closed-loop is evaluated based on consecutive CCD images obtained with a dedicated space debris survey telescope.It is demonstrated that our framework can achieve high performance compared with the traditional method,and with data fusion,the efficiency of the system can be improved without changing hardware or deploying new devices.The technique deserves a wider application in many fields of observational astronomy.展开更多
The development of spectroscopic survey telescopes like Large Sky Area Multi-Object Fiber Spectroscopic Telescope(LAMOST),Apache Point Observatory Galactic Evolution Experiment and Sloan Digital Sky Survey has opened ...The development of spectroscopic survey telescopes like Large Sky Area Multi-Object Fiber Spectroscopic Telescope(LAMOST),Apache Point Observatory Galactic Evolution Experiment and Sloan Digital Sky Survey has opened up unprecedented opportunities for stellar classification.Specific types of stars,such as early-type emission-line stars and those with stellar winds,can be distinguished by the profiles of their spectral lines.In this paper,we introduce a method based on derivative spectroscopy(DS)designed to detect signals within complex backgrounds and provide a preliminary estimation of curve profiles.This method exhibits a unique advantage in identifying weak signals and unusual spectral line profiles when compared to other popular line detection methods.We validated our approach using synthesis spectra,demonstrating that DS can detect emission signals three times fainter than Gaussian fitting.Furthermore,we applied our method to 579,680 co-added spectra from LAMOST Medium-Resolution Spectroscopic Survey,identifying 16,629 spectra with emission peaks around the Hαline from 10,963 stars.These spectra were classified into three distinct morphological groups,resulting in nine subclasses as follows.(1)Emission peak above the pseudo-continuum line(single peak,double peaks,emission peak situated within an absorption line,P Cygni profile,Inverse P Cygni profile);(2)Emission peak below the pseudo-continuum line(sharp emission peak,double absorption peaks,emission peak shifted to one side of the absorption line);(3)Emission peak between the pseudo-continuum line.展开更多
We report the confirmation of a sub-Saturn-size exoplanet,TOI-1194 b,with a mass of about 0.456+0.055-0.051M_(J),and a very low mass companion star with a mass of about 96.5±1.5 MJ,TOI-1251 B.Exoplanet candidates...We report the confirmation of a sub-Saturn-size exoplanet,TOI-1194 b,with a mass of about 0.456+0.055-0.051M_(J),and a very low mass companion star with a mass of about 96.5±1.5 MJ,TOI-1251 B.Exoplanet candidates provided by the Transiting Exoplanet Survey Satellite(TESS)are suitable for further follow-up observations by ground-based telescopes with small and medium apertures.The analysis is performed based on data from several telescopes worldwide,including telescopes in the Sino-German multiband photometric campaign,which aimed at confirming TESS Objects of Interest(TOIs)using ground-based small-aperture and medium-aperture telescopes,especially for long-period targets.TOI-1194 b is confirmed based on the consistent periodic transit depths from the multiband photometric data.We measure an orbital period of 2.310644±0.000001 days,the radius is 0.767+0.045-0.041RJ and the amplitude of the RV curve is 69.4_(-7.3)^(+7.9)m s^(-1).TOI-1251 B is confirmed based on the multiband photometric and high-resolution spectroscopic data,whose orbital period is 5.963054+0.000002-0.000001days,radius is 0.947+0.035-0.033 R_(J) and amplitude of the RV curve is 9849_(-40)^(+42)ms^(-1).展开更多
Stellar spectral classification is crucial in astronomical data analysis.However,existing studies are often limited by the uneven distribution of stellar samples,posing challenges in practical applications.Even when b...Stellar spectral classification is crucial in astronomical data analysis.However,existing studies are often limited by the uneven distribution of stellar samples,posing challenges in practical applications.Even when balancing stellar categories and their numbers,there is room for improvement in classification accuracy.This study introduces a Continuous Wavelet Transform using the Super Morlet wavelet to convert stellar spectra into wavelet images.A novel neural network,the Stellar Feature Network,is proposed for classifying these images.Stellar spectra from Large Sky Area Multi-Object Fiber Spectroscopic Telescope DR9,encompassing five equal categories(B,A,F,G,K),were used.Comparative experiments validate the effectiveness of the proposed methods and network,achieving significant improvements in classification accuracy.展开更多
Defocusing spot size detection is especially essential for aberration analysis and correction of optical systems. In the case of far defocusing, the celestial forms a pupil image on the detector, and the size of the i...Defocusing spot size detection is especially essential for aberration analysis and correction of optical systems. In the case of far defocusing, the celestial forms a pupil image on the detector, and the size of the image is linearly changed with the defocusing distance, and can be used to correct the optical system and analyze the image quality. Based on the focal plane attitude detection of Large Sky Area MultiObject Fiber Spectroscopy Telescope(LAMOST), this paper uses a variety of methods to detect the size of the defocusing spot of LAMOST telescope. For the particularity of the spot, the average value spacing algorithm, the peak value spacing algorithm, the ellipse fitting algorithm, and the multi-peak Gaussian fitting algorithm are used to detect the spot size. This paper will introduce these four methods, in which the average value spacing algorithm is proposed by the author of this paper. The advantages and disadvantages of the four methods are compared. The experimental results show that the average value spacing algorithm can achieve better accuracy of spot size detection in the four algorithms.展开更多
A filament is an important structure for studying star formation,especially intersections of filaments which are believed to be more dense than other regions.Identifying filament intersections is the first step in stu...A filament is an important structure for studying star formation,especially intersections of filaments which are believed to be more dense than other regions.Identifying filament intersections is the first step in studying them.Current methods can only extract two-dimensional intersections without considering the velocity dimension.In this paper,we propose a method to identify three-dimensional(3 D)intersections by combining Harris Corner Detection and Hough Line Transform,which achieve a precision of 98%.We apply this method for extracting intersection structures of the OMC-2/3 molecular cloud and to study its physical properties and obtain the associated PDF distribution.Results show denser gas is concentrated in those 3 D intersections.展开更多
Aiming at the subband division of ultra-wide bandwidth low-frequency(UWL)signal(frequency coverage range:704–4032 MHz)of the Xinjiang 110 m QiTai radio Telescope(QTT),a scheme of ultra-wide bandwidth signal is design...Aiming at the subband division of ultra-wide bandwidth low-frequency(UWL)signal(frequency coverage range:704–4032 MHz)of the Xinjiang 110 m QiTai radio Telescope(QTT),a scheme of ultra-wide bandwidth signal is designed.First,we analyze the effect of different window functions such as the Hanning window,Hamming window,and Kaiser window on the performance of finite impulse response(FIR)digital filters,and implement a critical sampling polyphase filter bank(CS-PFB)based on the Hamming window FIR digital filter.Second,we generate 3328 MHz simulation data of ultra-wideband pulsar baseband in the frequency range of 704–4032 MHz using the ultra-wide bandwidth pulsar baseband data generation algorithm based on the 400 MHz bandwidth pulsar baseband data obtained from Parkes CASPSR observations.Third,we obtain 26 subbands of 128 MHz based on CS-PFB and the simulation data,and the pulse profile of each subband by coherent dispersion,integration,and folding.Finally,the phase of each subband pulse profile is aligned by non-coherent dedispersion,and to generate a broadband pulse profile,which is basically the same as the pulse profile obtained from the original data using DSPSR.The experimental results show that the scheme for the QTT UWL receiving system is feasible,and the proposed channel algorithm in this paper is effective.展开更多
From the mid-19th century to the end of the 20th century, photographic plates served as the primary detectors for astronomical observations. Astronomical photographic observations in China began in 1901, and over a ce...From the mid-19th century to the end of the 20th century, photographic plates served as the primary detectors for astronomical observations. Astronomical photographic observations in China began in 1901, and over a century, a total of approximately 30,000 astronomical photographic plates were captured. These historical plates play an irreplaceable role in conducting long-term, time-domain astronomical research. To preserve and explore these valuable original astronomical observational data, Shanghai Astronomical Observatory has organized the transportation of plates, taken during nighttime observations from various stations across the country, to the Sheshan Plate Archive for centralized preservation. For the first time, plate information statistics were calculated. On this basis, the plates were cleaned and digitally scanned, and finally digitized images were acquired for 29,314 plates. In this study, using Gaia DR2 as the reference star catalog, astrometric processing was carried out successfully on 15,696 single-exposure plates, including object extraction, stellar identification,and plate model computation. As a result, for long focal length telescopes, such as the 40 cm double-tube refractor telescope, the 1.56 m reflector telescope at Shanghai Astronomical Observatory, and the 1m reflecting telescope at Yunnan Astronomical Observatory, the astrometric accuracy obtained for their plates is approximately 0."1–0."3. The distribution of astrometric accuracy for medium and short focal length telescopes ranges from 0."3 to 1."0. The relevant data of this batch of plates, including digitized images and a stellar catalog of the plates, are archived and released by the National Astronomical Data Center. Users can access and download plate data based on keywords such as station, telescope, observation year, and observed celestial coordinates.展开更多
The eclipsing binary star RS Sct is a semi-detached system of theβLyrae type.This system was photometered for six nights in 2019 August,and 2020 June and August.The light and radial velocity curves were simultaneousl...The eclipsing binary star RS Sct is a semi-detached system of theβLyrae type.This system was photometered for six nights in 2019 August,and 2020 June and August.The light and radial velocity curves were simultaneously analyzed to obtain the absolute physical and orbital parameters of the system,and the system geometry was determined.In this system,the primary component has filled its inner Roche lobe and the secondary component is close to filling it.Moreover,the change in the orbital period of this system was investigated.The presence of the third or fourth components and the mass transfer between the two components affect the orbital period of the system.In addition,the pulsation of the primary component was detected.Also,several frequencies with high signal-to-noise ratios were identified.According to the position of the primary component in the H-R diagram and the value of the obtained frequencies,this component is likely a delta-Scuti pulsator.展开更多
基金supported by the Chinese Meridian Project(CMP) and the National Natural Science Foundation of China(grant Nos.42230203,42374220,and 12173086)。
文摘The Spectral Imaging Corona Graph(SICG) serves as the optical observation equipment of E-corona in the Chinese Meridian Project Phase II, which aims at monitoring the initial source of solar activities. For the purpose of indepth exploration and space weather forecast in the full chain of Sun–Earth space, SICG is designed to work at two wavelengths of 637.4 and 530.3 nm in the quasi-simultaneous observation mode. Thus, the photometric calibration is more challenging to guarantee accurate scientific data of SICG. Two solar photometers are specially developed to match the observing wavelengths and make the photoelectronic conversion traceable. Correspondingly, the calibration process selects the solar disk center as the brightness reference, which compensates for the photometric losses along the atmospheric transmission path. This study derives the calibration coefficients from the two photometers for the E-coronal brightness processing in real time. By modeling aerosol absorption and scattering and comparing with continuous flat-field observation, the photometric calibration of SICG is evaluated with deviations of 2.1% and 2.3% at 637.4 nm and 530.3 nm, respectively. Based on this, the evolution speed of a multitemperature coronal loop was analyzed, facilitating further research into the physical mechanisms of coronal mass ejections.
基金supported by the China National Key Program for Science and Technology Research and Development of China (2022YFA1602901,2023YFA1608204)the National SKA Program of China (No.2022SKA0110201)+5 种基金the National Natural Science Foundation of China (NSFC,grant Nos.11873051,11988101,12033008,12041305,12125302,12173016,and 12203065)the CAS Project for Young Scientists in Basic Research grant (No.YSBR-062)the K.C.Wong Education Foundationthe science research grants from the China Manned Space Projectsupport from the Cultivation Project for FAST Scientific Payoff and Research Achievement of CAMS-CASsupported by the China Postdoctoral Science Foundation grant No.2024M763213
文摘The standing waves existing in radio telescope data are primarily due to reflections among the instruments,which significantly impact the spectral quality of the Five-hundred-meter Aperture Spherical radio Telescope(FAST).Eliminating these standing waves for FAST is challenging given the constant changes in their phases and amplitudes.Over a ten-second period,the phases shift by 18°while the amplitudes fluctuate by 6 mK.Thus,we developed the fast Fourier transform(FFT)filter method to eliminate these standing waves for every individual spectrum.The FFT filter can decrease the rms from 3.2 to 1.15 times the theoretical estimate.Compared to other methods such as sine fitting and running median,the FFT filter achieves a median rms of approximately 1.2 times the theoretical expectation and the smallest scatter at 12%.Additionally,the FFT filter method avoids the flux loss issue encountered with some other methods.The FFT is also efficient in detecting harmonic radio frequency interference(RFI).In the FAST data,we identified three distinct types of harmonic RFI,each with amplitudes exceeding 100 mK and intrinsic frequency periods of 8.1,0.5,and 0.37 MHz,respectively.The FFT filter,proven as the most effective method,is integrated into the H I data calibration and imaging pipeline for FAST(HiFAST,https://hifast.readthedocs.io).
基金supported by the National Key R&D Program of China Nos.2021YFC2203502 and 2022YFF0711502the National Natural Science Foundation of China(NSFC)(12173077)+5 种基金the Chinese Academy of Sciences(CAS)“Light of West China”Program(No.xbzg-zdsys-202410)the Tianshan Talent Project of Xinjiang Uygur Autonomous Region(2022TSYCCX0095 and 2023TSYCCX0112)the Scientific Instrument Developing Project of the Chinese Academy of Sciences,grant No.PTYQ2022YZZD01China National Astronomical Data Center(NADC)the Operation,Maintenance and Upgrading Fund for Astronomical Telescopes and Facility Instruments,budgeted from the Ministry of Finance of China(MOF)and administrated by the Chinese Academy of Sciences(CAS)Natural Science Foundation of Xinjiang Uygur Autonomous Region(2022D01A360).
文摘To address the issues of low accuracy and high computational complexity in traditional channelization techniques for ultra-wideband signals,this paper proposes a novel rationally oversampled channelization method to enhance the accuracy and efficiency of signal processing.The proposed method is evaluated by implementing and comparing critically sampled and integer oversampled channelization algorithms.A detailed analysis of the impact of different oversampling factors and filter orders on performance is provided.The validity of the proposed algorithm is verified using baseband data from pulsar J0437−4715 observed by the Parkes telescope,demonstrating its effectiveness and correctness.
基金funded by the National Science and Technology Major Project(2022ZD0117401)the National Defense Science and Technology Innovation Special Zone Project Foundation of China(grant No.19-163-21-TS-001-067-01)support was provided by the Chinese Academy of Sciences(CAS)“Light of West China”Program(No.2020-XBQNXZ-016 and No.2022-XBQNXZ-016).
文摘Near-Earth Asteroids posed a threat to human civilization,making their monitoring crucial.As the demand for asteroid detection technology increased,precise detection of these celestial bodies became an urgent task to understand their characteristics and assess potential impact risks.To improve asteroid detection accuracy and efficiency,we proposed an advanced image processing method and a deep learning network for automatic asteroid detection.Specifically,we aligned star clusters and overlaid images to exploit asteroid motion rates,transforming them into object-like trajectories and improving the signal-to-noise ratio.This approach created the Asteroid Trajectory Image Data set under various conditions.We modified CenterNet2 network to develop AstroCenterNet by integrating Multi-channel Histogram Truncation for feature enhancement,using the SimAM attention mechanism to expand contextual information and suppress noise,and refining Feature Pyramid Network to improve low-level feature detection.Our results demonstrated a detection accuracy of 98.4%,a recall of 97.6%,a mean Average Precision of 94.01%,a false alarm rate of 1.6%,and a processing speed of approximately 17.86 frames per second,indicating that our method achieves high precision and efficiency.
基金supported by the Youth Program of the Natural Science Foundation of Qinghai Province(2023-ZJ-951Q)Qinghai University Research Ability Enhancement Project(2025KTSQ26).
文摘Aperture photometry is a fundamental technique widely used to obtain high-precision light curves in optical survey projects like Tianyu.However,its effectiveness is limited in crowded fields,and the choice of aperture size critically impacts photometric precision.To address these challenges,we propose DeepAP,an efficient and accurate two-stage deep learning framework for aperture photometry.Specifically,for a given source,we first train a Vision Transformer(ViT)model to assess its feasibility of aperture photometry.We then train the Residual Neural Network(ResNet)to predict its optimal aperture size.For aperture photometry feasibility assessment,the ViT model yields an ROC AUC value of 0.96,and achieves a precision of 0.974,a recall of 0.930,and an F1 score of 0.952 on the test set.For aperture size prediction,the ResNet model effectively mitigates biases inherent in classical growth curve methods by adaptively selecting apertures appropriate for sources of varying brightness,thereby enhancing the signal-to-noise ratio(SNR)across a wide range of targets.Meanwhile,some samples in the test set have a higher SNR than those obtained by exhaustive aperture size enumeration because of the finer granularity of aperture size estimation.By integrating ResNet with the ViT network,the DeepAP framework achieves a median total processing time of 18 ms for a batch of 10 images,representing a speed-up of approximately 5.9×10^(4) times compared to exhaustive aperture size enumeration.This work paves the way for the automatic application of aperture photometry in future high-precision surveys such as Tianyu and Legacy Survey of Space and Time.The source code and model are available at https://github.com/ruiyicheng/DeepAP.
基金supported by the National Natural Science Foundation of China(NSFC,Grant Nos.12063002 and 12163004).
文摘Measuring the transverse velocity field in high-resolution solar images is essential for understanding solar dynamics.This paper introduces an innovative unsupervised deep learning optical flow model designed to calculate the transverse velocity field,addressing the challenges of missing optical flow labels and the limited accuracy of velocity field measurements in high-resolution solar images.The proposed method converts the transverse velocity field computation problem into an optical flow computation problem,using two forward propagations of features to get rid of the reliance on optical flow labels.Additionally,it reduces the impact of the“Brightness Consistency”constraint on optical flow accuracy by identifying and handling optical flow outliers.We apply this method to compute the transverse velocity fields of high-resolution solar image sequences from the Hαand TiO bands,observed by the New Vacuum Solar Telescope.Comparative experiments with several wellestablished optical flow methods,including those based on supervised deep learning models,show that our approach outperforms the comparison methods according to key evaluation metrics such as Residual Map Mean,Residual Map Variance,Cross Correlation,and Structural Similarity Index Measure.Moreover,since optical flow captures the fundamental motion information in image sequences,the proposed method can be applied to a variety of research areas,including solar image registration,sequence alignment,image super-resolution,magnetic field calibration,and solar activity forecasting.The code is available at https://github.com/jackie-willianm/Transverse-Velocity-Field-Measurement-of-Solar-High-Resolution-Images.
基金supported by the Young Data Scientist Program of the China National Astronomical Data Center。
文摘The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) has become a crucial resource in astronomical research,offering a vast amount of spectral data for stars,galaxies,and quasars.This paper presents the data processing methods used by LAMOST,focusing on the classification and redshift measurement of large spectral data sets through template matching,as well as the creation of data products.Additionally,this paper details the construction of the Multiple Epoch Catalogs by integrating LAMOST spectral data with photometric data from Gaia and Pan-STARRS,and explains the creation of both low-and medium-resolution data products.
基金supported by the National Key Research and Development Program of China No.2023YFA1608301the National Natural Science Foundation of China(NSFC,grant Nos.12133010 and 12273025)
文摘Certain transients require regular observations over several days at intervals of hours or shorter,which cannot be accomplished by telescopes at a single site.The deployment of globally distributed telescopes at geographic locations of different longitudes enables the periodic monitoring of transients through relay observation.However,the simultaneous relay observation of numerous targets requires a telescope array of multiple telescopes that can be efficiently coordinated,and an automated scheduler for the array.This paper proposes IPROS,an integer programming model relay observation scheduler for a telescope array,that accounts for the entire process of relay observation and is consistent with the practical scenarios.We introduce the integer programming mathematical model for the relay observation scheduling problem with the telescope array,upon which the scheduler is based.Additionally,we propose an algorithm to provide a comprehensive formulation of the optimization objective of minimizing cadence deviation in the model.Experimental results demonstrate that the relay observation scheduler based on the integer programming model can effectively address the telescope array relay observation problem.It shows superiority over a scheduler with non-specific consideration of relay observation in the modeling and a scheduler based on greedy thought.
基金partially supported by the Open Research Program of the CAS Key Laboratory of Solar Activity (KLSA201805)the Guizhou Science & Technology Plan Project (Platform Talent No.[2017]5788)+3 种基金the Youth Science & Technology Talents Development Project of Guizhou Education Department (No. KY[2018]119)the National Science Foundation of China (Grant Nos. 11103055, 11773062 and 61605153)“Light of West China” Programme (Grant Nos. RCPY201105 and 2017-XBQNXZ-A-008)the National Basic Research Program of China (973 program: 2012CB821804 and 2015CB857100)
文摘Radio interferometry significantly improves the resolution of observed images, and the final result also relies heavily on data recovery. The Cotton-Schwab CLEAN(CS-Clean) deconvolution approach is a widely used reconstruction algorithm in the field of radio synthesis imaging. However, parameter tuning for this algorithm has always been a difficult task. Here, its performance is improved by considering some internal characteristics of the data. From a mathematical point of view, a peak signal-to-noise-based(PSNRbased) method was introduced to optimize the step length of the steepest descent method in the recovery process. We also found that the loop gain curve in the new algorithm is a good indicator of parameter tuning.Tests show that the new algorithm can effectively solve the problem of oscillation for a large fixed loop gain and provides a more robust recovery.
基金partly supported by the CAS Light of West China Programthe Yunnan Youth Talent Project+3 种基金the Yunnan Fundamental Research Projects(grant No.2016FB007,No.202201AT070180)the National Natural Science Foundation of China(NSFC,No.11933008)partially supported by the Open Project Program of the CAS Key Laboratory of Optical Astronomy,National Astronomical Observatories,Chinese Academy of Sciencessupport from the Yunnan Fundamental Research Key Projects(grant No.202001BB050032)。
文摘A Synchronous Photometry Data Extraction(SPDE)program,performing indiscriminate monitoring of all stars appearing in the same field of view of an astronomical image,is developed by integrating several Astropy affiliated packages to make full use of time series observed by traditional small/medium aperture ground-based telescopes.The complete full-frame stellar photometry data reductions implemented for the two time series of cataclysmic variables:RX J2102.0+3359 and Paloma J0524+4244 produce 363 and 641 optimal light curves,respectively.A cross-identification with SIMBAD finds 23 known stars,of which 16 are red giant-/horizontal-branch stars,2 W UMa-type eclipsing variables,2 program stars,an X-ray source and 2 Asteroid Terrestrial-impact Last Alert System variables.Based on the data products from the SPDE program,a follow-up light curve analysis program identifies 32 potential variable light curves,of which 18 are from the time series of RX J2102.0+3359,and 14 are from that of Paloma J0524+4244.They are preliminarily separated into periodic,transient,and peculiar types.By querying for the 58 VizieR online data catalogs,their physical parameters and multi-band brightness spanning X-ray to radio are compiled for future analysis.
基金supported by the Natural Science Foundation of Tianjin(22JCYBJC00410)the Joint Research Fund in Astronomy,National Natural Science Foundation of China(U1931134)。
文摘The Wide-field Infrared Survey Explorer(WISE)has detected hundreds of millions of sources over the entire sky.However,classifying them reliably is a great challenge due to degeneracies in WISE multicolor space and low detection levels in its two longest-wavelength bandpasses.In this paper,the deep learning classification network,IICnet(Infrared Image Classification network),is designed to classify sources from WISE images to achieve a more accurate classification goal.IICnet shows good ability on the feature extraction of the WISE sources.Experiments demonstrate that the classification results of IICnet are superior to some other methods;it has obtained 96.2%accuracy for galaxies,97.9%accuracy for quasars,and 96.4%accuracy for stars,and the Area Under Curve of the IICnet classifier can reach more than 99%.In addition,the superiority of IICnet in processing infrared images has been demonstrated in the comparisons with VGG16,GoogleNet,ResNet34,Mobile Net,EfficientNetV2,and RepVGG-fewer parameters and faster inference.The above proves that IICnet is an effective method to classify infrared sources.
基金funded by the National Natural Science Foundation of China(NSFC,grant Nos.12473079 and 12073082)the National Key R&D Program of China(No.2023YFF0725300)。
文摘Optical survey is an important means for observing resident space objects and space situational awareness.With the application of astronomical techniques and reduction method,wide field of view telescopes have made significant contributions in discovering and identifying resident space objects.However,with the development of modern optical and electronic technology,the detection limit of instruments and infrastructure has been greatly extended,leading to an extensive number of raw images and many more sources in these images.Challenges arise when reducing these data in terms of traditional measurement and calibration.Based on the amount of data,it is particularly feasible and reliable to apply machine learning algorithms.Here an end-to-end deep learning framework is developed,it is trained with a priori information on raw detections and the automatic detection task is performed on the new data acquired.The closed-loop is evaluated based on consecutive CCD images obtained with a dedicated space debris survey telescope.It is demonstrated that our framework can achieve high performance compared with the traditional method,and with data fusion,the efficiency of the system can be improved without changing hardware or deploying new devices.The technique deserves a wider application in many fields of observational astronomy.
基金the support provided by the National Natural Science Foundation of China(NSFC,Grant Nos.12090040/3,12125303,12288102,and 11733008)the National Key Research and Development Program of China(grant No.2021YFA1600401/3)+3 种基金the China Manned Space Project(CMSCSST-2021-A10)the Yunnan Fundamental Research Projects(grant No.202101AV070001)the National Natural Science Foundation of China and the Chinese Academy of Sciences,under grant No.U1831125the Research Program of Frontier Sciences,CAS(grant No.QYZDY-SSW-SLH007)。
文摘The development of spectroscopic survey telescopes like Large Sky Area Multi-Object Fiber Spectroscopic Telescope(LAMOST),Apache Point Observatory Galactic Evolution Experiment and Sloan Digital Sky Survey has opened up unprecedented opportunities for stellar classification.Specific types of stars,such as early-type emission-line stars and those with stellar winds,can be distinguished by the profiles of their spectral lines.In this paper,we introduce a method based on derivative spectroscopy(DS)designed to detect signals within complex backgrounds and provide a preliminary estimation of curve profiles.This method exhibits a unique advantage in identifying weak signals and unusual spectral line profiles when compared to other popular line detection methods.We validated our approach using synthesis spectra,demonstrating that DS can detect emission signals three times fainter than Gaussian fitting.Furthermore,we applied our method to 579,680 co-added spectra from LAMOST Medium-Resolution Spectroscopic Survey,identifying 16,629 spectra with emission peaks around the Hαline from 10,963 stars.These spectra were classified into three distinct morphological groups,resulting in nine subclasses as follows.(1)Emission peak above the pseudo-continuum line(single peak,double peaks,emission peak situated within an absorption line,P Cygni profile,Inverse P Cygni profile);(2)Emission peak below the pseudo-continuum line(sharp emission peak,double absorption peaks,emission peak shifted to one side of the absorption line);(3)Emission peak between the pseudo-continuum line.
基金supported by National Natural Science Foundation of China(NSFC,Grant Nos.U1831209 and U2031144)the research fund of Ankara University(BAP)through the project 18A0759001。
文摘We report the confirmation of a sub-Saturn-size exoplanet,TOI-1194 b,with a mass of about 0.456+0.055-0.051M_(J),and a very low mass companion star with a mass of about 96.5±1.5 MJ,TOI-1251 B.Exoplanet candidates provided by the Transiting Exoplanet Survey Satellite(TESS)are suitable for further follow-up observations by ground-based telescopes with small and medium apertures.The analysis is performed based on data from several telescopes worldwide,including telescopes in the Sino-German multiband photometric campaign,which aimed at confirming TESS Objects of Interest(TOIs)using ground-based small-aperture and medium-aperture telescopes,especially for long-period targets.TOI-1194 b is confirmed based on the consistent periodic transit depths from the multiband photometric data.We measure an orbital period of 2.310644±0.000001 days,the radius is 0.767+0.045-0.041RJ and the amplitude of the RV curve is 69.4_(-7.3)^(+7.9)m s^(-1).TOI-1251 B is confirmed based on the multiband photometric and high-resolution spectroscopic data,whose orbital period is 5.963054+0.000002-0.000001days,radius is 0.947+0.035-0.033 R_(J) and amplitude of the RV curve is 9849_(-40)^(+42)ms^(-1).
文摘Stellar spectral classification is crucial in astronomical data analysis.However,existing studies are often limited by the uneven distribution of stellar samples,posing challenges in practical applications.Even when balancing stellar categories and their numbers,there is room for improvement in classification accuracy.This study introduces a Continuous Wavelet Transform using the Super Morlet wavelet to convert stellar spectra into wavelet images.A novel neural network,the Stellar Feature Network,is proposed for classifying these images.Stellar spectra from Large Sky Area Multi-Object Fiber Spectroscopic Telescope DR9,encompassing five equal categories(B,A,F,G,K),were used.Comparative experiments validate the effectiveness of the proposed methods and network,achieving significant improvements in classification accuracy.
基金Funding for the project has been provided by the National Development and Reform Commissionsupported by the National Key Basic Research Program of China(2015CB857100)the National Natural Science Foundation of China(Grant Nos.U1331204 and 14473050).
文摘Defocusing spot size detection is especially essential for aberration analysis and correction of optical systems. In the case of far defocusing, the celestial forms a pupil image on the detector, and the size of the image is linearly changed with the defocusing distance, and can be used to correct the optical system and analyze the image quality. Based on the focal plane attitude detection of Large Sky Area MultiObject Fiber Spectroscopy Telescope(LAMOST), this paper uses a variety of methods to detect the size of the defocusing spot of LAMOST telescope. For the particularity of the spot, the average value spacing algorithm, the peak value spacing algorithm, the ellipse fitting algorithm, and the multi-peak Gaussian fitting algorithm are used to detect the spot size. This paper will introduce these four methods, in which the average value spacing algorithm is proposed by the author of this paper. The advantages and disadvantages of the four methods are compared. The experimental results show that the average value spacing algorithm can achieve better accuracy of spot size detection in the four algorithms.
基金supported by the National Natural Science Foundation of China(Grant Nos.11988101,11725313,11403041,11373038 and 11373045)CAS International Partnership Program(No.114A11KYSB20160008)the Young Researcher Grant of National Astronomical Observatories,Chinese Academy of Sciences.
文摘A filament is an important structure for studying star formation,especially intersections of filaments which are believed to be more dense than other regions.Identifying filament intersections is the first step in studying them.Current methods can only extract two-dimensional intersections without considering the velocity dimension.In this paper,we propose a method to identify three-dimensional(3 D)intersections by combining Harris Corner Detection and Hough Line Transform,which achieve a precision of 98%.We apply this method for extracting intersection structures of the OMC-2/3 molecular cloud and to study its physical properties and obtain the associated PDF distribution.Results show denser gas is concentrated in those 3 D intersections.
基金supported by the National Key R&D Program of China(Nos.2021YFC2203502,2022YFF0711502)the National Natural Science Foundation of China(NSFC,Grant Nos.12173077,12003062)+5 种基金the Tianshan Innovation Team Plan of Xinjiang Uygur Autonomous Region(2022D14020)the Tianshan Talent Project of Xinjiang Uygur Autonomous Region(2022TSYCCX0095)the Scientific Instrument Developing Project of the Chinese Academy of Sciences,grant No.PTYQ2022YZZD01China National Astronomical Data Center(NADC)the Operation,Maintenance and Upgrading Fund for Astronomical Telescopes and Facility Instruments,budgeted from the Ministry of Finance of China(MOF)and administrated by the Chinese Academy of Sciences(CAS)Natural Science Foundation of Xinjiang Uygur Autonomous Region(2022D01A360)。
文摘Aiming at the subband division of ultra-wide bandwidth low-frequency(UWL)signal(frequency coverage range:704–4032 MHz)of the Xinjiang 110 m QiTai radio Telescope(QTT),a scheme of ultra-wide bandwidth signal is designed.First,we analyze the effect of different window functions such as the Hanning window,Hamming window,and Kaiser window on the performance of finite impulse response(FIR)digital filters,and implement a critical sampling polyphase filter bank(CS-PFB)based on the Hamming window FIR digital filter.Second,we generate 3328 MHz simulation data of ultra-wideband pulsar baseband in the frequency range of 704–4032 MHz using the ultra-wide bandwidth pulsar baseband data generation algorithm based on the 400 MHz bandwidth pulsar baseband data obtained from Parkes CASPSR observations.Third,we obtain 26 subbands of 128 MHz based on CS-PFB and the simulation data,and the pulse profile of each subband by coherent dispersion,integration,and folding.Finally,the phase of each subband pulse profile is aligned by non-coherent dedispersion,and to generate a broadband pulse profile,which is basically the same as the pulse profile obtained from the original data using DSPSR.The experimental results show that the scheme for the QTT UWL receiving system is feasible,and the proposed channel algorithm in this paper is effective.
基金supported by the Shanghai Science and Technology Innovation Action Plan(grant No.21511104100)the Global Common Challenge Special Project(grant No.018GJHZ2023110GC)the China National Key Basic Research Program(grant No.2012FY120500)。
文摘From the mid-19th century to the end of the 20th century, photographic plates served as the primary detectors for astronomical observations. Astronomical photographic observations in China began in 1901, and over a century, a total of approximately 30,000 astronomical photographic plates were captured. These historical plates play an irreplaceable role in conducting long-term, time-domain astronomical research. To preserve and explore these valuable original astronomical observational data, Shanghai Astronomical Observatory has organized the transportation of plates, taken during nighttime observations from various stations across the country, to the Sheshan Plate Archive for centralized preservation. For the first time, plate information statistics were calculated. On this basis, the plates were cleaned and digitally scanned, and finally digitized images were acquired for 29,314 plates. In this study, using Gaia DR2 as the reference star catalog, astrometric processing was carried out successfully on 15,696 single-exposure plates, including object extraction, stellar identification,and plate model computation. As a result, for long focal length telescopes, such as the 40 cm double-tube refractor telescope, the 1.56 m reflector telescope at Shanghai Astronomical Observatory, and the 1m reflecting telescope at Yunnan Astronomical Observatory, the astrometric accuracy obtained for their plates is approximately 0."1–0."3. The distribution of astrometric accuracy for medium and short focal length telescopes ranges from 0."3 to 1."0. The relevant data of this batch of plates, including digitized images and a stellar catalog of the plates, are archived and released by the National Astronomical Data Center. Users can access and download plate data based on keywords such as station, telescope, observation year, and observed celestial coordinates.
基金financial support of University of Birjand for this research under contract number 1399/D/6211。
文摘The eclipsing binary star RS Sct is a semi-detached system of theβLyrae type.This system was photometered for six nights in 2019 August,and 2020 June and August.The light and radial velocity curves were simultaneously analyzed to obtain the absolute physical and orbital parameters of the system,and the system geometry was determined.In this system,the primary component has filled its inner Roche lobe and the secondary component is close to filling it.Moreover,the change in the orbital period of this system was investigated.The presence of the third or fourth components and the mass transfer between the two components affect the orbital period of the system.In addition,the pulsation of the primary component was detected.Also,several frequencies with high signal-to-noise ratios were identified.According to the position of the primary component in the H-R diagram and the value of the obtained frequencies,this component is likely a delta-Scuti pulsator.
