Seeing is an important index to evaluate the quality of an astronomical site.To estimate seeing at the Muztagh-Ata site with height and time quantitatively,the European Centre for Medium-Range Weather Forecasts reanal...Seeing is an important index to evaluate the quality of an astronomical site.To estimate seeing at the Muztagh-Ata site with height and time quantitatively,the European Centre for Medium-Range Weather Forecasts reanalysis database(ERA5)is used.Seeing calculated from ERA5 is compared consistently with the Differential Image Motion Monitor seeing at the height of 12 m.Results show that seeing decays exponentially with height at the Muztagh-Ata site.Seeing decays the fastest in fall in 2021 and most slowly with height in summer.The seeing condition is better in fall than in summer.The median value of seeing at 12 m is 0.89 arcsec,the maximum value is1.21 arcsec in August and the minimum is 0.66 arcsec in October.The median value of seeing at 12 m is 0.72arcsec in the nighttime and 1.08 arcsec in the daytime.Seeing is a combination of annual and about biannual variations with the same phase as temperature and wind speed indicating that seeing variation with time is influenced by temperature and wind speed.The Richardson number Ri is used to analyze the atmospheric stability and the variations of seeing are consistent with Ri between layers.These quantitative results can provide an important reference for a telescopic observation strategy.展开更多
Lunar wrinkle ridges are an important stress geological structure on the Moon, which reflect the stress state and geological activity on the Moon. They provide important insights into the evolution of the Moon and are...Lunar wrinkle ridges are an important stress geological structure on the Moon, which reflect the stress state and geological activity on the Moon. They provide important insights into the evolution of the Moon and are key factors influencing future lunar activity, such as the choice of landing sites. However, automatic extraction of lunar wrinkle ridges is a challenging task due to their complex morphology and ambiguous features. Traditional manual extraction methods are time-consuming and labor-intensive. To achieve automated and detailed detection of lunar wrinkle ridges, we have constructed a lunar wrinkle ridge data set, incorporating previously unused aspect data to provide edge information, and proposed a Dual-Branch Ridge Detection Network(DBR-Net) based on deep learning technology. This method employs a dual-branch architecture and an Attention Complementary Feature Fusion module to address the issue of insufficient lunar wrinkle ridge features. Through comparisons with the results of various deep learning approaches, it is demonstrated that the proposed method exhibits superior detection performance. Furthermore, the trained model was applied to lunar mare regions, generating a distribution map of lunar mare wrinkle ridges;a significant linear relationship between the length and area of the lunar wrinkle ridges was obtained through statistical analysis, and six previously unrecorded potential lunar wrinkle ridges were detected. The proposed method upgrades the automated extraction of lunar wrinkle ridges to a pixel-level precision and verifies the effectiveness of DBR-Net in lunar wrinkle ridge detection.展开更多
BFOSC and YFOSC are the most frequently used instruments in the Xinglong 2.16 m telescope and Lijiang 2.4 m telescope,respectively.We developed a software package named“BYSpec”(BFOSC and YFOSC Spectra Reduction Pack...BFOSC and YFOSC are the most frequently used instruments in the Xinglong 2.16 m telescope and Lijiang 2.4 m telescope,respectively.We developed a software package named“BYSpec”(BFOSC and YFOSC Spectra Reduction Package)dedicated to automatically reducing the long-slit and echelle spectra obtained by these two instruments.The package supports bias and flat-fielding correction,order location,background subtraction,automatic wavelength calibration,and absolute flux calibration.The optimal extraction method maximizes the signal-to-noise ratio and removes most of the cosmic rays imprinted in the spectra.A comparison with the 1D spectra reduced with IRAF verifies the reliability of the results.This open-source software is publicly available to the community.展开更多
Method development has always been and will continue to be a core driving force of microbiome science, In this perspective, we argue that in the next decade, method development in microbiome analysis will be driven by...Method development has always been and will continue to be a core driving force of microbiome science, In this perspective, we argue that in the next decade, method development in microbiome analysis will be driven by three key changes in both ways of thinking and technological platforms： ① a shift from dissecting microbiota structure by sequencing to tracking microbiota state, function, and intercellular interaction via imaging; ② a shift from interrogating a consortium or population of cells to probing individual cells; and ③a shift from microbiome data analysis to microbiome data science. Some of the recent methoddevelopment efforts by Chinese microbiome scientists and their international collaborators that underlie these technological trends are highlighted here. It is our belief that the China Microbiome Initiative has the opportunity to deliver outstanding ＂Made-in-China＂ tools to the international research community, by building an ambitious, competitive, and collaborative program at the forefront of method development for microbiome science.展开更多
Noise is a significant part within a millimeter-wave molecular line datacube.Analyzing the noise improves our understanding of noise characteristics,and further contributes to scientific discoveries.We measure the noi...Noise is a significant part within a millimeter-wave molecular line datacube.Analyzing the noise improves our understanding of noise characteristics,and further contributes to scientific discoveries.We measure the noise level of a single datacube from MWISP and perform statistical analyses.We identified major factors which increase the noise level of a single datacube,including bad channels,edge effects,baseline distortion and line contamination.Cleaning algorithms are applied to remove or reduce these noise components.As a result,we obtained the cleaned datacube in which noise follows a positively skewed normal distribution.We further analyzed the noise structure distribution of a 3 D mosaicked datacube in the range l=40°7 to 43°3 and b=-2°3 to 0°3 and found that noise in the final mosaicked datacube is mainly characterized by noise fluctuation among the cells.展开更多
Very low frequency(VLF)signals are propagated between the ground-ionosphere.Multimode interference will cause the phase to show oscillatory changes with distance while propagating at night,leading to abnormalities in ...Very low frequency(VLF)signals are propagated between the ground-ionosphere.Multimode interference will cause the phase to show oscillatory changes with distance while propagating at night,leading to abnormalities in the received VLF signal.This study uses the VLF signal received in Qingdao City,Shandong Province,from the Russian Alpha navigation system to explore the multimode interference problem of VLF signal propagation.The characteristics of the effect of multimode interference phenomena on the phase are analyzed according to the variation of the phase of the VLF signal.However,the phase of VLF signals will also be affected by the X-ray and energetic particles that are released during the eruption of solar flares,therefore the two phenomena are studied in this work.It is concluded that the X-ray will not affect the phase of VLF signals at night,but the energetic particles will affect the phase change,and the influence of energetic particles should be excluded in the study of multimode interference phenomena.Using VLF signals for navigation positioning in degraded or unavailable GPS conditions is of great practical significance for VLF navigation systems as it can avoid the influence of multimode interference and improve positioning accuracy.展开更多
The microwave radiometer （MRM） onboard the Chang＇ E-1 （CE-I） lu- nar orbiter is a 4-frequency microwave radiometer, and it is mainly used to obtain the brightness temperature （TB） of the lunar surface, from whi...The microwave radiometer （MRM） onboard the Chang＇ E-1 （CE-I） lu- nar orbiter is a 4-frequency microwave radiometer, and it is mainly used to obtain the brightness temperature （TB） of the lunar surface, from which the thickness, temperature, dielectric constant and other related properties of the lunar regolith can be derived. The working mode of the CE-1 MRM, the ground calibration （including the official calibration coefficients）, as well as the acquisition and processing of the raw data are introduced. Our data analysis shows that TB increases with increasing frequency, decreases towards the lunar poles and is significantly affected by solar illumination. Our analysis also reveals that the main uncertainty in TB comes from ground calibration.展开更多
Attitude is one of the crucial parameters for space objects and plays a vital role in collision prediction and debris removal.Analyzing light curves to determine attitude is the most commonly used method.In photometri...Attitude is one of the crucial parameters for space objects and plays a vital role in collision prediction and debris removal.Analyzing light curves to determine attitude is the most commonly used method.In photometric observations,outliers may exist in the obtained light curves due to various reasons.Therefore,preprocessing is required to remove these outliers to obtain high quality light curves.Through statistical analysis,the reasons leading to outliers can be categorized into two main types:first,the brightness of the object significantly increases due to the passage of a star nearby,referred to as“stellar contamination,”and second,the brightness markedly decreases due to cloudy cover,referred to as“cloudy contamination.”The traditional approach of manually inspecting images for contamination is time-consuming and labor-intensive.However,we propose the utilization of machine learning methods as a substitute.Convolutional Neural Networks and SVMs are employed to identify cases of stellar contamination and cloudy contamination,achieving F1 scores of 1.00 and 0.98 on a test set,respectively.We also explore other machine learning methods such as ResNet-18 and Light Gradient Boosting Machine,then conduct comparative analyses of the results.展开更多
To address the problem of real-time processing of ultra-wide bandwidth pulsar baseband data,we designed and implemented a pulsar baseband data processing algorithm(PSRDP)based on GPU parallel computing technology.PSRD...To address the problem of real-time processing of ultra-wide bandwidth pulsar baseband data,we designed and implemented a pulsar baseband data processing algorithm(PSRDP)based on GPU parallel computing technology.PSRDP can perform operations such as baseband data unpacking,channel separation,coherent dedispersion,Stokes detection,phase and folding period prediction,and folding integration in GPU clusters.We tested the algorithm using the J0437-4715 pulsar baseband data generated by the CASPSR and Medusa backends of the Parkes,and the J0332+5434 pulsar baseband data generated by the self-developed backend of the Nan Shan Radio Telescope.We obtained the pulse profiles of each baseband data.Through experimental analysis,we have found that the pulse profiles generated by the PSRDP algorithm in this paper are essentially consistent with the processing results of Digital Signal Processing Software for Pulsar Astronomy(DSPSR),which verified the effectiveness of the PSRDP algorithm.Furthermore,using the same baseband data,we compared the processing speed of PSRDP with DSPSR,and the results showed that PSRDP was not slower than DSPSR in terms of speed.The theoretical and technical experience gained from the PSRDP algorithm research in this article lays a technical foundation for the real-time processing of QTT(Qi Tai radio Telescope)ultra-wide bandwidth pulsar baseband data.展开更多
In the task of classifying massive celestial data,the accurate classification of galaxies,stars,and quasars usually relies on spectral labels.However,spectral data account for only a small fraction of all astronomical...In the task of classifying massive celestial data,the accurate classification of galaxies,stars,and quasars usually relies on spectral labels.However,spectral data account for only a small fraction of all astronomical observation data,and the target source classification information in vast photometric data has not been accurately measured.To address this,we propose a novel deep learning-based algorithm,YL8C4Net,for the automatic detection and classification of target sources in photometric images.This algorithm combines the YOLOv8 detection network with the Conv4Net classification network.Additionally,we propose a novel magnitude-based labeling method for target source annotation.In the performance evaluation,the YOLOv8 achieves impressive performance with average precision scores of 0.824 for AP@0.5 and 0.795 for AP@0.5:0.95.Meanwhile,the constructed Conv4Net attains an accuracy of 0.8895.Overall,YL8C4Net offers the advantages of fewer parameters,faster processing speed,and higher classification accuracy,making it particularly suitable for large-scale data processing tasks.Furthermore,we employed the YL8C4Net model to conduct target source detection and classification on photometric images from 20 sky regions in SDSS-DR17.As a result,a catalog containing about 9.39 million target source classification results has been preliminarily constructed,thereby providing valuable reference data for astronomical research.展开更多
The Mini-SiTian(MST)project is a pathfinder for China's next-generation large-scale time-domain survey,SiTian,aimed at discovering variable stars,transients,and explosive events.MST generates hundreds of thousands...The Mini-SiTian(MST)project is a pathfinder for China's next-generation large-scale time-domain survey,SiTian,aimed at discovering variable stars,transients,and explosive events.MST generates hundreds of thousands of transient alerts every night,approximately 99%of which are false alarms,posing a significant challenge to its scientific goals.To mitigate the impact of false positives,we propose a deep learning–based solution and systematically evaluate 13 convolutional neural networks.The results show that ResNet achieves exceptional specificity(99.70%),EfficientNet achieves the highest recall rate(98.68%),and DenseNet provides balanced performance with a recall rate of 94.55%and specificity of 98.66%.Leveraging these complementary strengths,we developed a bagging-based ensemble classifier that integrates ResNet18,DenseNet121,and EfficientNet_B0 using a soft voting strategy.This classifier achieved the best AUC value(0.9961)among all models,with a recall rate of95.37%and specificity of 99.25%.It has now been successfully deployed in the MST real-time data processing pipeline.Validation using 5000 practically processed samples with a classification threshold of 0.798 showed that the classifier achieved 88.31%accuracy,91.89%recall rate,and 99.82%specificity,confirming its effectiveness and robustness under real application conditions.展开更多
Slitless spectroscopy onboard space telescopes is a powerful tool to detect emission-line objects such as emissionline galaxies(ELGs)and quasars.In this work,we present a study of ELGs observed with slitless spectrosc...Slitless spectroscopy onboard space telescopes is a powerful tool to detect emission-line objects such as emissionline galaxies(ELGs)and quasars.In this work,we present a study of ELGs observed with slitless spectroscopy by the Hubble Space Telescope(HST)in a deep field of~44 arcmin^(2).This is one of the deepest HST fields with a wealth of imaging and spectral data.In particular,previous VLT/MUSE observations have covered this field and identified a large number of ELGs.We reduce the HST spectra using the latest pipeline with a forward modeling algorithm and construct a sample of ELGs.By comparing with the MUSE spectra,we characterize our ELG detection in the HST spectra,including the impact of the line flux,line width,signal-to-noise ratio,etc.We find that the morphological broadening may affect the detection of ELGs,such that more compact sources are easier to be detected in slitless spectra.We discuss its implications to future slitless spectroscopic surveys that will be carried out by the China Space Station Telescope(CSST)and find that the CSST slitless spectroscopy has a capability comparable to that of HST in terms of the detection of emission lines.展开更多
As a pathfinder of the SiTian project,the Mini-SiTian(MST)Array,employed three commercial CMOS cameras,represents a next-generation,cost-effective optical time-domain survey project.This paper focuses primarily on the...As a pathfinder of the SiTian project,the Mini-SiTian(MST)Array,employed three commercial CMOS cameras,represents a next-generation,cost-effective optical time-domain survey project.This paper focuses primarily on the precise data processing pipeline designed for wide-field,CMOS-based devices,including the removal of instrumental effects,astrometry,photometry,and flux calibration.When applying this pipeline to approximately3000 observations taken in the Field 02(f02)region by MST,the results demonstrate a remarkable astrometric precision of approximately 70–80 mas(about 0.1 pixel),an impressive calibration accuracy of approximately1 mmag in the MST zero points,and a photometric accuracy of about 4 mmag for bright stars.Our studies demonstrate that MST CMOS can achieve photometric accuracy comparable to that of CCDs,highlighting the feasibility of large-scale CMOS-based optical time-domain surveys and their potential applications for cost optimization in future large-scale time-domain surveys,like the SiTian project.展开更多
Ancient stellar observations are a valuable cultural heritage,profoundly influencing both cultural domains and modern astronomical research.Shi’s Star Catalog(石氏星经),the oldest extant star catalog in China,faces c...Ancient stellar observations are a valuable cultural heritage,profoundly influencing both cultural domains and modern astronomical research.Shi’s Star Catalog(石氏星经),the oldest extant star catalog in China,faces controversy regarding its observational epoch.Determining this epoch via precession assumes accurate ancient coordinates and correspondence with contemporary stars,posing significant challenges.This study introduces a novel method using the Generalized Hough Transform to ascertain the catalog’s observational epoch.This approach statistically accommodates errors in ancient coordinates and discrepancies between ancient and modern stars,addressing limitations in prior methods.Our findings date Shi’s Star Catalog to the 4th century BCE,with 2nd-century CE adjustments.In comparison,the Western tradition’s oldest known catalog,the Ptolemaic Star Catalog(2nd century CE),likely derives from the Hipparchus Star Catalog(2nd century BCE).Thus,Shi’s Star Catalog is identified as the world’s oldest known star catalog.Beyond establishing its observation period,this study aims to consolidate and digitize these cultural artifacts.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
Clock difference between the ensemble pulsar timescale(PT)and the International Atomic Time(TAI)PT-TAI derived from the International Pulsar Timing Array(IPTA)data set indicates a very similar variation trend with the...Clock difference between the ensemble pulsar timescale(PT)and the International Atomic Time(TAI)PT-TAI derived from the International Pulsar Timing Array(IPTA)data set indicates a very similar variation trend with the Terrestrial Time TT(BIPMXXXX)-TAI but PT has larger measurement error.In this paper,we discuss the smoothing method of PT using a combined smoothing filter and compare the results with that from other filters.The clock difference sequence between PT-TAI and the first time derivative series of the TT(BIPMXXXX)-TAI can be combined by a combined smoothing filter to yield two smooth curves tied by the constraints assuring that the latter is the derivative of the former.The ensemble pulsar time IPTA2016 with respect to TAI published by G.Hobbs et al.and first time derivative series of the TT(BIPM2017)-TAI with quadratic polynomial terms removed are processed by combined smoothing filter in order to demonstrate the properties of the smoothed results.How to correctly estimate two smoothing coefficients is described and the output results of the combined smoothing filter are analyzed.The results show that the combined smoothing method efficiently removes high frequency noises of two input data series and the smoothed data of the PT-TAI combine long term fractional frequency stability of the pulsar time and frequency accuracy of the terrestrial time.Fractional frequency stability analysis indicates that both short and medium time interval stability of the smoothed PT-TAI is improved while keeping its original long term frequency stability level.The combined smoothing filter is more suitable for smoothing observational pulsar timescale data than any filter that only performs smoothing of a single pulsar time series.The smoothed pulsar time by combined smoothing filter is a pulsar atomic time combined timescale.This kind of combined timescale can also be used as terrestrial time.展开更多
As massive amounts of image data are generated by large-scale sky survey projects, the importance of research on the morphological classification of galaxy images is growing increasingly. Deep learning, with the capab...As massive amounts of image data are generated by large-scale sky survey projects, the importance of research on the morphological classification of galaxy images is growing increasingly. Deep learning, with the capability of automatic feature extraction, exhibits remarkable performance in image classification algorithms. In the past, most of the excellent algorithm models proposed by astronomers focused on the classification of major categories and often ignored the subtle differences between galaxy categories. For this purpose, based on the DenseNet-121model, this paper attempts to introduce a variety of improvement strategies such as dynamic multi-scale convolution, learnable grouped convolution, and the squeeze-and-excitation module to optimize the performance of the model. After numerous exhaustive experimental comparisons, the DenseNet-SE4 network with excellent performance is proposed. Subsequently, we conduct comparative experiments between this network and multiple advanced convolutional models on the data set consisting of Galaxy10 DECaLS and GZD-5. We select the data of eight galaxy categories with similar morphologies, such as round smooth galaxies and barred spiral galaxies, to comprehensively test the classification ability of the model. The experimental results illustrate that the DenseNetSE4 network achieves an accuracy of 88.96%, a precision of 89.00%, a recall rate of 89.44%, and an F1-score of 89.21% on the test set, thus it has reached the highest level among the eight comparison algorithms. Moreover, the model was tested on data within different redshift intervals, demonstrating good robustness. Finally, the visualization method was employed to further validate the effectiveness and rationality of the DenseNet-SE4 network.展开更多
The Square Kilometre Array(SKA)has the potential to revolutionize astronomical research through its unparalleled precision.A critical aspect of SKA imaging is the computation of the UVW coordinates,which must be accur...The Square Kilometre Array(SKA)has the potential to revolutionize astronomical research through its unparalleled precision.A critical aspect of SKA imaging is the computation of the UVW coordinates,which must be accurate and reliable for the development of the SKA scientific data processor.Katpoint is the current method used to calculate UVW in Meer KAT.Using a pseudo-source,we employ a simple cross-product method to determine UVWs.In this study,we explore the applicability of Katpoint for SKA1-low and SKA1-mid and evaluate its precision.The conventional method,CALC/Omni UV,and Katpoint were quantitatively assessed through simulations.The results indicate that Katpoint exhibits substantial accuracy with MeerKAT compared to traditional techniques.However,its precision is slightly inadequate for the long baselines of SKA1.We improved the precision of Katpoint by identifying optimal offset values for pseudo-sources on the SKA1 telescope through simulation,finding a 0°.11 offset suitable for SKA1-Mid and a 0°.045 offset for SKA1-Low.Final result validations demonstrate that these adjustments render the computational accuracy fully comparable to the standard CALC/Omni UV method,which would meet the requirements of SKA high-precision imaging and offer a solution for high-precision imaging in radio interferometers.展开更多
We present a wideband polarization analysis of the mode-changing pulsar PSR J1938+2213 using the ultra-wideband low-frequency receiver on Murriyang,the Parkes 64 m radio telescope.Polarization profiles for both the bu...We present a wideband polarization analysis of the mode-changing pulsar PSR J1938+2213 using the ultra-wideband low-frequency receiver on Murriyang,the Parkes 64 m radio telescope.Polarization profiles for both the burst and weak emission modes are obtained.We find that the pulse widths of the two modes exhibit distinct frequency dependencies:the pulse width increases with frequency in the burst mode,but decreases in the weak mode.The linear and circular polarization fractions also show different trends with frequency between the two modes.Our spectral analysis shows that both modes follow power-law spectra,but with differing spectral indices.展开更多
The 21 cm radiation of neutral hydrogen provides crucial information for studying the early universe and its evolution.To advance this research,countries have made significant investments in constructing large lowfreq...The 21 cm radiation of neutral hydrogen provides crucial information for studying the early universe and its evolution.To advance this research,countries have made significant investments in constructing large lowfrequency radio telescope arrays,such as the Low Frequency Array and the Square Kilometre Array Phase 1 Low Frequency.These instruments are pivotal for radio astronomy research.However,challenges such as ionospheric plasma interference,ambient radio noise,and instrument-related effects have become increasingly prominent,posing major obstacles in cosmology research.To address these issues,this paper proposes an efficient signal processing method that combines wavelet transform and mathematical morphology.The method involves the following steps:Background Subtraction:Background interference in radio observation signals is eliminated.Wavelet Transform:The signal,after removing background noise,undergoes a two-dimensional discrete wavelet transform.Threshold processing is then applied to the wavelet coefficients to effectively remove interference components.Wavelet Inversion:The processed signal is reconstructed using wavelet inversion.Mathematical Morphology:The reconstructed signal is further optimized using mathematical morphology to refine the results.Experimental verification was conducted using solar observation data from the Xinjiang Observatory and the Yunnan Observatory.The results demonstrate that this method successfully removes interference signals while preserving useful signals,thus improving the accuracy of radio astronomy observations and reducing the impact of radio frequency interference.展开更多
基金funded by the National Natural Science Foundation of China(NSFC)the Chinese Academy of Sciences(CAS)(grant No.U2031209)the National Natural Science Foundation of China(NSFC,grant Nos.11872128,42174192,and 91952111)。
文摘Seeing is an important index to evaluate the quality of an astronomical site.To estimate seeing at the Muztagh-Ata site with height and time quantitatively,the European Centre for Medium-Range Weather Forecasts reanalysis database(ERA5)is used.Seeing calculated from ERA5 is compared consistently with the Differential Image Motion Monitor seeing at the height of 12 m.Results show that seeing decays exponentially with height at the Muztagh-Ata site.Seeing decays the fastest in fall in 2021 and most slowly with height in summer.The seeing condition is better in fall than in summer.The median value of seeing at 12 m is 0.89 arcsec,the maximum value is1.21 arcsec in August and the minimum is 0.66 arcsec in October.The median value of seeing at 12 m is 0.72arcsec in the nighttime and 1.08 arcsec in the daytime.Seeing is a combination of annual and about biannual variations with the same phase as temperature and wind speed indicating that seeing variation with time is influenced by temperature and wind speed.The Richardson number Ri is used to analyze the atmospheric stability and the variations of seeing are consistent with Ri between layers.These quantitative results can provide an important reference for a telescopic observation strategy.
文摘Lunar wrinkle ridges are an important stress geological structure on the Moon, which reflect the stress state and geological activity on the Moon. They provide important insights into the evolution of the Moon and are key factors influencing future lunar activity, such as the choice of landing sites. However, automatic extraction of lunar wrinkle ridges is a challenging task due to their complex morphology and ambiguous features. Traditional manual extraction methods are time-consuming and labor-intensive. To achieve automated and detailed detection of lunar wrinkle ridges, we have constructed a lunar wrinkle ridge data set, incorporating previously unused aspect data to provide edge information, and proposed a Dual-Branch Ridge Detection Network(DBR-Net) based on deep learning technology. This method employs a dual-branch architecture and an Attention Complementary Feature Fusion module to address the issue of insufficient lunar wrinkle ridge features. Through comparisons with the results of various deep learning approaches, it is demonstrated that the proposed method exhibits superior detection performance. Furthermore, the trained model was applied to lunar mare regions, generating a distribution map of lunar mare wrinkle ridges;a significant linear relationship between the length and area of the lunar wrinkle ridges was obtained through statistical analysis, and six previously unrecorded potential lunar wrinkle ridges were detected. The proposed method upgrades the automated extraction of lunar wrinkle ridges to a pixel-level precision and verifies the effectiveness of DBR-Net in lunar wrinkle ridge detection.
基金supported by the National Natural Science Foundation of China under grant No.U2031144partially supported by the Open Project Program of the Key Laboratory of Optical Astronomy,National Astronomical Observatories,Chinese Academy of Sciences+5 种基金supported by the National Key R&D Program of China with No.2021YFA1600404the National Natural Science Foundation of China(12173082)the Yunnan Fundamental Research Projects(grant 202201AT070069)the Top-notch Young Talents Program of Yunnan Provincethe Light of West China Program provided by the Chinese Academy of Sciencesthe International Centre of Supernovae,Yunnan Key Laboratory(No.202302AN360001)。
文摘BFOSC and YFOSC are the most frequently used instruments in the Xinglong 2.16 m telescope and Lijiang 2.4 m telescope,respectively.We developed a software package named“BYSpec”(BFOSC and YFOSC Spectra Reduction Package)dedicated to automatically reducing the long-slit and echelle spectra obtained by these two instruments.The package supports bias and flat-fielding correction,order location,background subtraction,automatic wavelength calibration,and absolute flux calibration.The optimal extraction method maximizes the signal-to-noise ratio and removes most of the cosmic rays imprinted in the spectra.A comparison with the 1D spectra reduced with IRAF verifies the reliability of the results.This open-source software is publicly available to the community.
基金We are grateful to the support from the National Natural Science Foundation of China （NSFC） （31425002, 91231205, 81430011, 61303161, 31470220, and 31327001）, and the Frontier Science Research Program, the Soil-Microbe System Function and Regulation Program, and the Science and Technology Service Network Initiative （STS） from the Chinese Academy of Sciences （CAS）.
文摘Method development has always been and will continue to be a core driving force of microbiome science, In this perspective, we argue that in the next decade, method development in microbiome analysis will be driven by three key changes in both ways of thinking and technological platforms： ① a shift from dissecting microbiota structure by sequencing to tracking microbiota state, function, and intercellular interaction via imaging; ② a shift from interrogating a consortium or population of cells to probing individual cells; and ③a shift from microbiome data analysis to microbiome data science. Some of the recent methoddevelopment efforts by Chinese microbiome scientists and their international collaborators that underlie these technological trends are highlighted here. It is our belief that the China Microbiome Initiative has the opportunity to deliver outstanding ＂Made-in-China＂ tools to the international research community, by building an ambitious, competitive, and collaborative program at the forefront of method development for microbiome science.
基金supported by the National Key R&D Program of China(2017YFA0402701)Key Research Program of Frontier Sciences of CAS(QYZDJ-SSW-SLH047)partially supported by the National Natural Science Foundation of China(Grant No.U2031202)。
文摘Noise is a significant part within a millimeter-wave molecular line datacube.Analyzing the noise improves our understanding of noise characteristics,and further contributes to scientific discoveries.We measure the noise level of a single datacube from MWISP and perform statistical analyses.We identified major factors which increase the noise level of a single datacube,including bad channels,edge effects,baseline distortion and line contamination.Cleaning algorithms are applied to remove or reduce these noise components.As a result,we obtained the cleaned datacube in which noise follows a positively skewed normal distribution.We further analyzed the noise structure distribution of a 3 D mosaicked datacube in the range l=40°7 to 43°3 and b=-2°3 to 0°3 and found that noise in the final mosaicked datacube is mainly characterized by noise fluctuation among the cells.
基金supported by the National Natural Science Foundation of China(U1704134)。
文摘Very low frequency(VLF)signals are propagated between the ground-ionosphere.Multimode interference will cause the phase to show oscillatory changes with distance while propagating at night,leading to abnormalities in the received VLF signal.This study uses the VLF signal received in Qingdao City,Shandong Province,from the Russian Alpha navigation system to explore the multimode interference problem of VLF signal propagation.The characteristics of the effect of multimode interference phenomena on the phase are analyzed according to the variation of the phase of the VLF signal.However,the phase of VLF signals will also be affected by the X-ray and energetic particles that are released during the eruption of solar flares,therefore the two phenomena are studied in this work.It is concluded that the X-ray will not affect the phase of VLF signals at night,but the energetic particles will affect the phase change,and the influence of energetic particles should be excluded in the study of multimode interference phenomena.Using VLF signals for navigation positioning in degraded or unavailable GPS conditions is of great practical significance for VLF navigation systems as it can avoid the influence of multimode interference and improve positioning accuracy.
基金supported by the National Natural Science Foundation of China (Grant No. 11173038)
文摘The microwave radiometer （MRM） onboard the Chang＇ E-1 （CE-I） lu- nar orbiter is a 4-frequency microwave radiometer, and it is mainly used to obtain the brightness temperature （TB） of the lunar surface, from which the thickness, temperature, dielectric constant and other related properties of the lunar regolith can be derived. The working mode of the CE-1 MRM, the ground calibration （including the official calibration coefficients）, as well as the acquisition and processing of the raw data are introduced. Our data analysis shows that TB increases with increasing frequency, decreases towards the lunar poles and is significantly affected by solar illumination. Our analysis also reveals that the main uncertainty in TB comes from ground calibration.
基金funded by the National Natural Science Foundation of China(NSFC,Nos.12373086 and 12303082)CAS“Light of West China”Program+2 种基金Yunnan Revitalization Talent Support Program in Yunnan ProvinceNational Key R&D Program of ChinaGravitational Wave Detection Project No.2022YFC2203800。
文摘Attitude is one of the crucial parameters for space objects and plays a vital role in collision prediction and debris removal.Analyzing light curves to determine attitude is the most commonly used method.In photometric observations,outliers may exist in the obtained light curves due to various reasons.Therefore,preprocessing is required to remove these outliers to obtain high quality light curves.Through statistical analysis,the reasons leading to outliers can be categorized into two main types:first,the brightness of the object significantly increases due to the passage of a star nearby,referred to as“stellar contamination,”and second,the brightness markedly decreases due to cloudy cover,referred to as“cloudy contamination.”The traditional approach of manually inspecting images for contamination is time-consuming and labor-intensive.However,we propose the utilization of machine learning methods as a substitute.Convolutional Neural Networks and SVMs are employed to identify cases of stellar contamination and cloudy contamination,achieving F1 scores of 1.00 and 0.98 on a test set,respectively.We also explore other machine learning methods such as ResNet-18 and Light Gradient Boosting Machine,then conduct comparative analyses of the results.
基金supported by the National Key R&D Program of China Nos.2021YFC2203502 and 2022YFF0711502the National Natural Science Foundation of China(NSFC)(12173077 and 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To address the problem of real-time processing of ultra-wide bandwidth pulsar baseband data,we designed and implemented a pulsar baseband data processing algorithm(PSRDP)based on GPU parallel computing technology.PSRDP can perform operations such as baseband data unpacking,channel separation,coherent dedispersion,Stokes detection,phase and folding period prediction,and folding integration in GPU clusters.We tested the algorithm using the J0437-4715 pulsar baseband data generated by the CASPSR and Medusa backends of the Parkes,and the J0332+5434 pulsar baseband data generated by the self-developed backend of the Nan Shan Radio Telescope.We obtained the pulse profiles of each baseband data.Through experimental analysis,we have found that the pulse profiles generated by the PSRDP algorithm in this paper are essentially consistent with the processing results of Digital Signal Processing Software for Pulsar Astronomy(DSPSR),which verified the effectiveness of the PSRDP algorithm.Furthermore,using the same baseband data,we compared the processing speed of PSRDP with DSPSR,and the results showed that PSRDP was not slower than DSPSR in terms of speed.The theoretical and technical experience gained from the PSRDP algorithm research in this article lays a technical foundation for the real-time processing of QTT(Qi Tai radio Telescope)ultra-wide bandwidth pulsar baseband data.
基金supported by the National Natural Science Foundation of China (NSFC, Grant No. U1731128)
文摘In the task of classifying massive celestial data,the accurate classification of galaxies,stars,and quasars usually relies on spectral labels.However,spectral data account for only a small fraction of all astronomical observation data,and the target source classification information in vast photometric data has not been accurately measured.To address this,we propose a novel deep learning-based algorithm,YL8C4Net,for the automatic detection and classification of target sources in photometric images.This algorithm combines the YOLOv8 detection network with the Conv4Net classification network.Additionally,we propose a novel magnitude-based labeling method for target source annotation.In the performance evaluation,the YOLOv8 achieves impressive performance with average precision scores of 0.824 for AP@0.5 and 0.795 for AP@0.5:0.95.Meanwhile,the constructed Conv4Net attains an accuracy of 0.8895.Overall,YL8C4Net offers the advantages of fewer parameters,faster processing speed,and higher classification accuracy,making it particularly suitable for large-scale data processing tasks.Furthermore,we employed the YL8C4Net model to conduct target source detection and classification on photometric images from 20 sky regions in SDSS-DR17.As a result,a catalog containing about 9.39 million target source classification results has been preliminarily constructed,thereby providing valuable reference data for astronomical research.
基金supported by the National Key Basic R&D Program of China via 2023YFA1608303the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB0550103)the National Natural Science Foundation of China under grant Nos.12273076,12133001,12422303 and12261141690。
文摘The Mini-SiTian(MST)project is a pathfinder for China's next-generation large-scale time-domain survey,SiTian,aimed at discovering variable stars,transients,and explosive events.MST generates hundreds of thousands of transient alerts every night,approximately 99%of which are false alarms,posing a significant challenge to its scientific goals.To mitigate the impact of false positives,we propose a deep learning–based solution and systematically evaluate 13 convolutional neural networks.The results show that ResNet achieves exceptional specificity(99.70%),EfficientNet achieves the highest recall rate(98.68%),and DenseNet provides balanced performance with a recall rate of 94.55%and specificity of 98.66%.Leveraging these complementary strengths,we developed a bagging-based ensemble classifier that integrates ResNet18,DenseNet121,and EfficientNet_B0 using a soft voting strategy.This classifier achieved the best AUC value(0.9961)among all models,with a recall rate of95.37%and specificity of 99.25%.It has now been successfully deployed in the MST real-time data processing pipeline.Validation using 5000 practically processed samples with a classification threshold of 0.798 showed that the classifier achieved 88.31%accuracy,91.89%recall rate,and 99.82%specificity,confirming its effectiveness and robustness under real application conditions.
基金support from the National Key R&D Program of China(2022YFF0503401)the China Manned Space Project with No.CMS-CSST-2021-A05the National Natural Science Foundation of China(12225301)。
文摘Slitless spectroscopy onboard space telescopes is a powerful tool to detect emission-line objects such as emissionline galaxies(ELGs)and quasars.In this work,we present a study of ELGs observed with slitless spectroscopy by the Hubble Space Telescope(HST)in a deep field of~44 arcmin^(2).This is one of the deepest HST fields with a wealth of imaging and spectral data.In particular,previous VLT/MUSE observations have covered this field and identified a large number of ELGs.We reduce the HST spectra using the latest pipeline with a forward modeling algorithm and construct a sample of ELGs.By comparing with the MUSE spectra,we characterize our ELG detection in the HST spectra,including the impact of the line flux,line width,signal-to-noise ratio,etc.We find that the morphological broadening may affect the detection of ELGs,such that more compact sources are easier to be detected in slitless spectra.We discuss its implications to future slitless spectroscopic surveys that will be carried out by the China Space Station Telescope(CSST)and find that the CSST slitless spectroscopy has a capability comparable to that of HST in terms of the detection of emission lines.
基金supported by the National Key Basic R&D Program of China via 2023YFA1608303the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB0550103)+3 种基金the National Science Foundation of China 12422303,12403024,12222301,12173007,and 12261141690the Postdoctoral Fellowship Program of CPSF under grant Number GZB20240731the Young Data Scientist Project of the National Astronomical Data Center,and the China Post-doctoral Science Foundation No.2023M743447support from the NSFC through grant No.12303039 and No.12261141690.
文摘As a pathfinder of the SiTian project,the Mini-SiTian(MST)Array,employed three commercial CMOS cameras,represents a next-generation,cost-effective optical time-domain survey project.This paper focuses primarily on the precise data processing pipeline designed for wide-field,CMOS-based devices,including the removal of instrumental effects,astrometry,photometry,and flux calibration.When applying this pipeline to approximately3000 observations taken in the Field 02(f02)region by MST,the results demonstrate a remarkable astrometric precision of approximately 70–80 mas(about 0.1 pixel),an impressive calibration accuracy of approximately1 mmag in the MST zero points,and a photometric accuracy of about 4 mmag for bright stars.Our studies demonstrate that MST CMOS can achieve photometric accuracy comparable to that of CCDs,highlighting the feasibility of large-scale CMOS-based optical time-domain surveys and their potential applications for cost optimization in future large-scale time-domain surveys,like the SiTian project.
基金supported by China National Astronomical Data Center(NADC),CAS Astronomical Data Center and Chinese Virtual Observatory(China-VO)supported by Astronomical Big Data Joint Research Center,co-founded by National Astronomical Observatories,Chinese Academy of Sciences and Alibaba Cloud。
文摘Ancient stellar observations are a valuable cultural heritage,profoundly influencing both cultural domains and modern astronomical research.Shi’s Star Catalog(石氏星经),the oldest extant star catalog in China,faces controversy regarding its observational epoch.Determining this epoch via precession assumes accurate ancient coordinates and correspondence with contemporary stars,posing significant challenges.This study introduces a novel method using the Generalized Hough Transform to ascertain the catalog’s observational epoch.This approach statistically accommodates errors in ancient coordinates and discrepancies between ancient and modern stars,addressing limitations in prior methods.Our findings date Shi’s Star Catalog to the 4th century BCE,with 2nd-century CE adjustments.In comparison,the Western tradition’s oldest known catalog,the Ptolemaic Star Catalog(2nd century CE),likely derives from the Hipparchus Star Catalog(2nd century BCE).Thus,Shi’s Star Catalog is identified as the world’s oldest known star catalog.Beyond establishing its observation period,this study aims to consolidate and digitize these cultural artifacts.
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 Strategic Priority Research Program of Chinese Academy of Sciences(grant No.XDA0350502)the National SKA Program of China(grant No.2020SKA0120103)the National Natural Science Foundation of China(NSFC,Grant Nos.U1831130 and 11973046)。
文摘Clock difference between the ensemble pulsar timescale(PT)and the International Atomic Time(TAI)PT-TAI derived from the International Pulsar Timing Array(IPTA)data set indicates a very similar variation trend with the Terrestrial Time TT(BIPMXXXX)-TAI but PT has larger measurement error.In this paper,we discuss the smoothing method of PT using a combined smoothing filter and compare the results with that from other filters.The clock difference sequence between PT-TAI and the first time derivative series of the TT(BIPMXXXX)-TAI can be combined by a combined smoothing filter to yield two smooth curves tied by the constraints assuring that the latter is the derivative of the former.The ensemble pulsar time IPTA2016 with respect to TAI published by G.Hobbs et al.and first time derivative series of the TT(BIPM2017)-TAI with quadratic polynomial terms removed are processed by combined smoothing filter in order to demonstrate the properties of the smoothed results.How to correctly estimate two smoothing coefficients is described and the output results of the combined smoothing filter are analyzed.The results show that the combined smoothing method efficiently removes high frequency noises of two input data series and the smoothed data of the PT-TAI combine long term fractional frequency stability of the pulsar time and frequency accuracy of the terrestrial time.Fractional frequency stability analysis indicates that both short and medium time interval stability of the smoothed PT-TAI is improved while keeping its original long term frequency stability level.The combined smoothing filter is more suitable for smoothing observational pulsar timescale data than any filter that only performs smoothing of a single pulsar time series.The smoothed pulsar time by combined smoothing filter is a pulsar atomic time combined timescale.This kind of combined timescale can also be used as terrestrial time.
基金supported by the National Natural Science Foundation of China(NSFC,grant No.U1731128).
文摘As massive amounts of image data are generated by large-scale sky survey projects, the importance of research on the morphological classification of galaxy images is growing increasingly. Deep learning, with the capability of automatic feature extraction, exhibits remarkable performance in image classification algorithms. In the past, most of the excellent algorithm models proposed by astronomers focused on the classification of major categories and often ignored the subtle differences between galaxy categories. For this purpose, based on the DenseNet-121model, this paper attempts to introduce a variety of improvement strategies such as dynamic multi-scale convolution, learnable grouped convolution, and the squeeze-and-excitation module to optimize the performance of the model. After numerous exhaustive experimental comparisons, the DenseNet-SE4 network with excellent performance is proposed. Subsequently, we conduct comparative experiments between this network and multiple advanced convolutional models on the data set consisting of Galaxy10 DECaLS and GZD-5. We select the data of eight galaxy categories with similar morphologies, such as round smooth galaxies and barred spiral galaxies, to comprehensively test the classification ability of the model. The experimental results illustrate that the DenseNetSE4 network achieves an accuracy of 88.96%, a precision of 89.00%, a recall rate of 89.44%, and an F1-score of 89.21% on the test set, thus it has reached the highest level among the eight comparison algorithms. Moreover, the model was tested on data within different redshift intervals, demonstrating good robustness. Finally, the visualization method was employed to further validate the effectiveness and rationality of the DenseNet-SE4 network.
基金supported by the China National SKA Programme(2020SKA0110300)the National Natural Science Foundation of China(NSFC,Grant Nos.12433012 and 12373097)the Guangzhou Science and Technology Funds(2023A03J0016)。
文摘The Square Kilometre Array(SKA)has the potential to revolutionize astronomical research through its unparalleled precision.A critical aspect of SKA imaging is the computation of the UVW coordinates,which must be accurate and reliable for the development of the SKA scientific data processor.Katpoint is the current method used to calculate UVW in Meer KAT.Using a pseudo-source,we employ a simple cross-product method to determine UVWs.In this study,we explore the applicability of Katpoint for SKA1-low and SKA1-mid and evaluate its precision.The conventional method,CALC/Omni UV,and Katpoint were quantitatively assessed through simulations.The results indicate that Katpoint exhibits substantial accuracy with MeerKAT compared to traditional techniques.However,its precision is slightly inadequate for the long baselines of SKA1.We improved the precision of Katpoint by identifying optimal offset values for pseudo-sources on the SKA1 telescope through simulation,finding a 0°.11 offset suitable for SKA1-Mid and a 0°.045 offset for SKA1-Low.Final result validations demonstrate that these adjustments render the computational accuracy fully comparable to the standard CALC/Omni UV method,which would meet the requirements of SKA high-precision imaging and offer a solution for high-precision imaging in radio interferometers.
基金supported by the National Natural Science Foundation of China(Nos.12288102,12203092,12041304,12403060,12203045,12203093,12163001 and 12463007)the Major Science and Technology Program of Xinjiang Uygur Autonomous Region(Nos.2022A03013-3,and 2022A03013-2)+7 种基金the National SKA Program of China(No.2020SKA0120100)the National Key Research and Development Program of China(Nos.2022YFC2205202 and 2021YFC2203502)the Natural Science Foundation of Xinjiang Uygur Autonomous Region(Nos.2022D01B71 and 2022D01B218)the Tianshan Talent Training Program for Young Elite Scientists(No.2023TSYCQNTJ0024)the 2022 Project Xinjiang Uygur Autonomous Region of China for Tianchi Talents,the open research project funded by the Key Laboratory of Xinjiang Uyghur Autonomous Region(No.2021000059)the National Key Research and Development Program(No.2022YFA1603104)The research is partly supported by 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)Murriyang,CSIRO’s Parkes radio telescope,is part of the Australia Telescope National Facility(https://ror.org/05qajvd42)which is funded by the Australian Government for operation as a National Facility managed by CSIRO.
文摘We present a wideband polarization analysis of the mode-changing pulsar PSR J1938+2213 using the ultra-wideband low-frequency receiver on Murriyang,the Parkes 64 m radio telescope.Polarization profiles for both the burst and weak emission modes are obtained.We find that the pulse widths of the two modes exhibit distinct frequency dependencies:the pulse width increases with frequency in the burst mode,but decreases in the weak mode.The linear and circular polarization fractions also show different trends with frequency between the two modes.Our spectral analysis shows that both modes follow power-law spectra,but with differing spectral indices.
基金funded by the National Key Research and Development Program’s intergovernmental International Science and Technology Innovation Cooperation project,titled Remote Sensing and Radio Astronomy Observation of Space Weather in Low and Middle Latitudes(project number:2022YFE0140000)Supported by International Partnership Program of Chinese Academy of Sciences,grant No.114A11KYSB20200001。
文摘The 21 cm radiation of neutral hydrogen provides crucial information for studying the early universe and its evolution.To advance this research,countries have made significant investments in constructing large lowfrequency radio telescope arrays,such as the Low Frequency Array and the Square Kilometre Array Phase 1 Low Frequency.These instruments are pivotal for radio astronomy research.However,challenges such as ionospheric plasma interference,ambient radio noise,and instrument-related effects have become increasingly prominent,posing major obstacles in cosmology research.To address these issues,this paper proposes an efficient signal processing method that combines wavelet transform and mathematical morphology.The method involves the following steps:Background Subtraction:Background interference in radio observation signals is eliminated.Wavelet Transform:The signal,after removing background noise,undergoes a two-dimensional discrete wavelet transform.Threshold processing is then applied to the wavelet coefficients to effectively remove interference components.Wavelet Inversion:The processed signal is reconstructed using wavelet inversion.Mathematical Morphology:The reconstructed signal is further optimized using mathematical morphology to refine the results.Experimental verification was conducted using solar observation data from the Xinjiang Observatory and the Yunnan Observatory.The results demonstrate that this method successfully removes interference signals while preserving useful signals,thus improving the accuracy of radio astronomy observations and reducing the impact of radio frequency interference.
