Synthetic aperture radar(SAR)is an active microwave imaging sensor that enables day-and-night,all-weather Earth observation.When integrated on unmanned aerial vehicles(UAVs),SAR provides high-resolution imaging with small size,low cost,and high maneuverability,while shortening deployment and data turnaround.Focusing on large astronomical facilities,this paper presents the design and validation of a Ka-band UAV-borne phased-array SAR system.The payload adopts a modular,lightweight architecture compatible with multirotor,fixed-wing,and compound-wing airframes.The short wavelength of Ka-band millimeter-wave signals enhances sensitivity to fine linear features and complex metallic structures,making it well suited for precise geometric mapping,array-layout calibration,structural health monitoring,and perimeter inspection of astronomical facilities.We conducted multiple day-and-night flight experiments at the Mingantu Observing Station and acquired high-resolution two-dimensional SAR images of the Chinese Meridian Project Phase II interplanetary scintillation phased-array telescope and surrounding infrastructure.Results exhibit generally well-focused impulse responses with clear mainlobes and controlled sidelobes,supported by quantitative metrics(e.g.,resolution,peak sidelobe ratio,and integrated sidelobe ratio).These results indicate that the Ka-band UAV-borne SAR enables rapid,fine-grained,and repeatable monitoring of large astronomical equipment and sites.The system also shows promise for broader applications,including geohazard assessment,mining subsidence,major civil-infrastructure monitoring,power-line and pipeline inspection,and environmental monitoring.
The Accurate Infrared Magnetic Field Measurements of the Sun(AIMS)project requires a root-mean-square tracking accuracy of 1″ per 30 min to observe targets on the solar disk.Since the terminal scientific instruments in AIMS are positioned at the Coudé focus,the telescope and guiding optical path introduce slowly varying nonlinear tracking errors during solar tracking,which degrade long-duration tracking accuracy.To address this issue,we first analyze the mechanisms and characteristics of these slowly varying tracking errors at the Coudé focus,based on the optical-mechanical structure of AIMS and motion principles,and conduct actual measurements of the tracking errors.We have designed a closed-loop correction scheme in conjunction with the optical-mechanical mechanisms of AIMS,detailing both hardware and software implementations.Following over a year of testing and trial use on AIMS,results demonstrate that the system can stably perform closed-loop tracking of solar observation targets for over 30 min,with a tracking error of less than 1.0″.Both the tracking duration and error value meet tracking accuracy requirements.
The High Frequency(HF,3-30 MHz)to Very High Frequency(VHF,30-300 MHz)band is a critical observational window in radio astronomy,playing a key role in the study of early-universe reionization,space weather monitoring,and solar physics.We determine whether a genetic algorithm-optimized sparse configuration of a 64-element planar radio antenna array can minimize the peak sidelobe level and enhance performance within the 10-90 MHz frequency range,compared with a regular configuration.The sparse-optimized array achieves a 1.04 dB reduction in peak sidelobe level across the frequency band compared with the regular array.Sensitivity improves significantly at all frequency points,with increases of up to 56%at 10 MHz and 45%at 50 MHz.At 90 MHz,the sensitivity matches that of the regular array.At three representative frequencies(50 MHz,60 MHz,and 70 MHz),grating lobe suppression tests at different scan angles show that the regular array shows prominent grating lobes at specific scan angles(θ=53° at 50 MHz,θ=30° at 60 MHz,and θ=15° at 70 MHz).By contrast,the sparse array shows no observable grating lobes,confirming its superior suppression capability.At wide bandwidths,a sparse array optimized with a genetic algorithm outperforms a regular array in peak sidelobe level,sensitivity,and scanning range,supporting its use as a better technical solution for radio astronomical observations.
Universal time(UT)1 is an alternative description of the Earth’s rotation angle and is one of the spatial parameters representing the Earth’s orientation that reflects subtle changes in its rotational speed.While very long baseline interferometry(VLBI)has recently achieved high-precision measurements of UT1,its prohibitively high equipment costs and complex data processes make it difficult to meet the requirements of users in fields with stringent real-time data requirements,such as astronomical measurement and celestial navigation.Currently,the digital zenith telescope is one of the most accurate ground-based optical astronomical measurement instruments available.This study briefly introduces the digital zenith telescope measurement system and the basic principles of UT1 measurement and data processing.On the basis of more than 400 UT1 measurement experiments conducted at Luonan,Lijiang,and Delingha,the accuracy of UT1 measurements based on the digital zenith telescope is analyzed.The experimental results show that the internal consistency accuracy within 20 min can reach 10 ms and that the internal consistency accuracy of single-day observations can reach 0.05″.Compared with IERS 14C04,the mean absolute error of the UT1 measurements is approximately 3 ms.This indicates that optical astronomical observations based on the digital zenith telescope can be used as an effective regional autonomous monitoring method to supplement VLBI by providing high-frequency UT1,functioning in particular as an emergency backup when satellite navigation fails.
In Global Navigation Satellite Systems(GNSS),accurate and stable atomic clocks need to be equipped on satellites to ensure reliable,high-accuracy positioning,navigation,and timing services.It is essential to continuously monitor the behavior of satellite clocks in space and predict satellite clock corrections for real-time GNSS applications,especially for precise point positioning.Some commercial software is available for clock characterization,but special attention has to be paid when referring to satellite clocks,the analysis and prediction of which may be complicated by outliers,data gaps,and periodic fluctuations in onboard clock data,not often encountered by clock data from a timekeeping laboratory.The typical approaches for clock characterization and prediction currently employed in a timekeeping laboratory are therefore unsuitable for clock applications in space.We present a software package developed in MATLAB at the National Time Service Center,Chinese Academy of Sciences,intended for satellite clock characterization and prediction.The software package includes many subroutines and functionalities of particular interest in characterizing and predicting clock behavior in space,such as dynamic frequency stability evaluation,periodic fluctuation analysis,and multi-step prediction of clock signals.The software package allows handling of satellite clock data directly from Receiver Independent Exchange Format clock files widely used in GNSS,facilitating quick characterization analysis and prediction of satellite clocks,with graphically visualized output.
The open-source Collaboration for Astronomy Signal Processing and Electronics Research(CASPER)toolflow has become a popular choice for building reconfigurable digital backends in radio astronomy.We extend this toolflow to the third-party TQ47DR Radio-Frequency System-on-Chip(RFSoC)platform,a cost-effective and widely available board.Our implementation includes a custom PetaLinux system,a lightweight control server,a deterministic clock-management driver,and platform-specific yellow-block adaptations that expose the on-chip data converters and 100-Gigabit Ethernet interfaces.System validation demonstrates high-quality converter performance,stable packet streaming,and real-time spectrometry,confirming that such third-party hardware can be integrated seamlessly into this open-source programming ecosystem for next-generation instruments.
This study aims to enhance the thermal management of phased array receiver systems used in radio astronomy by optimizing the nitrogen-based cooling mechanism.A numerical simulation approach is used to evaluate the influence of various structural and flow parameters,including the dimensions,quantity,configuration,and shape of the inlets and outlets and the nitrogen flow rate,on the cooling performance.A computational model is developed for a phased array receiver system comprising 64 groups of low-noise amplifiers,totaling 128 units.Computational fluid dynamics is used to analyze the thermal behavior across the system.The results indicate that moderate nitrogen flow velocities(ranging from 2.5 m s-1 to 3.0 m s-1)and an intermediate inlet radius(50-70 mm)offer the most efficient and uniform cooling.Additionally,a dual-inlet,dual-outlet configuration enhances the overall temperature distribution,while square inlets,which provide the same area as circular ones,improve the localized cooling performance.The optimized configuration substantially reduces thermal non-uniformity and supports stable operation under low-temperature conditions.The proposed nitrogen cooling design and configuration strategies present a practical and energy-efficient alternative to traditional cryogenic systems for phased array receiver systems.These findings provide valuable insights for future large-scale implementations in radio astronomy applications.
The Space-based multi-band astronomical Variable Objects Monitor(SVOM)mission requires a scalable and efficient data processing and management system to support multi-band data processing,storage,sharing,and operational monitoring.Key technologies developed to meet mission requirements include a multi-station Level 0 data fusion algorithm,a data quality assessment algorithm based on orbit and observation number,and a Level 1A science data product processing algorithm for the Gamma Ray Monitor payload.Adopting a layered and modular architecture,the system enables end-to-end automated processing,distribution,archiving,and publication of satellite downlink data across four transmission channels(X-band,S-band,very high frequency,and BeiDou).In-orbit operational validation shows that the system achieves excellent performance in stability,high throughput,and robust adaptability to mission dynamics.
The sensitivity of a phased array receiver can be enhanced by cryogenically cooling either its low-noise amplifiers or its front-end antenna array.This study addresses the cryogenic cooling requirements of phased array receivers.Based on a laboratory-based prototype broadband Vivaldi antenna array,we have designed a Dewar system adopting a full-array integrated cooling approach.Through thermal load analysis of the internal cryogenic structure and comparison with measured cooling temperatures from a prototype,we have validated a structural design while identifying areas for improvement.This work provides valuable insights for future integrated cooling and sensitivity optimization of phased array receivers equipped with cryogenic low-noise amplifiers.
Radio astronomy is crucial for understanding the origin,structure,and evolution of the universe,and for exploring extreme matter states in astrophysical environments.However,radio signals are often disrupted by noise and interference.Detecting and reducing radio frequency interference is vital for maximizing the scientific output of radio telescopes.Traditional methods like Singular Value Decomposition,Principal Component Analysis,Cumsum,and SumThreshold have limitations in handling complex interference.Recently,researchers have combined traditional machine learning with deep learning.Neural networks offer new ideas and tools for future detection of radio frequency interference.This paper discusses the principles,advantages,challenges,and effectiveness of these techniques applied to real astronomical data.
The solar cycle(SC),a phenomenon caused by the quasi-periodic regular activities in the Sun,occurs approximately every 11 years.Intense solar activity can disrupt the Earth's ionosphere,affecting communication and navigation systems.Consequently,accurately predicting the intensity of the SC holds great significance,but predicting the SC involves a long-term time series,and many existing time series forecasting methods have fallen short in terms of accuracy and efficiency.The Time-series Dense Encoder model is a deep learning solution tailored for long time series prediction.Based on a multi-layer perceptron structure,it outperforms the best previously existing models in accuracy,while being efficiently trainable on general datasets.We propose a method based on this model for SC forecasting.Using a trained model,we predict the test set from SC 19 to SC 25 with an average mean absolute percentage error of 32.02,root mean square error of 30.3,mean absolute error of 23.32,and R2(coefficient of determination)of 0.76,outperforming other deep learning models in terms of accuracy and training efficiency on sunspot number datasets.Subsequently,we use it to predict the peaks of SC 25 and SC 26.For SC 25,the peak time has ended,but a stronger peak is predicted for SC 26,of 199.3,within a range of 170.8-221.9,projected to occur during April 2034.
As large-scale astronomical surveys,such as the Sloan Digital Sky Survey(SDSS)and the Large Sky Area Multi-Object Fiber Spectroscopic Telescope(LAMOST),generate increasingly complex datasets,clustering algorithms have become vital for identifying patterns and classifying celestial objects.This paper systematically investigates the application of five main categories of clustering techniques—partition-based,density-based,model-based,hierarchical,and"others"—across a range of astronomical research over the past decade.This review focuses on the six key application areas of stellar classification,galaxy structure analysis,detection of galactic and interstellar features,high-energy astrophysics,exoplanet studies,and anomaly detection.This paper provides an in-depth analysis of the performance and results of each method,considering their respective suitabilities for different data types.Additionally,it presents clustering algorithm selection strategies based on the characteristics of the spectroscopic data being analyzed.We highlight challenges such as handling large datasets,the need for more efficient computational tools,and the lack of labeled data.We also underscore the potential of unsupervised and semi-supervised clustering approaches to overcome these challenges,offering insight into their practical applications,performance,and results in astronomical research.
The Chinese Giant Solar Telescope(CGST)low-dispersion spectrograph requires a large field-of-view(FOV)and high spatial resolution,which can be addressed by a carefully designed image slicer system.Our proposed design divides the rectangular 50ʹʹ×20ʹʹ FOV at the telescope focal plane into four 50ʹʹ×5ʹʹ subfields.Each subfield undergoes optical reconstruction using its independent collimator-camera system(F/36-F/25.79),achieving vertical alignment and focal reduction of subfields to form a pseudo-slit.Using tilt mirrors for scanning allows simultaneous acquisition of spectral data with both a large FOV and a high angular resolution of 0.05ʹʹ.This resolves manufacturing challenges for an image slicer,avoiding the requirement for hundreds of elements,multi-angle configurations,and compact dimensions,and also provides effective technical support for engineering work on the CGST.
The threat posed by space debris to space security is continuously increasing.Optical observation is the main detection method for space debris,but the variety of observation geometries in available measurement datasets is limited.Therefore,simulations are required to supplement observational data.Hardware-in-the-loop(HIL)simulations can provide high-quality simulated optical detection data at a reasonable cost,but existing hardware-in-the-loop methods are only adapted to simple motion scenarios.To extend the simulation ability to complex space motion scenarios,here we propose an optical hardware-in-the-loop space debris simulation method,relying on dynamic detection scenarios,that uses a collaborative scenario-modality-feature simulation scheme to simulate variable observation geometries and to obtain sequential space debris simulation data covering a variety of modalities and scenarios.We apply the proposed space debris detection method to ground-based and space-based simulation experiments and analyze target features within the simulated detection data to demonstrate the usefulness of such simulations.Our simulation method is applicable to space debris optical detection under diverse observation conditions and to multidimensional space debris feature characterization.
Image registration within a solar photosphere sequence is crucial for observational solar physics studies requiring high spatial and temporal resolutions.Previously,we identified residual large-scale nonrigid distortions in high-resolution solar photosphere images from ground-based telescopes after high-resolution reconstruction.Because these distortions are not eliminated by conventional sequence correlation alignment,they can affect the analysis of small-scale activity in the solar photosphere.Here,we implemented an image registration model using deep learning(HCAM-Net)to solve the problem.Within an encoder-decoder framework,we introduced a hybrid attention mechanism to improve context information capture and extract accurate deformation fields.Analyzing solar photosphere images acquired by the New Vacuum Solar Telescope,we demonstrated that the proposed model effectively achieved highly accurate nonrigid image registration.Evaluation metrics and visualization results indicated that our model outperformed current state-of-the-art models,such as VoxelMorph and TransMorph,for nonrigid registration of solar photosphere images,with a structural similarity index measure of 0.965 and a coefficient of determination of 0.976.
The Lucy-Richardson-Rosen Algorithm is widely used for image restoration,but suffers from slow convergence or failure when analyzing images with severe optical aberrations and high noise.To address these limitations,we propose the Differential Lucy-Richardson-Rosen Algorithm which enhances both robustness and convergence speed.By integrating a Hartmann-Shack wavefront sensor into the imaging system,our proposed algorithm directly measures wavefront distortions to accurately estimate the spatially varying point spread function,enabling high-fidelity non-blind deconvolution,even for images acquired by ground-based telescopes,with significant optical imperfections.Extensive simulations and experiments demonstrate that our proposed algorithm outperforms its predecessor in image quality and computational efficiency under challenging aberration and noise conditions.Its rapid and stable performance makes it particularly suitable for real-time or near-real-time astronomical imaging,where reliable,high-resolution recovery is critical.This work advances computational imaging for next-generation astronomical instrumentation through a tightly coupled hardware-algorithm framework.
This paper introduces PyTransHelio,a Python-based graphical interface tool designed for solar physics research,which automates the calculation of magnetic flux-weighted centroid coordinates of active regions in full-disk magnetograms and supports the identification and analysis of footpoints in trans-equatorial loops.The tool addresses the operational complexity and lack of dedicated graphical user environments in traditional Interactive Data Language/SolarSoftWare workflows by integrating modules for magnetogram header parsing,three-coordinate system conversions(pixel/Stonyhurst/Carrington coordinates),automatic magnetic pole detection,and footpoint distance calculation,enabling end-to-end automation.Users can swiftly obtain active region centroid positions and trans-equatorial loop footpoint spacings through an intuitive three-step workflow:"Load → Click → Results".Its modular architecture balances flexibility and extensibility as an open-source tool,significantly lowering the barrier to solar photospheric magnetogram analysis.
Wide-field rapid sky surveys serve as critical observational methods for time-domain astronomical research. The Antarctic region, with several months of continuous dark nights annually, is an ideal site for time-domain astronomical observations. The Antarctic TianMu Staring Observation Project aims to deploy a fleet of small telescopes, adopting an array observation model to conduct time-domain optical observations in Antarctica, featuring wide-sky coverage, high-cadence sampling, long-period staring, and simultaneous multi-band measurements. Considering the severe challenges optical telescopes face in Antarctica, including extremely low temperatures, unattended operation, and limited power supply and network transmission, we have designed and developed the Antarctic TianMu prototype telescope based on drift-scan charge-coupled device technology. In October 2022, our prototype (with an aperture of 18 cm), named AT-Proto was transported to Zhongshan Station in Antarctica aboard China's 39th Antarctic Research Expedition. It has since operated stably and reliably in the frigid environment for over two years, demonstrating the significant advantages of this technology in polar astronomical observations. The experimental observation results of AT-Proto provide a solid foundation for the subsequent construction of a time-domain astronomy observation array in Antarctica.
To address the challenges of high-precision optical surface defect detection,we propose a novel design for a wide-field and broadband light field camera in this work.The proposed system can achieve a 50° field of view and operates at both visible and near-infrared wavelengths.Using the principles of light field imaging,the proposed design enables 3D reconstruction of optical surfaces,thus enabling vertical surface height measurements with enhanced accuracy.Using Zemax-based simulations,we evaluate the system's modulation transfer function,its optical aberrations,and its tolerance to shape variations through Zernike coefficient adjustments.The results demonstrate that this camera can achieve the required spatial resolution while also maintaining high imaging quality and thus offers a promising solution for advanced optical surface defect inspection.
Previous solar probes have relied on solar energy for power,but in the near-solar environment,traditional solar panels are prone to overheating and radiation damage,increasing system complexity and reducing power reliability.This study introduces a dual-power system integrating solar-thermal thermoelectric generation with photovoltaic technology.First,suitable thermoelectric materials are screened,the geometric structure of the thermoelectric devices is simulated,and then the fabricated thermoelectric devices are subjected to cyclic heating-cooling power generation tests and long-duration high-temperature power generation tests.The results demonstrate that a single thermoelectric device can stably provide 3.5 W of power with excellent cycling stability.Additionally,this study discusses design concepts for energy storage and intelligent energy management systems required by the dual-power system.Designed for the Solar Close Observations and Proximity Experiments(SCOPE)mission,this dual-power supply system integrates the benefits of both to address demands under varying environmental conditions.
