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 na...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 R^(2)(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.展开更多
High-resolution solar observations are critical for resolving small-scale dynamic solar processes.Specifically,solar continuum observations,which are used to characterize the photospheric radiative energy distribution...High-resolution solar observations are critical for resolving small-scale dynamic solar processes.Specifically,solar continuum observations,which are used to characterize the photospheric radiative energy distribution,identify atmospheric temperature gradients,and model space weather events,serve as a cornerstone of solar physics research.However,existing observational frameworks face inherent limitations:space-based instruments are constrained by diffraction limits,while ground-based data suffer from atmospheric turbulence and temporal discontinuity.To address these challenges,this study proposes a resolution enhancement method based on cross-platform data fusion between Solar Dynamics Observatory(SDO)/Helioseismic and Magnetic Imager(HMI)space-based full-disk coverage observations and Optical and Near-infrared Solar Eruption Telescope(ONSET)ground-based high-resolution local observations to overcome the physical limitations faced by single-instrument observations.Using 6537 preprocessed spatiotemporally aligned datasets(from 2022),we achieve sub-pixel registration via the scale-invariant feature transform(SIFT)algorithm and design a lightweight model called Cross-Instrument Super-Resolution(CISR)based on a residual local feature block network,optimized for feature extraction and reconstruction using the smooth L1-loss function.Experimental results demonstrate that CISR achieves a pixel-wise correlation coefficient of 0.946,a peak signal-to-noise ratio(PSNR)of 33.924 dB,and a structural similarity index of 0.855 on the test set,significantly outperforming bicubic interpolation and the Super-Resolution Convolutional Neural Network(SRCNN)baseline model.Qualitative visual assessment verifies the method’s efficacy for HMI continuum data resolution enhancement,with exceptional performance in maintaining both sunspot boundary acuity and granule structural fidelity.This work provides a novel approach for multi-source solar data synergy,with future potential to incorporate physics-driven evaluation metrics to further improve the model generalization.展开更多
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 distorti...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.展开更多
获得真实的日面观测图像,是用户基于太阳望远镜开展科学和应用研究的基础,而平场定标是科学数据生产过程中的必要步骤之一,因为平场定标可以扣除太阳望远镜在成像过程中产生的不均匀性.“夸父一号”又名先进天基太阳天文台(Advanced Spa...获得真实的日面观测图像,是用户基于太阳望远镜开展科学和应用研究的基础,而平场定标是科学数据生产过程中的必要步骤之一,因为平场定标可以扣除太阳望远镜在成像过程中产生的不均匀性.“夸父一号”又名先进天基太阳天文台(Advanced Space-based Solar Observatory,ASO-S)卫星的载荷之一,莱曼阿尔法太阳望远镜(Lyman-alpha Solar Telescope,LST),包括3台科学仪器,具体来说是由一个双波段太阳日冕仪(Solar Corona Imager,SCI)以及白光太阳望远镜(White-light Solar Telescope,WST)和莱曼阿尔法全日面太阳成像仪(Solar Disk Imager,SDI)这两个全日面太阳望远镜构成.WST和SDI的探测器是互补金属氧化物半导体(Complementary Metal-Oxide-Semiconductor,CMOS),其平场本身的主要特征是由于激光退火造成的条纹结构,当CMOS探测器在紫外波段使用时会产生一定的衰减与辐射损伤,并且探测器上的水汽凝结和有机物的积累等污染,也会对所得平场产生一定影响.主要展示了ASO-S卫星发射两年多来,载荷LST的两个仪器WST和SDI在轨平场定标时使用的偏摆方案及其优化以及获得的平场图像和其随时间的演化情况等,并简要地介绍了WST和SDI探测器随时间的衰减和辐射损伤情况.展开更多
针对低空经济发展涉及的安全管理问题,在总结低空经济相关技术路线原理及落地方案的运行经验,分析低空安防普适性的4个建设方案:雷达与通感一体技术融合方案、广播式自动相关监视技术方案、远程识别技术方案和基于TDOA(time difference ...针对低空经济发展涉及的安全管理问题,在总结低空经济相关技术路线原理及落地方案的运行经验,分析低空安防普适性的4个建设方案:雷达与通感一体技术融合方案、广播式自动相关监视技术方案、远程识别技术方案和基于TDOA(time difference of arrival)无线电技术的多源融合方案的基础上,构建无人飞行器探测技术评价指标体系,并建立了一种基于决策试验评估实验室(decision-making trial and evaluation laboratory, DEMATEL)和优劣解距离法(technique for order preference by similarity to an ideal solution, TOPSIS)的多属性评价方法。结果发现,以TDOA为基础的多源融合方案是构建城市低空安防体系的有效路径和普适性方案。研究表明,低空安防体系的建设是一个系统性工程,需要政府、企业和社会各方的共同努力,在技术、数据、运营等多个层面进行整合,以适应未来低空经济的发展需求。展开更多
利用抚仙湖太阳观测站(Fuxian Solar Observatory,FSO)的1 m新真空太阳望远镜(New Vacuum Solar Telescope,NVST)在TiO波段拍摄到的高分辨率观测数据和最新的米粒识别算法,对光球内不同磁场结构对米粒组织的影响进行了统计研究.NVST数...利用抚仙湖太阳观测站(Fuxian Solar Observatory,FSO)的1 m新真空太阳望远镜(New Vacuum Solar Telescope,NVST)在TiO波段拍摄到的高分辨率观测数据和最新的米粒识别算法,对光球内不同磁场结构对米粒组织的影响进行了统计研究.NVST数据具有较高的米粒对比度(9.6%),因此有助于对更小尺度的米粒进行识别并作详细的分析和研究.研究发现米粒组织存在两个临界尺度D1和D2,而且尺度小于D1的米粒,其等效直径概率密度具有幂律分布特征,指数与Kolmogorov谱指数相近.所以,米粒组织可以根据其起源机制的不同分为3种类型:尺度小于D1的湍流米粒、尺度大于D2的对流米粒以及尺度介于D1和D2之间的湍流对流混合米粒,这种米粒被认为是前面两种米粒的过渡情况或中间情形.同时,还发现光球内不同磁场结构对湍流米粒的临界尺度D1是有影响的,附近的磁场越强D1越小.但是,磁场对其外部的米粒组织的平均辐射强度及其分布特征则几乎没有影响.展开更多
莱曼阿尔法太阳望远镜(Lyα Solar Telescope, LST)是先进天基太阳天文台(Advanced Space-based Solar Observatory, ASO-S,中文名为“夸父一号”)卫星上的有效载荷之一,它包括白光太阳望远镜(Whitelight Solar Telescope, WST)、莱曼...莱曼阿尔法太阳望远镜(Lyα Solar Telescope, LST)是先进天基太阳天文台(Advanced Space-based Solar Observatory, ASO-S,中文名为“夸父一号”)卫星上的有效载荷之一,它包括白光太阳望远镜(Whitelight Solar Telescope, WST)、莱曼阿尔法全日面成像仪(Solar Disk Imager, SDI)和日冕仪(Solar Corona Imager, SCI) 3台科学仪器.其中WST工作在(360±2) nm(近白光)波段, SDI工作在(121.6±4.5) nm(紫外莱曼阿尔法)波段,两者的观测视场均为1.2 R⊙(R⊙为太阳半径,整个视场相当于38.4′).通过WST和SDI的成像数据可以探索太阳爆发活动在低层大气(光球、色球及过渡区)中的触发和响应,比如研究太阳耀斑的触发机制、白光耀斑的物理性质以及爆发暗条/日珥的形态演化和运动学,并推导出太阳大气的物理参数等.若要获得WST和SDI观测的太阳大气不同特征的物理参数,如耀斑能量、日珥温度和密度等,则需要把它们观测的计数值(Digital Number, DN)转化为物理单位(如erg·cm-2·s-1·sr-1),这个过程即称为辐射定标.辐射定标是WST和SDI科学数据生产过程中的必要步骤之一.目前, WST和SDI在轨辐射定标均以太阳为参考源,其中前者使用美国材料与测试协会(American Society for Testing and Materials, ASTM)于2020年发布的太阳光谱数据,后者则使用地球同步环境系列卫星(Geostationary Operational Environmental Satellite R, GOESR)上搭载的极紫外传感器(Extreme Ultraviolet Sensors, EUVS)观测的数据.给出了WST和SDI在2023年8月到2024年2月正常观测期间的在轨辐射定标系数及其不确定度.通过拟合WST在轨辐射定标系数日平均值得到其经验公式.利用辐射定标后的数据,能够计算太阳耀斑在白光和莱曼阿尔法波段辐射出的能量以及获得日珥密度等,有利于实现WST和SDI的科学目标.展开更多
Solar activity plays an important role in influencing space weather,making it important to understand numerous aspects of spatial and temporal variations in the Sun's radiative output.High-performance deep learnin...Solar activity plays an important role in influencing space weather,making it important to understand numerous aspects of spatial and temporal variations in the Sun's radiative output.High-performance deep learning models and long-term observational records of sunspot relative numbers are essential for solar cycle forecasting.Using the multivariate time series of monthly sunspot relative numbers provided by the National Astronomical Observatory of Japan and two Informer-based models,we forecast the amplitude and timing of solar cycles 25 and 26.The main results are as follows:(1)The maximum amplitude of solar cycle 25 is higher than the previous solar cycle 24 and the following solar cycle 26,suggesting that the long-term oscillatory variation of sunspot magnetic fields is related to the roughly centennial Gleissberg cyclicity.(2)Solar cycles 25 and 26 exhibit a pronounced Gnevyshev gap,which might be caused by two non-coincident peaks resulting from solar magnetic flux transported by meridional circulation and mid-latitude diffusion in the convection zone.(3)Hemispheric prediction of sunspot activity reveals a significant northsouth asynchrony,with activity level of the Sun being more intense in the southern hemisphere.These results are consistent with expectations derived from precursor methods and dynamo theories,and further provide evidence for internal changes in solar magnetic field during the decay of the Modern Maximum.展开更多
基金supported by the Academic Research Projects of Beijing Union University(ZK20202204)the National Natural Science Foundation of China(12250005,12073040,12273059,11973056,12003051,11573037,12073041,11427901,11572005,11611530679 and 12473052)+1 种基金the Strategic Priority Research Program of the China Academy of Sciences(XDB0560000,XDA15052200,XDB09040200,XDA15010700,XDB0560301,and XDA15320102)the Chinese Meridian Project(CMP).
文摘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 R^(2)(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.
基金supported by the National Natural Science Foundation of China(12003068)the Yunnan Key Laboratory of Solar Physics and Space Science(202205AG070009).
文摘High-resolution solar observations are critical for resolving small-scale dynamic solar processes.Specifically,solar continuum observations,which are used to characterize the photospheric radiative energy distribution,identify atmospheric temperature gradients,and model space weather events,serve as a cornerstone of solar physics research.However,existing observational frameworks face inherent limitations:space-based instruments are constrained by diffraction limits,while ground-based data suffer from atmospheric turbulence and temporal discontinuity.To address these challenges,this study proposes a resolution enhancement method based on cross-platform data fusion between Solar Dynamics Observatory(SDO)/Helioseismic and Magnetic Imager(HMI)space-based full-disk coverage observations and Optical and Near-infrared Solar Eruption Telescope(ONSET)ground-based high-resolution local observations to overcome the physical limitations faced by single-instrument observations.Using 6537 preprocessed spatiotemporally aligned datasets(from 2022),we achieve sub-pixel registration via the scale-invariant feature transform(SIFT)algorithm and design a lightweight model called Cross-Instrument Super-Resolution(CISR)based on a residual local feature block network,optimized for feature extraction and reconstruction using the smooth L1-loss function.Experimental results demonstrate that CISR achieves a pixel-wise correlation coefficient of 0.946,a peak signal-to-noise ratio(PSNR)of 33.924 dB,and a structural similarity index of 0.855 on the test set,significantly outperforming bicubic interpolation and the Super-Resolution Convolutional Neural Network(SRCNN)baseline model.Qualitative visual assessment verifies the method’s efficacy for HMI continuum data resolution enhancement,with exceptional performance in maintaining both sunspot boundary acuity and granule structural fidelity.This work provides a novel approach for multi-source solar data synergy,with future potential to incorporate physics-driven evaluation metrics to further improve the model generalization.
基金funded by the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB0560000)the National Natural Science Foundation of China(12473054)+1 种基金the Basic Research on Fund Projects in Yunnan Province(2019FA001)the Yunnan Province Science Foundation Project(202105AC160085).
文摘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.
文摘获得真实的日面观测图像,是用户基于太阳望远镜开展科学和应用研究的基础,而平场定标是科学数据生产过程中的必要步骤之一,因为平场定标可以扣除太阳望远镜在成像过程中产生的不均匀性.“夸父一号”又名先进天基太阳天文台(Advanced Space-based Solar Observatory,ASO-S)卫星的载荷之一,莱曼阿尔法太阳望远镜(Lyman-alpha Solar Telescope,LST),包括3台科学仪器,具体来说是由一个双波段太阳日冕仪(Solar Corona Imager,SCI)以及白光太阳望远镜(White-light Solar Telescope,WST)和莱曼阿尔法全日面太阳成像仪(Solar Disk Imager,SDI)这两个全日面太阳望远镜构成.WST和SDI的探测器是互补金属氧化物半导体(Complementary Metal-Oxide-Semiconductor,CMOS),其平场本身的主要特征是由于激光退火造成的条纹结构,当CMOS探测器在紫外波段使用时会产生一定的衰减与辐射损伤,并且探测器上的水汽凝结和有机物的积累等污染,也会对所得平场产生一定影响.主要展示了ASO-S卫星发射两年多来,载荷LST的两个仪器WST和SDI在轨平场定标时使用的偏摆方案及其优化以及获得的平场图像和其随时间的演化情况等,并简要地介绍了WST和SDI探测器随时间的衰减和辐射损伤情况.
文摘针对低空经济发展涉及的安全管理问题,在总结低空经济相关技术路线原理及落地方案的运行经验,分析低空安防普适性的4个建设方案:雷达与通感一体技术融合方案、广播式自动相关监视技术方案、远程识别技术方案和基于TDOA(time difference of arrival)无线电技术的多源融合方案的基础上,构建无人飞行器探测技术评价指标体系,并建立了一种基于决策试验评估实验室(decision-making trial and evaluation laboratory, DEMATEL)和优劣解距离法(technique for order preference by similarity to an ideal solution, TOPSIS)的多属性评价方法。结果发现,以TDOA为基础的多源融合方案是构建城市低空安防体系的有效路径和普适性方案。研究表明,低空安防体系的建设是一个系统性工程,需要政府、企业和社会各方的共同努力,在技术、数据、运营等多个层面进行整合,以适应未来低空经济的发展需求。
文摘利用抚仙湖太阳观测站(Fuxian Solar Observatory,FSO)的1 m新真空太阳望远镜(New Vacuum Solar Telescope,NVST)在TiO波段拍摄到的高分辨率观测数据和最新的米粒识别算法,对光球内不同磁场结构对米粒组织的影响进行了统计研究.NVST数据具有较高的米粒对比度(9.6%),因此有助于对更小尺度的米粒进行识别并作详细的分析和研究.研究发现米粒组织存在两个临界尺度D1和D2,而且尺度小于D1的米粒,其等效直径概率密度具有幂律分布特征,指数与Kolmogorov谱指数相近.所以,米粒组织可以根据其起源机制的不同分为3种类型:尺度小于D1的湍流米粒、尺度大于D2的对流米粒以及尺度介于D1和D2之间的湍流对流混合米粒,这种米粒被认为是前面两种米粒的过渡情况或中间情形.同时,还发现光球内不同磁场结构对湍流米粒的临界尺度D1是有影响的,附近的磁场越强D1越小.但是,磁场对其外部的米粒组织的平均辐射强度及其分布特征则几乎没有影响.
基金supported by the National Nature Science Foundation of China(12463009)the Yunnan Fundamental Research Projects(202301AV070007,202401AU070026)+2 种基金the"Yunnan Revitalization Talent Support Program"Innovation Team Project(202405AS350012)the Scientific Research Foundation Project of Yunnan Education Department(2023J0624,2024Y469)the GHfund A(202407016295)。
文摘Solar activity plays an important role in influencing space weather,making it important to understand numerous aspects of spatial and temporal variations in the Sun's radiative output.High-performance deep learning models and long-term observational records of sunspot relative numbers are essential for solar cycle forecasting.Using the multivariate time series of monthly sunspot relative numbers provided by the National Astronomical Observatory of Japan and two Informer-based models,we forecast the amplitude and timing of solar cycles 25 and 26.The main results are as follows:(1)The maximum amplitude of solar cycle 25 is higher than the previous solar cycle 24 and the following solar cycle 26,suggesting that the long-term oscillatory variation of sunspot magnetic fields is related to the roughly centennial Gleissberg cyclicity.(2)Solar cycles 25 and 26 exhibit a pronounced Gnevyshev gap,which might be caused by two non-coincident peaks resulting from solar magnetic flux transported by meridional circulation and mid-latitude diffusion in the convection zone.(3)Hemispheric prediction of sunspot activity reveals a significant northsouth asynchrony,with activity level of the Sun being more intense in the southern hemisphere.These results are consistent with expectations derived from precursor methods and dynamo theories,and further provide evidence for internal changes in solar magnetic field during the decay of the Modern Maximum.
