受地球大气影响,使用地基光学望远镜观测角距离较小的双星系统或主卫星系统时往往会出现星象不可分辨的情况.因此,系统光心位置与系统质心位置可能存在一定的偏差.准确地测量太阳系天体系统质心位置可以改进其轨道参数,有助于揭示太阳...受地球大气影响,使用地基光学望远镜观测角距离较小的双星系统或主卫星系统时往往会出现星象不可分辨的情况.因此,系统光心位置与系统质心位置可能存在一定的偏差.准确地测量太阳系天体系统质心位置可以改进其轨道参数,有助于揭示太阳系的形成与演化.以矮行星Haumea及其亮卫星Hi’iaka的运动为例,仿真系统光心围绕质心扰动的过程,探究视宁度(用星象的半高全宽表示)变化对准确测量光心位置的影响.仿真结果表明,使用二维高斯定心算法测定的系统光心位置随视宁度变化,而修正矩定心算法的定心结果不受视宁度的影响.根据仿真结果,研究能够有效减少视宁度变化对光心位置准确测量影响的定心算法十分必要;同时,新的定心算法还需考虑主星光度变化的影响.使用云南天文台2.4 m望远镜, 1 m望远镜以及紫金山天文台姚安观测站0.8 m望远镜从2022年2月7日至2022年5月25日观测矮行星Haumea系统,得到29晚共463幅CCD图像.新定心算法确定的光心位置与使用二维高斯定心算法的结果相比具有更好的位置拟合效果.此外,还发现亮卫星Hi’iaka在Jet Propulsion Laboratory (JPL)历表与Institut de Mécanique Céleste et de Calcul des éphémérides (IMCCE)历表中的位置存在较大偏差.展开更多
随着西部天文选址工作在川西无名山地区的逐步深入,利用地理信息科学(Geographic Information System,GIS)手段收集了大量长期数据,对无名山及周边地区的地理、地质、气候、气象、社会与人口发展趋势等方面开展了详细的调查研究.资料分...随着西部天文选址工作在川西无名山地区的逐步深入,利用地理信息科学(Geographic Information System,GIS)手段收集了大量长期数据,对无名山及周边地区的地理、地质、气候、气象、社会与人口发展趋势等方面开展了详细的调查研究.资料分析结果显示:无名山地处青藏高原向东延伸的褶皱地带—典型的横断山脉地区,形成地势整体落差大、山脊走势平缓、地质结构稳定的特色.无名山区域最高点海拔高度超过5000 m,但附近存在海拔仅2000–3000 m的人口定居点多处,可实现低成本后勤保障;鲜有地震泥石流等不良地质灾害记录;大气干燥、植被稀少、地表半干旱状态;常年盛行西南风,冬季气候寒冷、降雨量稀少,夏季受南部印度洋暖湿气流影响存在明显的雨季;属大香格里拉核心地带,大气洁净度高,无沙尘暴等恶劣天气记录;年均云量少于5成,风向稳定、风速小,可利用晴日/夜数多;人口稀少、经济发展缓慢、社会和谐稳定、远离川滇藏经济相对发达地区;近年来随着本地区旅游资源开发,交通条件得到明显改善,具备高质量公路维护与日常航空运输能力,鲜有其他高原地区常见的大雪封山、航空停运等运输不畅情形发生.因此,GIS综合分析结果表明:无名山地区满足建设高海拔天文观测站的一系列基本保障条件,是我国西部难得的光学/红外天文址点资源.展开更多
Research on the solar magnetic field and its effects on solar dynamo mechanisms and space weather events has benefited from the continual improvements in instrument resolution and measurement frequency.The augmentatio...Research on the solar magnetic field and its effects on solar dynamo mechanisms and space weather events has benefited from the continual improvements in instrument resolution and measurement frequency.The augmentation and assimilation of historical observational data timelines also play a significant role in understanding the patterns of solar magnetic field variation.Within the realm of astronomical data processing,super-resolution(SR)reconstruction refers to the process of using a substantial corpus of training data to learn the nonlinear mapping between low-resolution(LR)and high-resolution(HR)images,thereby achieving higherresolution astronomical images.This paper is an application study in high-dimensional nonlinear regression.Deep learning models were employed to perform SR modeling on SOHO/MDI magnetograms and SDO/HMI magnetograms,thus reliably achieving resolution enhancement of full-disk SOHO/MDI magnetograms and enhancing the image resolution to obtain more detailed information.For this study,a data set comprising 9717pairs of data from 2010 April to 2011 February was used as the training set,1332 pairs from 2011 March were used as the validation set and 1034 pairs from 2011 April were used as the test set.After data preprocessing,we randomly cropped 128×128 sub-images as the LR cases from the full-disk MDI magnetograms,and the corresponding 512×512 sub-images as HR ones from the HMI full-disk magnetograms for model training.The tests conducted have shown that the study successfully produced reliable 4×SR reconstruction of full-disk MDI magnetograms.The MESR model's results(0.911)were highly correlated with the target HMI magnetographs as indicated by the correlation coefficient values.Furthermore,the method achieved the best PSNR,SSIM,MAE and RMSE values,indicating that the MESR model can effectively reconstruct magnetograms.展开更多
We have developed a novel method for co-adding multiple under-sampled images that combines the iteratively reweighted least squares and divide-and-conquer algorithms.Our approach not only allows for the anti-aliasing ...We have developed a novel method for co-adding multiple under-sampled images that combines the iteratively reweighted least squares and divide-and-conquer algorithms.Our approach not only allows for the anti-aliasing of the images but also enables Point-Spread Function(PSF)deconvolution,resulting in enhanced restoration of extended sources,the highest peak signal-to-noise ratio,and reduced ringing artefacts.To test our method,we conducted numerical simulations that replicated observation runs of the China Space Station Telescope/the VLT Survey Telescope(VST)and compared our results to those obtained using previous algorithms.The simulation showed that our method outperforms previous approaches in several ways,such as restoring the profile of extended sources and minimizing ringing artefacts.Additionally,because our method relies on the inherent advantages of least squares fitting,it is more versatile and does not depend on the local uniformity hypothesis for the PSF.However,the new method consumes much more computation than the other approaches.展开更多
The Solar Polar-orbit Observatory(SPO),proposed by Chinese scientists,is designed to observe the solar polar regions in an unprecedented way with a spacecraft traveling in a large solar inclination angle and a small e...The Solar Polar-orbit Observatory(SPO),proposed by Chinese scientists,is designed to observe the solar polar regions in an unprecedented way with a spacecraft traveling in a large solar inclination angle and a small ellipticity.However,one of the most significant challenges lies in ultra-long-distance data transmission,particularly for the Magnetic and Helioseismic Imager(MHI),which is the most important payload and generates the largest volume of data in SPO.In this paper,we propose a tailored lossless data compression method based on the measurement mode and characteristics of MHI data.The background out of the solar disk is removed to decrease the pixel number of an image under compression.Multiple predictive coding methods are combined to eliminate the redundancy utilizing the correlation(space,spectrum,and polarization)in data set,improving the compression ratio.Experimental results demonstrate that our method achieves an average compression ratio of 3.67.The compression time is also less than the general observation period.The method exhibits strong feasibility and can be easily adapted to MHI.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
低频射电望远镜阵列宽视场成像正面临着一系列难点问题,其中最关键的问题是非共面基线效应.它的存在使得忽略w项将导致最终图像出现畸变,且随着视场的增大而加重.综述并剖析了几种w项改正算法及其技术原理,并分析了它们的计算成本和计...低频射电望远镜阵列宽视场成像正面临着一系列难点问题,其中最关键的问题是非共面基线效应.它的存在使得忽略w项将导致最终图像出现畸变,且随着视场的增大而加重.综述并剖析了几种w项改正算法及其技术原理,并分析了它们的计算成本和计算复杂度,进而分析比较了它们的优缺点.以平方公里阵(Square Kilometre Array,SKA)射电望远镜第1阶段低频阵列为研究对象,选取faceting和w-projection成像算法进行了仿真实验.与传统的二维傅立叶变换成像算法进行对比,分析了它们的成像质量和正确性,结果表明这两种算法在宽视场成像方面均明显优于二维傅立叶变换方法.还具体分析了分面(facet)的数目对faceting成像质量和运行时间的影响,以及w步数对w-projection成像质量和运行时间的影响,表明facet数目和w步数的选择必须合理.最后,分析了数据量大小对这两种成像算法运行时间的影响,表明这两种算法在进行海量数据处理前,需要作算法优化改进.研究结果为后续进一步综合分析宽视场成像技术以及这些技术的实用性研究提供了有价值的参考.展开更多
Radio interferometry significantly improves the resolution of observed images, and the final result also relies heavily on data recovery. The Cotton-Schwab CLEAN(CS-Clean) deconvolution approach is a widely used recon...Radio interferometry significantly improves the resolution of observed images, and the final result also relies heavily on data recovery. The Cotton-Schwab CLEAN(CS-Clean) deconvolution approach is a widely used reconstruction algorithm in the field of radio synthesis imaging. However, parameter tuning for this algorithm has always been a difficult task. Here, its performance is improved by considering some internal characteristics of the data. From a mathematical point of view, a peak signal-to-noise-based(PSNRbased) method was introduced to optimize the step length of the steepest descent method in the recovery process. We also found that the loop gain curve in the new algorithm is a good indicator of parameter tuning.Tests show that the new algorithm can effectively solve the problem of oscillation for a large fixed loop gain and provides a more robust recovery.展开更多
This paper presents an automatic multi-band source cross-identification method based on deep learning to identify the hosts of extragalactic radio emission structures.The aim is to satisfy the increased demand for aut...This paper presents an automatic multi-band source cross-identification method based on deep learning to identify the hosts of extragalactic radio emission structures.The aim is to satisfy the increased demand for automatic radio source identification and analysis of large-scale survey data from next-generation radio facilities such as the Square Kilometre Array and the Next Generation Very Large Array.We demonstrate a 97%overall accuracy in distinguishing quasi-stellar objects,galaxies and stars using their optical morphologies plus their corresponding mid-infrared information by training and testing a convolutional neural network on Pan-STARRS imaging and WISE photometry.Compared with an expert-evaluated sample,we show that our approach has 95%accuracy at identifying the hosts of extended radio components.We also find that improving radio core localization,for instance by locating its geodesic center,could further increase the accuracy of locating the hosts of systems with a complex radio structure,such as C-shaped radio galaxies.The framework developed in this work can be used for analyzing data from future large-scale radio surveys.展开更多
文摘受地球大气影响,使用地基光学望远镜观测角距离较小的双星系统或主卫星系统时往往会出现星象不可分辨的情况.因此,系统光心位置与系统质心位置可能存在一定的偏差.准确地测量太阳系天体系统质心位置可以改进其轨道参数,有助于揭示太阳系的形成与演化.以矮行星Haumea及其亮卫星Hi’iaka的运动为例,仿真系统光心围绕质心扰动的过程,探究视宁度(用星象的半高全宽表示)变化对准确测量光心位置的影响.仿真结果表明,使用二维高斯定心算法测定的系统光心位置随视宁度变化,而修正矩定心算法的定心结果不受视宁度的影响.根据仿真结果,研究能够有效减少视宁度变化对光心位置准确测量影响的定心算法十分必要;同时,新的定心算法还需考虑主星光度变化的影响.使用云南天文台2.4 m望远镜, 1 m望远镜以及紫金山天文台姚安观测站0.8 m望远镜从2022年2月7日至2022年5月25日观测矮行星Haumea系统,得到29晚共463幅CCD图像.新定心算法确定的光心位置与使用二维高斯定心算法的结果相比具有更好的位置拟合效果.此外,还发现亮卫星Hi’iaka在Jet Propulsion Laboratory (JPL)历表与Institut de Mécanique Céleste et de Calcul des éphémérides (IMCCE)历表中的位置存在较大偏差.
文摘随着西部天文选址工作在川西无名山地区的逐步深入,利用地理信息科学(Geographic Information System,GIS)手段收集了大量长期数据,对无名山及周边地区的地理、地质、气候、气象、社会与人口发展趋势等方面开展了详细的调查研究.资料分析结果显示:无名山地处青藏高原向东延伸的褶皱地带—典型的横断山脉地区,形成地势整体落差大、山脊走势平缓、地质结构稳定的特色.无名山区域最高点海拔高度超过5000 m,但附近存在海拔仅2000–3000 m的人口定居点多处,可实现低成本后勤保障;鲜有地震泥石流等不良地质灾害记录;大气干燥、植被稀少、地表半干旱状态;常年盛行西南风,冬季气候寒冷、降雨量稀少,夏季受南部印度洋暖湿气流影响存在明显的雨季;属大香格里拉核心地带,大气洁净度高,无沙尘暴等恶劣天气记录;年均云量少于5成,风向稳定、风速小,可利用晴日/夜数多;人口稀少、经济发展缓慢、社会和谐稳定、远离川滇藏经济相对发达地区;近年来随着本地区旅游资源开发,交通条件得到明显改善,具备高质量公路维护与日常航空运输能力,鲜有其他高原地区常见的大雪封山、航空停运等运输不畅情形发生.因此,GIS综合分析结果表明:无名山地区满足建设高海拔天文观测站的一系列基本保障条件,是我国西部难得的光学/红外天文址点资源.
文摘低表面亮度星系(Low Surface Brightness Galaxy,LSBG)的特征对于理解星系整体特征非常重要,通过现代的机器学习特别是深度学习算法来搜寻扩充低表面亮度星系样本具有重要意义.LSBG因特征不明显而难以用传统方法进行自动和准确辨别,但深度学习确具有自动找出复杂且有效特征的优势,针对此问题提出了一种可用于在大样本巡天观测项目中搜寻LSBG的算法---YOLOX-CS(You Only Look Once version X-CS).首先通过实验对比5种经典目标检测算法并选择较优的YOLOX算法作为基础算法,然后结合不同注意力机制和不同优化器,构建了YOLOX-CS的框架结构.数据集使用的是斯隆数字化巡天(Sloan Digital Sky Survey,SDSS)中的图像,其标签来自于α.40-SDSS DR7(40%中性氢苜蓿巡天与第7次数据发布的斯隆数字化巡天的交叉覆盖天区)巡天项目中的LSBG,由于该数据集样本较少,还采用了深度卷积生成对抗网络(Deep Convolutional Generative Adversarial Networks,DCGAN)模型扩充了实验测试数据.通过与一系列目标检测算法对比后,YOLOX-CS在扩充前后两个数据集中搜索LSBG的召回率和AP(Average Precision)值都有较好的测试结果,其在未扩充数据集的测试集中的召回率达到97.75%,AP值达到97.83%,在DCGAN模型扩充的数据集中,同样测试集下进行实验的召回率达到99.10%,AP值达到98.94%,验证了该算法在LSBG搜索中具有优秀的性能.最后,将该算法应用到SDSS部分测光数据上,搜寻得到了765个LSBG候选体.
基金funded by the National Natural Science Foundation of China(NSFC,Grant No.12003068)Yunnan Key Laboratory of Solar Physics and Space Science under the number 202205AG070009。
文摘Research on the solar magnetic field and its effects on solar dynamo mechanisms and space weather events has benefited from the continual improvements in instrument resolution and measurement frequency.The augmentation and assimilation of historical observational data timelines also play a significant role in understanding the patterns of solar magnetic field variation.Within the realm of astronomical data processing,super-resolution(SR)reconstruction refers to the process of using a substantial corpus of training data to learn the nonlinear mapping between low-resolution(LR)and high-resolution(HR)images,thereby achieving higherresolution astronomical images.This paper is an application study in high-dimensional nonlinear regression.Deep learning models were employed to perform SR modeling on SOHO/MDI magnetograms and SDO/HMI magnetograms,thus reliably achieving resolution enhancement of full-disk SOHO/MDI magnetograms and enhancing the image resolution to obtain more detailed information.For this study,a data set comprising 9717pairs of data from 2010 April to 2011 February was used as the training set,1332 pairs from 2011 March were used as the validation set and 1034 pairs from 2011 April were used as the test set.After data preprocessing,we randomly cropped 128×128 sub-images as the LR cases from the full-disk MDI magnetograms,and the corresponding 512×512 sub-images as HR ones from the HMI full-disk magnetograms for model training.The tests conducted have shown that the study successfully produced reliable 4×SR reconstruction of full-disk MDI magnetograms.The MESR model's results(0.911)were highly correlated with the target HMI magnetographs as indicated by the correlation coefficient values.Furthermore,the method achieved the best PSNR,SSIM,MAE and RMSE values,indicating that the MESR model can effectively reconstruct magnetograms.
基金supported by the GHfund A(202302017475)supported by the Foundation for Distinguished Young Scholars of Jiangsu Province(No.BK20140050)+5 种基金the National Natural Science Foundation of China(Nos.11973070,11333008,11273061,11825303,and 11673065)the China Manned Space Project with No.CMS-CSST-2021-A01,CMSCSST-2021-A03,CMS-CSST-2021-B01the Joint Funds of the National Natural Science Foundation of China(No.U1931210)the support from Key Research Program of Frontier Sciences,CAS,grant No.ZDBS-LY-7013Program of Shanghai Academic/Technology Research Leaderthe support from the science research grants from the China Manned Space Project with CMS-CSST-2021-A04,CMS-CSST-2021-A07。
文摘We have developed a novel method for co-adding multiple under-sampled images that combines the iteratively reweighted least squares and divide-and-conquer algorithms.Our approach not only allows for the anti-aliasing of the images but also enables Point-Spread Function(PSF)deconvolution,resulting in enhanced restoration of extended sources,the highest peak signal-to-noise ratio,and reduced ringing artefacts.To test our method,we conducted numerical simulations that replicated observation runs of the China Space Station Telescope/the VLT Survey Telescope(VST)and compared our results to those obtained using previous algorithms.The simulation showed that our method outperforms previous approaches in several ways,such as restoring the profile of extended sources and minimizing ringing artefacts.Additionally,because our method relies on the inherent advantages of least squares fitting,it is more versatile and does not depend on the local uniformity hypothesis for the PSF.However,the new method consumes much more computation than the other approaches.
基金supported by the National Key R&D Program of China(grant No.2022YFF0503800)by the National Natural Science Foundation of China(NSFC)(grant No.11427901)+1 种基金by the Strategic Priority Research Program of the Chinese Academy of Sciences(CAS-SPP)(grant No.XDA15320102)by the Youth Innovation Promotion Association(CAS No.2022057)。
文摘The Solar Polar-orbit Observatory(SPO),proposed by Chinese scientists,is designed to observe the solar polar regions in an unprecedented way with a spacecraft traveling in a large solar inclination angle and a small ellipticity.However,one of the most significant challenges lies in ultra-long-distance data transmission,particularly for the Magnetic and Helioseismic Imager(MHI),which is the most important payload and generates the largest volume of data in SPO.In this paper,we propose a tailored lossless data compression method based on the measurement mode and characteristics of MHI data.The background out of the solar disk is removed to decrease the pixel number of an image under compression.Multiple predictive coding methods are combined to eliminate the redundancy utilizing the correlation(space,spectrum,and polarization)in data set,improving the compression ratio.Experimental results demonstrate that our method achieves an average compression ratio of 3.67.The compression time is also less than the general observation period.The method exhibits strong feasibility and can be easily adapted to MHI.
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.
文摘低频射电望远镜阵列宽视场成像正面临着一系列难点问题,其中最关键的问题是非共面基线效应.它的存在使得忽略w项将导致最终图像出现畸变,且随着视场的增大而加重.综述并剖析了几种w项改正算法及其技术原理,并分析了它们的计算成本和计算复杂度,进而分析比较了它们的优缺点.以平方公里阵(Square Kilometre Array,SKA)射电望远镜第1阶段低频阵列为研究对象,选取faceting和w-projection成像算法进行了仿真实验.与传统的二维傅立叶变换成像算法进行对比,分析了它们的成像质量和正确性,结果表明这两种算法在宽视场成像方面均明显优于二维傅立叶变换方法.还具体分析了分面(facet)的数目对faceting成像质量和运行时间的影响,以及w步数对w-projection成像质量和运行时间的影响,表明facet数目和w步数的选择必须合理.最后,分析了数据量大小对这两种成像算法运行时间的影响,表明这两种算法在进行海量数据处理前,需要作算法优化改进.研究结果为后续进一步综合分析宽视场成像技术以及这些技术的实用性研究提供了有价值的参考.
基金partially supported by the Open Research Program of the CAS Key Laboratory of Solar Activity (KLSA201805)the Guizhou Science & Technology Plan Project (Platform Talent No.[2017]5788)+3 种基金the Youth Science & Technology Talents Development Project of Guizhou Education Department (No. KY[2018]119)the National Science Foundation of China (Grant Nos. 11103055, 11773062 and 61605153)“Light of West China” Programme (Grant Nos. RCPY201105 and 2017-XBQNXZ-A-008)the National Basic Research Program of China (973 program: 2012CB821804 and 2015CB857100)
文摘Radio interferometry significantly improves the resolution of observed images, and the final result also relies heavily on data recovery. The Cotton-Schwab CLEAN(CS-Clean) deconvolution approach is a widely used reconstruction algorithm in the field of radio synthesis imaging. However, parameter tuning for this algorithm has always been a difficult task. Here, its performance is improved by considering some internal characteristics of the data. From a mathematical point of view, a peak signal-to-noise-based(PSNRbased) method was introduced to optimize the step length of the steepest descent method in the recovery process. We also found that the loop gain curve in the new algorithm is a good indicator of parameter tuning.Tests show that the new algorithm can effectively solve the problem of oscillation for a large fixed loop gain and provides a more robust recovery.
基金supported by grants from the National Natural Science Foundation of China(Nos.11973051,12041302)funded by Chinese Academy of Sciences President’s International Fellowship Initiative.Grant No.2019PM0017。
文摘This paper presents an automatic multi-band source cross-identification method based on deep learning to identify the hosts of extragalactic radio emission structures.The aim is to satisfy the increased demand for automatic radio source identification and analysis of large-scale survey data from next-generation radio facilities such as the Square Kilometre Array and the Next Generation Very Large Array.We demonstrate a 97%overall accuracy in distinguishing quasi-stellar objects,galaxies and stars using their optical morphologies plus their corresponding mid-infrared information by training and testing a convolutional neural network on Pan-STARRS imaging and WISE photometry.Compared with an expert-evaluated sample,we show that our approach has 95%accuracy at identifying the hosts of extended radio components.We also find that improving radio core localization,for instance by locating its geodesic center,could further increase the accuracy of locating the hosts of systems with a complex radio structure,such as C-shaped radio galaxies.The framework developed in this work can be used for analyzing data from future large-scale radio surveys.
