With the availability of multi-object spectrometers and the design and operation of some large scale sky surveys, the issue of how to deal with enormous quantities of spectral data efficiently and accurately is becomi...With the availability of multi-object spectrometers and the design and operation of some large scale sky surveys, the issue of how to deal with enormous quantities of spectral data efficiently and accurately is becoming more and more important. This work investigates the classification problem of stellar spectra under the assumption that there is no perfect absolute flux calibration, for example, when considering spectra from the Guo Shou Jing Telescope(the Large Sky Area Multi-Object Fiber Spectroscopic Telescope, LAMOST). The proposed scheme consists of the following two procedures: Firstly, a spectrum is normalized based on a 17 th order polynomial fitting;secondly, a random forest(RF) is utilized to classify the stellar spectra. Experiments on four stellar spectral libraries show that the RF has good classification performance. This work also studied the spectral feature evaluation problem based on RF. The evaluation is helpful in understanding the results of the proposed stellar classification scheme and exploring its potential improvements in the future.展开更多
Astronomical knowledge entities,such as celestial object identifiers,are crucial for literature retrieval and knowledge graph construction,and other research and applications in the field of astronomy.Traditional meth...Astronomical knowledge entities,such as celestial object identifiers,are crucial for literature retrieval and knowledge graph construction,and other research and applications in the field of astronomy.Traditional methods of extracting knowledge entities from texts face numerous challenging obstacles that are difficult to overcome.Consequently,there is a pressing need for improved methods to efficiently extract them.This study explores the potential of pre-trained Large Language Models(LLMs)to perform astronomical knowledge entity extraction(KEE)task from astrophysical journal articles using prompts.We propose a prompting strategy called PromptKEE,which includes five prompt elements,and design eight combination prompts based on them.We select four representative LLMs(Llama-2-70B,GPT-3.5,GPT-4,and Claude 2)and attempt to extract the most typical astronomical knowledge entities,celestial object identifiers and telescope names,from astronomical journal articles using these eight combination prompts.To accommodate their token limitations,we construct two data sets:the full texts and paragraph collections of 30 articles.Leveraging the eight prompts,we test on full texts with GPT-4and Claude 2,on paragraph collections with all LLMs.The experimental results demonstrate that pre-trained LLMs show significant potential in performing KEE tasks,but their performance varies on the two data sets.Furthermore,we analyze some important factors that influence the performance of LLMs in entity extraction and provide insights for future KEE tasks in astrophysical articles using LLMs.Finally,compared to other methods of KEE,LLMs exhibit strong competitiveness in multiple aspects.展开更多
Cross-matching is a key technique to achieve fusion of multi-band astronomical catalogs. Due to different equipment such as various astronomical telescopes, the existence of measurement errors, and proper motions of t...Cross-matching is a key technique to achieve fusion of multi-band astronomical catalogs. Due to different equipment such as various astronomical telescopes, the existence of measurement errors, and proper motions of the celestial bodies, the same celestial object will have different positions in different catalogs, making it difficult to integrate multi-band or full-band astronomical data. In this study, we propose an online cross-matching method based on pseudo-spherical indexing techniques and develop a service combining with high performance computing system(Taurus) to improve cross-matching efficiency, which is designed for the Data Center of Xinjiang Astronomical Observatory. Specifically, we use Quad Tree Cube to divide the spherical blocks of the celestial object and map the 2D space composed of R.A. and decl. to 1D space and achieve correspondence between real celestial objects and spherical patches. Finally, we verify the performance of the service using Gaia 3 and PPMXL catalogs. Meanwhile, we send the matching results to VO tools-Topcat and Aladin respectively to get visual results. The experimental results show that the service effectively solves the speed bottleneck problem of crossmatching caused by frequent I/O, and significantly improves the retrieval and matching speed of massive astronomical data.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
GRB 190530A was jointly observed by the High Energy X-ray Telescope of the Hard X-ray Modulation Telescope(Insight-HXMT/HE)and the Ground-Based Wide-Angle Camera network(GWAC-N)with the extremely large field of view.A...GRB 190530A was jointly observed by the High Energy X-ray Telescope of the Hard X-ray Modulation Telescope(Insight-HXMT/HE)and the Ground-Based Wide-Angle Camera network(GWAC-N)with the extremely large field of view.After triggered by Insight-HXMT/HE and Fermi/GBM,we observed the optical emission of GRB 190530A,using the 30 cm telescope of GWAC(GWAC-F30)to search and locate its position.Subsequent observation of the late afterglow of GRB 190530A was made with the 2.16 m telescope at Xinglong Observatory.In this paper,we make a detailed exploration of the origin of GRB 190530A.In the prompt emission,a“double-tracking”pattern is presented both for the low-energy spectral indexαand the peak energy Epin the Band function with Insight-HXMT/HE and Fermi/GBM data;the results of GRB 190530A are consistent with the Amati and Yonetoku correlations;the spectral lag(τ)versus energy(E)can be estimated withτ=-3.0±0.06+(0.17±0.03)logE.The synchrotron radiation can account for the origin of GRB190530A prompt emission behaviors.Theαand Epof the precursor are essentially the same as that of the main prompt emission,implying that they have the same origin.For the afterglow,it can be described with the external forward shock model in ISM circumburst medium.In summary,from precursor,prompt emission to afterglow of GRB 190530A all originated from synchrotron radiation.展开更多
The Xinjiang Astronomical Observatory Data Center faces issues related to delay-affected services. As a result, these services cannot be implemented in a timely manner due to the overloading of transmission links. In ...The Xinjiang Astronomical Observatory Data Center faces issues related to delay-affected services. As a result, these services cannot be implemented in a timely manner due to the overloading of transmission links. In this paper, the software-defined network technology is applied to the Xinjiang Astronomical Observatory Data Center Network(XAODCN). Specifically, a novel reconfiguration method is proposed to realise the software-defined Xinjiang Astronomical Observatory Data Center Network(SDXAO-DCN), and a network model is constructed. To overcome the congestion problem, a traffic load-balancing algorithm is designed for fast transmission of the service traffic by combining three factors: network structure, congestion level and transmission service. The proposed algorithm is compared with current commonly load-balancing algorithms which are used in data center to verify its efficiency. Simulation experiments show that the algorithm improved transmission performance and transmission quality for the SDXAO-DCN.展开更多
基金supported by the National Natural Science Foundation of China (Grant Nos: 61273248 and 61075033)the Natural Science Foundation of Guangdong Province (2014A030313425 and S2011010003348)China Scholarship Council (201706755006) and the Joint Research Fund in Astronomy (U1531242) under cooperative agreement between the National Natural Science Foundation of China and Chinese Academy of Sciences
文摘With the availability of multi-object spectrometers and the design and operation of some large scale sky surveys, the issue of how to deal with enormous quantities of spectral data efficiently and accurately is becoming more and more important. This work investigates the classification problem of stellar spectra under the assumption that there is no perfect absolute flux calibration, for example, when considering spectra from the Guo Shou Jing Telescope(the Large Sky Area Multi-Object Fiber Spectroscopic Telescope, LAMOST). The proposed scheme consists of the following two procedures: Firstly, a spectrum is normalized based on a 17 th order polynomial fitting;secondly, a random forest(RF) is utilized to classify the stellar spectra. Experiments on four stellar spectral libraries show that the RF has good classification performance. This work also studied the spectral feature evaluation problem based on RF. The evaluation is helpful in understanding the results of the proposed stellar classification scheme and exploring its potential improvements in the future.
基金supported by the National Natural Science Foundation of China(NSFC,Grant Nos.12273077,72101068,12373110,and 12103070)National Key Research and Development Program of China under grants(2022YFF0712400,2022YFF0711500)+2 种基金the 14th Five-year Informatization Plan of Chinese Academy of Sciences(CAS-WX2021SF-0204)supported by Astronomical Big Data Joint Research Centerco-founded by National Astronomical Observatories,Chinese Academy of Sciences and Alibaba Cloud。
文摘Astronomical knowledge entities,such as celestial object identifiers,are crucial for literature retrieval and knowledge graph construction,and other research and applications in the field of astronomy.Traditional methods of extracting knowledge entities from texts face numerous challenging obstacles that are difficult to overcome.Consequently,there is a pressing need for improved methods to efficiently extract them.This study explores the potential of pre-trained Large Language Models(LLMs)to perform astronomical knowledge entity extraction(KEE)task from astrophysical journal articles using prompts.We propose a prompting strategy called PromptKEE,which includes five prompt elements,and design eight combination prompts based on them.We select four representative LLMs(Llama-2-70B,GPT-3.5,GPT-4,and Claude 2)and attempt to extract the most typical astronomical knowledge entities,celestial object identifiers and telescope names,from astronomical journal articles using these eight combination prompts.To accommodate their token limitations,we construct two data sets:the full texts and paragraph collections of 30 articles.Leveraging the eight prompts,we test on full texts with GPT-4and Claude 2,on paragraph collections with all LLMs.The experimental results demonstrate that pre-trained LLMs show significant potential in performing KEE tasks,but their performance varies on the two data sets.Furthermore,we analyze some important factors that influence the performance of LLMs in entity extraction and provide insights for future KEE tasks in astrophysical articles using LLMs.Finally,compared to other methods of KEE,LLMs exhibit strong competitiveness in multiple aspects.
基金supported by the National Key R&D Program of China (Nos. 2022YFF0711502 and 2021YFC2203502)the National Natural Science Foundation of China (NSFC)(12173077 and 12003062)+6 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. PTYQ2022YZZD01)China 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)supported by Astronomical Big Data Joint Research Center,co-founded by National Astronomical Observatories,Chinese Academy of Sciences。
文摘Cross-matching is a key technique to achieve fusion of multi-band astronomical catalogs. Due to different equipment such as various astronomical telescopes, the existence of measurement errors, and proper motions of the celestial bodies, the same celestial object will have different positions in different catalogs, making it difficult to integrate multi-band or full-band astronomical data. In this study, we propose an online cross-matching method based on pseudo-spherical indexing techniques and develop a service combining with high performance computing system(Taurus) to improve cross-matching efficiency, which is designed for the Data Center of Xinjiang Astronomical Observatory. Specifically, we use Quad Tree Cube to divide the spherical blocks of the celestial object and map the 2D space composed of R.A. and decl. to 1D space and achieve correspondence between real celestial objects and spherical patches. Finally, we verify the performance of the service using Gaia 3 and PPMXL catalogs. Meanwhile, we send the matching results to VO tools-Topcat and Aladin respectively to get visual results. The experimental results show that the service effectively solves the speed bottleneck problem of crossmatching caused by frequent I/O, and significantly improves the retrieval and matching speed of massive astronomical data.
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 Open Project Program of the Key Laboratory of Optical Astronomy,National Astronomical Observatories,Chinese Academy of Sciencessupported by the National Key R&D Program of China(grant No.2021YFA0718500)+3 种基金the National Natural Science Foundation of China(grant Nos.U1938201,12103055,11863007 and 11973055)the Guangxi Science Foundation(grant No.2018GXNSFGA281007)the Innovation Project of Guangxi Graduate Education(grant No.YSCW2019050)the Teaching reform project of Guangxi Higher Education(grant No.2019JGZ102)。
文摘GRB 190530A was jointly observed by the High Energy X-ray Telescope of the Hard X-ray Modulation Telescope(Insight-HXMT/HE)and the Ground-Based Wide-Angle Camera network(GWAC-N)with the extremely large field of view.After triggered by Insight-HXMT/HE and Fermi/GBM,we observed the optical emission of GRB 190530A,using the 30 cm telescope of GWAC(GWAC-F30)to search and locate its position.Subsequent observation of the late afterglow of GRB 190530A was made with the 2.16 m telescope at Xinglong Observatory.In this paper,we make a detailed exploration of the origin of GRB 190530A.In the prompt emission,a“double-tracking”pattern is presented both for the low-energy spectral indexαand the peak energy Epin the Band function with Insight-HXMT/HE and Fermi/GBM data;the results of GRB 190530A are consistent with the Amati and Yonetoku correlations;the spectral lag(τ)versus energy(E)can be estimated withτ=-3.0±0.06+(0.17±0.03)logE.The synchrotron radiation can account for the origin of GRB190530A prompt emission behaviors.Theαand Epof the precursor are essentially the same as that of the main prompt emission,implying that they have the same origin.For the afterglow,it can be described with the external forward shock model in ISM circumburst medium.In summary,from precursor,prompt emission to afterglow of GRB 190530A all originated from synchrotron radiation.
基金supported by National Key R&D Program of China No.2021YFC2203502the National Natural Science Foundation of China (NSFC)(11803080,12173077,11873082,12003062)+2 种基金the Tianshan Innovation Team Plan of Xinjiang Uygur Autonomous Region (2022D14020)the Youth Innovation Promotion Association CASNational Key R&D Program of China No.2018 YFA0404704。
文摘The Xinjiang Astronomical Observatory Data Center faces issues related to delay-affected services. As a result, these services cannot be implemented in a timely manner due to the overloading of transmission links. In this paper, the software-defined network technology is applied to the Xinjiang Astronomical Observatory Data Center Network(XAODCN). Specifically, a novel reconfiguration method is proposed to realise the software-defined Xinjiang Astronomical Observatory Data Center Network(SDXAO-DCN), and a network model is constructed. To overcome the congestion problem, a traffic load-balancing algorithm is designed for fast transmission of the service traffic by combining three factors: network structure, congestion level and transmission service. The proposed algorithm is compared with current commonly load-balancing algorithms which are used in data center to verify its efficiency. Simulation experiments show that the algorithm improved transmission performance and transmission quality for the SDXAO-DCN.
