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Linying Mi

Publications and source records attributed to Linying Mi.

15 recordsLinked to original sources

The Mini-SiTian Array: Imaging Processing Pipeline

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 approximately 3000 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 approximately 1\,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.

astro-ph.IM

Astronomical Knowledge Entity Extraction in Astrophysics Journal Articles via Large Language Models

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 challenges like high manual effort, poor generalization, and costly maintenance. 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 Prompt-KEE, which includes five prompt elements, and design eight combination prompts based on them. Celestial object identifier and telescope name, two most typical astronomical knowledge entities, are selected to be experimental object. And we introduce four currently representative LLMs, namely Llama-2-70B, GPT-3.5, GPT-4, and Claude 2. To accommodate their token limitations, we construct two datasets: the full texts and paragraph collections of 30 articles. Leveraging the eight prompts, we test on full texts with GPT-4 and Claude 2, on paragraph collections with all LLMs. The experimental results demonstrated that pre-trained LLMs have the significant potential to perform KEE tasks in astrophysics journal articles, but there are differences in their performance. 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.

astro-ph.IM

Photometric redshift estimation of galaxies in the DESI Legacy Imaging Surveys

The accurate estimation of photometric redshifts plays a crucial role in accomplishing science objectives of the large survey projects. The template-fitting and machine learning are the two main types of methods applied currently. Based on the training set obtained by cross-correlating the DESI Legacy Imaging Surveys DR9 galaxy catalogue and SDSS DR16 galaxy catalogue, the two kinds of methods are used and optimized, such as EAZY for template-fitting approach and CATBOOST for machine learning. Then the created models are tested by the cross-matched samples of the DESI Legacy Imaging SurveysDR9 galaxy catalogue with LAMOST DR7, GAMA DR3 and WiggleZ galaxy catalogues. Moreover three machine learning methods (CATBOOST, Multi-Layer Perceptron and Random Forest) are compared, CATBOOST shows its superiority for our case. By feature selection and optimization of model parameters, CATBOOST can obtain higher accuracy with optical and infrared photometric information, the best performance ($MSE=0.0032$, $\sigma_{NMAD}=0.0156$ and $O=0.88$ per cent) with $g \le 24.0$, $r \le 23.4$ and $z \le 22.5$ is achieved. But EAZY can provide more accurate photometric redshift estimation for high redshift galaxies, especially beyond the redhisft range of training sample. Finally, we finish the redshift estimation of all DESI DR9 galaxies with CATBOOST and EAZY, which will contribute to the further study of galaxies and their properties.

astro-ph.IM

Identification of BASS DR3 Sources as Stars, Galaxies and Quasars by XGBoost

The Beijing-Arizona Sky Survey (BASS) Data Release 3 (DR3) catalogue was released in 2019, which contains the data from all BASS and the Mosaic z-band Legacy Survey (MzLS) observations during 2015 January and 2019 March, about 200 million sources. We cross-match BASS DR3 with spectral databases from the Sloan Digital Sky Survey (SDSS) and the Large Sky Area Multi-object Fiber Spectroscopic Telescope (LAMOST) to obtain the spectroscopic classes of known samples. Then, the samples are cross-matched with ALLWISE database. Based on optical and infrared information of the samples, we use the XGBoost algorithm to construct different classifiers, including binary classification and multiclass classification. The accuracy of these classifiers with the best input pattern is larger than 90.0 per cent. Finally, all selected sources in the BASS DR3 catalogue are classified by these classifiers. The classification label and probabilities for individual sources are assigned by different classifiers. When the predicted results by binary classification are the same as multiclass classification with optical and infrared information, the number of star, galaxy and quasar candidates is separately 12 375 838 (P_S>0.95), 18 606 073 (P_G>0.95) and 798 928 (P_Q>0.95). For these sources without infrared information, the predicted results can be as a reference. Those candidates may be taken as input catalogue of LAMOST, DESI or other projects for follow up observation. The classified result will be of great help and reference for future research of the BASS DR3 sources.

astro-ph.GA

GWOPS: A VO-technology Driven Tool to Search for the Electromagnetic Counterpart of Gravitational Wave Event

The search and follow-up observation of electromagnetic (EM) counterparts of gravitational waves (GW) is a current hot topic of GW cosmology. Due to the limitation of the accuracy of the GW observation facility at this stage, we can only get a rough sky-localization region for the GW event, and the typical area of the region is between 200 and 1500 square degrees. Since GW events occur in or near galaxies, limiting the observation target to galaxies can significantly speedup searching for EM counterparts. Therefore, how to efficiently select host galaxy candidates in such a large GW localization region, how to arrange the observation sequence, and how to efficiently identify the GW source from observational data are the problems that need to be solved. International Virtual Observatory Alliance has developed a series of technical standards for data retrieval, interoperability and visualization. Based on the application of VO technologies, we construct the GW follow-up Observation Planning System (GWOPS). It consists of three parts: a pipeline to select host candidates of GW and sort their priorities for follow-up observation, an identification module to find the transient from follow-up observation data, and a visualization module to display GW-related data. GWOPS can rapidly respond to GW events. With GWOPS, the operations such as follow-up observation planning, data storage, data visualization, and transient identification can be efficiently coordinated, which will promote the success searching rate for GWs EM counterparts.

astro-ph.IM

Towards an Astronomical Science Platform: Experiences and Lessons Learned from Chinese Virtual Observatory

In the era of big data astronomy, next generation telescopes and large sky surveys produce data sets at the TB or even PB level. Due to their large data volumes, these astronomical data sets are extremely difficult to transfer and analyze using personal computers or small clusters. In order to offer better access to data, data centers now generally provide online science platforms that enable analysis close to the data. The Chinese Virtual Observatory (China-VO) is one of the member projects in the International Virtual Observatory Alliance and it is dedicated to providing a research and education environment where globally distributed astronomy archives are simple to find, access, and interoperate. In this study, we summarize highlights of the work conducted at the China-VO, as well the experiences and lessons learned during the full life-cycle management of astronomical data. Finally, We discuss the challenges and future trends for astronomical science platforms.

astro-ph.IM

IVOA HiPS Implementation in the Framework of WorldWide Telescope

The WorldWide Telescope(WWT) is a scientific visualization platform which can browse deep space images, star catalogs, and planetary remote sensing data from different observation facilities in a three-dimensional virtual scene. First launched and then open-sourced by Microsoft Research, the WWT is now managed by the American Astronomical Society (AAS). Hierarchical Progressive Survey (HiPS) is an astronomical data release scheme proposed by Centre de Donn\'ees astronomiques de Strasbourg (CDS) and has been accepted as a recommendation by International Virtual Observatory Alliance (IVOA). The HiPS solution has been adopted widely by many astronomical institutions for data release. Since WWT selected Hierarchical Triangular Mesh (HTM) as the standard for data visualization in the early stage of development, data released by HiPS cannot be visualized in WWT, which significantly limits the application of WWT. This paper introduces the implementation method for HiPS dataset visualization in WWT, and introduces HiPS data projection, mesh rendering, and data index implementation in WWT. Taking Chang'E-2 lunar probe data as an example, this paper introduces how to convert planetary remote sensing data into a HiPS dataset and integrate it into WWT. This paper also compares the efficiency and memory consumption of WWT loading its native data and HiPS data, and illustrates the application of HiPS in scientific data visualization and science education in WWT.

astro-ph.IM

A hybrid architecture for astronomical computing

With many large science equipment constructing and putting into use, astronomy has stepped into the big data era. The new method and infrastructure of big data processing has become a new requirement of many astronomers. Cloud computing, Map/Reduce, Hadoop, Spark, etc. many new technology has sprung up in recent years. Comparing to the high performance computing(HPC), Data is the center of these new technology. So, a new computing architecture infrastructure is necessary, which can be shared by both HPC and big data processing. Based on Astronomy Cloud project of Chinese Virtual Observatory (China-VO), we have made much efforts to optimize the designation of the hybrid computing platform. which include the hardware architecture, cluster management, Job and Resource scheduling.

cs.DC

A Conception of Engineering Design for Remote Unattended Operation Public Observatory

Public observatory project is playing more and more important role in science popularization education and scientific research, and many amateur astronomers also have began to build their own observatories in remote areas. As a result of the limitation of technical condition and construction funds for amateur astronomers, their system often breaks down, and then a stable remote unattended operation system becomes very critical. Hardware connection and control is the basic and core part in observatory design. Here we propose a conception of engineering hardware design for public observatory operation as a bridge between observatory equipment and observation software. It can not only satisfy multiple observation mode requirement, but also save cost.

astro-ph.IM

AstroCloud: A Distributed Cloud Computing and Application Platform for Astronomy

Virtual Observatory (VO) is a data-intensively online astronomical research and education environment, which takes advantages of advanced information technologies to achieve seamless and global access to astronomical information. AstroCloud is a cyber-infrastructure for astronomy research initiated by Chinese Virtual Observatory (China-VO) project, and also a kind of physical distributed platform which integrates lots of tasks such as telescope access proposal management, data archiving, data quality control, data release and open access, cloud based data processing and analysis. It consists of five application channels, i.e. observation, data, tools, cloud and public and is acting as a full lifecycle management system and gateway for astronomical data and telescopes. Physically, the platform is hosted in six cities currently, i.e. Beijing, Nanjing, Shanghai, Kunming, Lijiang and Urumqi, and serving more than 17 thousand users. Achievements from international Virtual Observatories and Cloud Computing are adopted heavily. In the paper, backgrounds of the project, architecture, Cloud Computing environment, key features of the system, current status and future plans are introduced.

astro-ph.IM

Update of the China-VO AstroCloud

As the cyber-infrastructure for Astronomical research from Chinese Virtual Observatory (China-VO) project, AstroCloud has been archived solid progresses during the last one year. Proposal management system and data access system are re-designed. Several new sub-systems are developed, including China-VO PaperData, AstroCloud Statics and Public channel. More data sets and application environments are integrated into the platform. LAMOST DR1, the largest astronomical spectrum archive was released to the public using the platform. The latest progresses will be introduced.

astro-ph.IM

The LAMOST Data Archive and Data Release

The Large sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) is the largest optical telescope in China. In last four years, the LAMOST telescope has published four editions data (pilot data release, data release 1, data release 2 and data release 3). To archive and release these data (raw data, catalog, spectrum etc), we have set up a data cycle management system, including the transfer of data, archiving, backup. And through the evolution of four software versions, mature established data release system.

astro-ph.IM

AstroCloud, a Cyber-Infrastructure for Astronomy Research: Architecture

AstroCloud is a cyber-Infrastructure for Astronomy Research initiated by Chinese Virtual Observatory (China-VO) under funding support from NDRC (National Development and Reform commission) and CAS (Chinese Academy of Sciences). The ultimate goal of this project is to provide a comprehensive end-to-end astronomy research environment where several independent systems seamlessly collaborate to support the full lifecycle of the modern observational astronomy based on big data, from proposal submission, to data archiving, data release, and to in-situ data analysis and processing. In this paper, the architecture and key designs of the AstroCloud platform are introduced, including data access middleware, access control and security framework, extendible proposal workflow, and system integration mechanism.

astro-ph.IM

AstroCloud, a Cyber-Infrastructure for Astronomy Research: Data Archiving and Quality Control

AstroCloud is a cyber-Infrastructure for Astronomy Research initiated by Chinese Virtual Observatory (China-VO) under funding support from NDRC (National Development and Reform commission) and CAS (Chinese Academy of Sciences){\url{http://astrocloud.china-vo.org}}\citep{O8-5_Cui_adassxxiv}. To archive the astronomical data in China, we present the implementation of the astronomical data archiving system (ADAS). Data archiving and quality control are the infrastructure for the AstroCloud. Throughout the data of the entire life cycle, data archiving system standardized data, transferring data, logging observational data, archiving ambient data, And storing these data and metadata in database. Quality control covers the whole process and all aspects of data archiving.

astro-ph.IM

AstroCloud, a Cyber-Infrastructure for Astronomy Research: Data Access and Interoperability

Data access and interoperability module connects the observation proposals, data, virtual machines and software. According to the unique identifier of PI (principal investigator), an email address or an internal ID, data can be collected by PI's proposals, or by the search interfaces, e.g. conesearch. Files associated with the searched results could be easily transported to cloud storages, including the storage with virtual machines, or several commercial platforms like Dropbox. Benefitted from the standards of IVOA (International Observatories Alliance), VOTable formatted searching result could be sent to kinds of VO software. Latter endeavor will try to integrate more data and connect archives and some other astronomical resources.

astro-ph.IM