{"title":"AstroSA:用 python 编写的天文观测调度评估框架","authors":"H. Xie , Z. Kang , X. Jiang","doi":"10.1016/j.ascom.2024.100806","DOIUrl":null,"url":null,"abstract":"<div><p>Time-domain astronomy, as a leading aspect of astronomical research, demands a significant increase in telescope hours. An efficient scheduler is crucial to handle the large number of observational requests effectively. However, the commonly used schedulers in observatories have not yet fully utilized the advancements in mathematics and computer science. In order to establish a connection between astronomy and the latest achievements in these fields, we propose the Astronomical Observing Scheduler Assessment Framework (<span>AstroSA</span>), implemented as a Python package. The <span>AstroSA</span> offers a rapid and user-friendly quantitative evaluator of the scheduler with five built-in metrics: expected quality of observed data, overhead ratio, scientific value, schedule rate, and ratio to the best airmass. Additionally, <span>AstroSA</span> includes a default virtual telescope and a night of cloud coverage, so that users can start to use it with minimal settings.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"47 ","pages":"Article 100806"},"PeriodicalIF":1.9000,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"AstroSA: An astronomical observation scheduler assessment framework in python\",\"authors\":\"H. Xie , Z. Kang , X. Jiang\",\"doi\":\"10.1016/j.ascom.2024.100806\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Time-domain astronomy, as a leading aspect of astronomical research, demands a significant increase in telescope hours. An efficient scheduler is crucial to handle the large number of observational requests effectively. However, the commonly used schedulers in observatories have not yet fully utilized the advancements in mathematics and computer science. In order to establish a connection between astronomy and the latest achievements in these fields, we propose the Astronomical Observing Scheduler Assessment Framework (<span>AstroSA</span>), implemented as a Python package. The <span>AstroSA</span> offers a rapid and user-friendly quantitative evaluator of the scheduler with five built-in metrics: expected quality of observed data, overhead ratio, scientific value, schedule rate, and ratio to the best airmass. Additionally, <span>AstroSA</span> includes a default virtual telescope and a night of cloud coverage, so that users can start to use it with minimal settings.</p></div>\",\"PeriodicalId\":48757,\"journal\":{\"name\":\"Astronomy and Computing\",\"volume\":\"47 \",\"pages\":\"Article 100806\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2024-03-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Astronomy and Computing\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2213133724000210\",\"RegionNum\":4,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ASTRONOMY & ASTROPHYSICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Astronomy and Computing","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2213133724000210","RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ASTRONOMY & ASTROPHYSICS","Score":null,"Total":0}
AstroSA: An astronomical observation scheduler assessment framework in python
Time-domain astronomy, as a leading aspect of astronomical research, demands a significant increase in telescope hours. An efficient scheduler is crucial to handle the large number of observational requests effectively. However, the commonly used schedulers in observatories have not yet fully utilized the advancements in mathematics and computer science. In order to establish a connection between astronomy and the latest achievements in these fields, we propose the Astronomical Observing Scheduler Assessment Framework (AstroSA), implemented as a Python package. The AstroSA offers a rapid and user-friendly quantitative evaluator of the scheduler with five built-in metrics: expected quality of observed data, overhead ratio, scientific value, schedule rate, and ratio to the best airmass. Additionally, AstroSA includes a default virtual telescope and a night of cloud coverage, so that users can start to use it with minimal settings.
Astronomy and ComputingASTRONOMY & ASTROPHYSICSCOMPUTER SCIENCE,-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
CiteScore
4.10
自引率
8.00%
发文量
67
期刊介绍:
Astronomy and Computing is a peer-reviewed journal that focuses on the broad area between astronomy, computer science and information technology. The journal aims to publish the work of scientists and (software) engineers in all aspects of astronomical computing, including the collection, analysis, reduction, visualisation, preservation and dissemination of data, and the development of astronomical software and simulations. The journal covers applications for academic computer science techniques to astronomy, as well as novel applications of information technologies within astronomy.