AstroSA: An astronomical observation scheduler assessment framework in python

IF 1.9 4区 物理与天体物理 Q2 ASTRONOMY & ASTROPHYSICS Astronomy and Computing Pub Date : 2024-03-07 DOI:10.1016/j.ascom.2024.100806
H. Xie , Z. Kang , X. Jiang
{"title":"AstroSA: An astronomical observation scheduler assessment framework in python","authors":"H. Xie ,&nbsp;Z. Kang ,&nbsp;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}
引用次数: 0

Abstract

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.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
AstroSA:用 python 编写的天文观测调度评估框架
时域天文学作为天文学研究的一个主要方面,需要大量增加望远镜的使用时间。高效的调度程序对于有效处理大量观测请求至关重要。然而,天文台常用的调度程序尚未充分利用数学和计算机科学的进步。为了在天文学与这些领域的最新成果之间建立联系,我们提出了天文观测调度评估框架(AstroSA),并以 Python 软件包的形式实现。AstroSA 提供了一个快速、用户友好的调度器量化评估工具,内置五个指标:观测数据的预期质量、开销比、科学价值、调度率和与最佳气团的比率。此外,AstroSA 还包括一个默认的虚拟望远镜和一个云覆盖夜,因此用户只需进行最少的设置即可开始使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Astronomy and Computing
Astronomy and Computing ASTRONOMY & 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.
期刊最新文献
AstroMLab 1: Who wins astronomy jeopardy!? Extended black hole solutions in Rastall theory of gravity Classification of galaxies from image features using best parameter selection by horse herd optimization algorithm (HOA) Accelerating radio astronomy imaging with RICK A numerical solution of Schrödinger equation for the dynamics of early universe
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1