ASIVA - 恒星变量观测和计算分析平台

IF 1.9 4区 物理与天体物理 Q2 ASTRONOMY & ASTROPHYSICS New Astronomy Pub Date : 2024-04-06 DOI:10.1016/j.newast.2024.102232
Parvej Reja Saleh , Tanveer Singh , Debasish Hazarika , Surabhi Rajkumari , Saurabh Rajkumar , Pritam Das , Padmakar Singh Parihar , Eeshankur Saikia
{"title":"ASIVA - 恒星变量观测和计算分析平台","authors":"Parvej Reja Saleh ,&nbsp;Tanveer Singh ,&nbsp;Debasish Hazarika ,&nbsp;Surabhi Rajkumari ,&nbsp;Saurabh Rajkumar ,&nbsp;Pritam Das ,&nbsp;Padmakar Singh Parihar ,&nbsp;Eeshankur Saikia","doi":"10.1016/j.newast.2024.102232","DOIUrl":null,"url":null,"abstract":"<div><p>The astronomical data analysis consists of two crucial process; data reduction of the captured images and data analysis of the derived magnitudes. We present the platform ASIVA, a data analysis platform which comes along with a data reduction pipeline. The data reduction pipeline gives flexibility to analyse the FITS images and also perform image alignment for detecting the correct image coordinates for required objects. It can be custom scheduled with cron jobs so that it picks the latest data and appends the results accordingly. The data analysis platform allows user to effectively analyse the ensemble data and perform accurate data processing and grouping with ease. It is integrated with a custom algorithm to detect the variable stars from an ensemble with its relative standard deviations. The statistical, spectral and non-linear dynamics features can be extracted from time series data which can be eventually used for in-depth analysis. To validate the capability, we have analysed 15 nights of Orion Nebula Cluster field in I filter which had 1585 images. ASIVA reduces manual effort to a great extent thus saves analysis time and excludes human errors.</p></div>","PeriodicalId":54727,"journal":{"name":"New Astronomy","volume":"111 ","pages":"Article 102232"},"PeriodicalIF":1.9000,"publicationDate":"2024-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"ASIVA – Platform for observational and computational analysis of stellar variables\",\"authors\":\"Parvej Reja Saleh ,&nbsp;Tanveer Singh ,&nbsp;Debasish Hazarika ,&nbsp;Surabhi Rajkumari ,&nbsp;Saurabh Rajkumar ,&nbsp;Pritam Das ,&nbsp;Padmakar Singh Parihar ,&nbsp;Eeshankur Saikia\",\"doi\":\"10.1016/j.newast.2024.102232\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The astronomical data analysis consists of two crucial process; data reduction of the captured images and data analysis of the derived magnitudes. We present the platform ASIVA, a data analysis platform which comes along with a data reduction pipeline. The data reduction pipeline gives flexibility to analyse the FITS images and also perform image alignment for detecting the correct image coordinates for required objects. It can be custom scheduled with cron jobs so that it picks the latest data and appends the results accordingly. The data analysis platform allows user to effectively analyse the ensemble data and perform accurate data processing and grouping with ease. It is integrated with a custom algorithm to detect the variable stars from an ensemble with its relative standard deviations. The statistical, spectral and non-linear dynamics features can be extracted from time series data which can be eventually used for in-depth analysis. To validate the capability, we have analysed 15 nights of Orion Nebula Cluster field in I filter which had 1585 images. ASIVA reduces manual effort to a great extent thus saves analysis time and excludes human errors.</p></div>\",\"PeriodicalId\":54727,\"journal\":{\"name\":\"New Astronomy\",\"volume\":\"111 \",\"pages\":\"Article 102232\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2024-04-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"New Astronomy\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1384107624000460\",\"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":"New Astronomy","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1384107624000460","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

摘要

天文数据分析包括两个关键过程:对拍摄到的图像进行数据还原和对得出的星等进行数据分析。我们介绍的 ASIVA 平台是一个数据分析平台,它配备了数据还原管道。数据还原流水线可以灵活地分析 FITS 图像,还可以执行图像配准,为所需的天体检测正确的图像坐标。它可以通过 cron 作业进行自定义调度,以便选取最新数据并相应地添加结果。数据分析平台可让用户有效地分析集合数据,并轻松执行精确的数据处理和分组。它集成了一种定制算法,可以从集合中检测出变星及其相对标准偏差。可以从时间序列数据中提取统计、光谱和非线性动力学特征,最终用于深入分析。为了验证其能力,我们分析了猎户座星云星团15个夜晚的I滤光片,共1585幅图像。ASIVA 在很大程度上减少了人工操作,从而节省了分析时间并避免了人为错误。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

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
ASIVA – Platform for observational and computational analysis of stellar variables

The astronomical data analysis consists of two crucial process; data reduction of the captured images and data analysis of the derived magnitudes. We present the platform ASIVA, a data analysis platform which comes along with a data reduction pipeline. The data reduction pipeline gives flexibility to analyse the FITS images and also perform image alignment for detecting the correct image coordinates for required objects. It can be custom scheduled with cron jobs so that it picks the latest data and appends the results accordingly. The data analysis platform allows user to effectively analyse the ensemble data and perform accurate data processing and grouping with ease. It is integrated with a custom algorithm to detect the variable stars from an ensemble with its relative standard deviations. The statistical, spectral and non-linear dynamics features can be extracted from time series data which can be eventually used for in-depth analysis. To validate the capability, we have analysed 15 nights of Orion Nebula Cluster field in I filter which had 1585 images. ASIVA reduces manual effort to a great extent thus saves analysis time and excludes human errors.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
New Astronomy
New Astronomy 地学天文-天文与天体物理
CiteScore
4.00
自引率
10.00%
发文量
109
审稿时长
13.6 weeks
期刊介绍: New Astronomy publishes articles in all fields of astronomy and astrophysics, with a particular focus on computational astronomy: mathematical and astronomy techniques and methodology, simulations, modelling and numerical results and computational techniques in instrumentation. New Astronomy includes full length research articles and review articles. The journal covers solar, stellar, galactic and extragalactic astronomy and astrophysics. It reports on original research in all wavelength bands, ranging from radio to gamma-ray.
期刊最新文献
A robust assessment of the local anisotropy of the Hubble constant in the Pantheon+ sample A comprehensive study on the K2-type binary V1393 Tau in four-year observations The baryonic mass estimates of the Milky Way halo in the form of high-velocity clouds Modifications of SPH towards three-dimensional simulations of an icy moon with internal ocean TESS and AAVSO observations of the eclipsing Z Cam-type cataclysmic variable V416 Dra
×
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