弹性数据分仓:用于时域天体物理分析的时间序列绘制

IF 0.4 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
S. Sako
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引用次数: 0

摘要

时域天体物理学分析(TDAA)涉及对可能包含不相关信息的天体现象的观测调查,因为几个因素,其中之一是光学望远镜的灵敏度。数据装仓是天体物理学分析中消除不一致性和澄清原始数据主要特征的典型技术。它将数据序列拆分为具有固定大小的较小的仓,然后将它们绘制成一种新的表示形式。在这项研究中,我们引入了一种新方法,称为弹性数据仓(EBinning),使用两种基于线性回归和Hoeffding不等式的Student t检验的统计指标自动调整每个仓的大小。EBinning在提取时间序列数据的相关特征(称为光曲线)方面优于TDAA中的知名算法。我们证明了使用EBinning成功地表示了从Kiso Schmidt望远镜收集的光曲线中的各种特性,以及它在TDAA瞬态检测中的适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Elastic Data Binning: Time-Series Sketching for Time-Domain Astrophysics Analysis
Time-domain astrophysics analysis (TDAA) involves observational surveys of celestial phenomena that may contain irrelevant information because of several factors, one of which is the sensitivity of the optical telescopes. Data binning is a typical technique for removing inconsistencies and clarifying the main characteristics of the original data in astrophysics analysis. It splits the data sequence into smaller bins with a fixed size and subsequently sketches them into a new representation form. In this study, we introduce a novel approach, called elastic data binning (EBinning), to automatically adjust each bin size using two statistical metrics based on the Student's t-test for linear regression and Hoeffding inequality. EBinning outperforms well-known algorithms in TDAA for extracting relevant characteristics of time-series data, called lightcurve. We demonstrate the successful representation of various characteristics in the lightcurve gathered from the Kiso Schmidt telescope using EBinning and its applicability for transient detection in TDAA.
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来源期刊
Applied Computing Review
Applied Computing Review COMPUTER SCIENCE, INFORMATION SYSTEMS-
自引率
40.00%
发文量
8
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