Elastic Data Binning: Time-Series Sketching for Time-Domain Astrophysics Analysis

Abstract

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.