Distinguishing Time Clustering of Astrophysical Bursts

TL;DR

This paper develops methods to identify and analyze periodic windowed activity in astrophysical burst time series, distinguishing it from random clustering, and applies these techniques to data from SGR1935+2154.

Contribution

It introduces novel time clustering analysis methods for detecting periodic windows in burst activity, accounting for absence of bursts, and demonstrates their effectiveness on real data.

Findings

Identified a 231-day period with 55% duty cycle in SGR1935+2154.

Successfully predicted active and inactive periods based on the analysis.

Provided a framework for distinguishing windowed periodic behavior from random clustering.

Abstract

Many astrophysical bursts can recur, and their time series structure or pattern could be closely tied to the emission and system physics. While analysis of periodic events is well established, some sources, e.g. some fast radio bursts and soft gamma-ray emitters, are suspected of more subtle and less explored periodic windowed behavior: the bursts themselves are not periodic, but the activity only occurs during periodic windows. We focus here on distinguishing periodic windowed behavior from merely clustered events through time clustering analysis, using techniques analogous to spatial clustering, demonstrating methods for identifying and characterizing the behavior. An important aspect is accounting for the ``curious incident of the dog in the night time'' - lack of bursts carries information. As a worked example, we analyze six years of data from the soft gamma repeater SGR1935+2154, deriving a window period of 231 days and 55% duty cycle; this has now successfully predicted both active and inactive periods.