Overview of the BlockNormal Event Trigger Generator

TL;DR

The paper reviews the BlockNormal method for detecting unmodeled gravitational wave bursts by identifying change-points in time series data where statistical properties shift, segmenting data into stationary blocks for event detection.

Contribution

It provides an overview of the BlockNormal event trigger generator, explaining its approach to identifying candidate gravitational wave events through change-point detection.

Findings

Effective segmentation of data into stationary blocks

Identification of candidate events based on statistical inconsistencies

Facilitates subsequent detailed multi-detector analysis

Abstract

In the search for unmodeled gravitational wave bursts, there are a variety of methods that have been proposed to generate candidate events from time series data. Block Normal is a method of identifying candidate events by searching for places in the data stream where the characteristic statistics of the data change. These change-points divide the data into blocks in which the characteristics of the block are stationary. Blocks in which these characteristics are inconsistent with the long term characteristic statistics are marked as Event-Triggers which can then be investigated by a more computationally demanding multi-detector analysis.