Waiting Times of Quasi-homologous Coronal Mass Ejections from Super Active Regions

TL;DR

This study statistically analyzes the waiting times of quasi-homologous CMEs from super active regions, revealing a two-component distribution and suggesting that CME interactions, rather than energy buildup, trigger these events.

Contribution

It provides the first statistical characterization of CME waiting times from super active regions, highlighting the role of CME interactions over energy accumulation.

Findings

Waiting times have a two-component distribution with an 18-hour separation.

A 7-hour peak indicates the timescale of instability growth.

CME speeds and occurrence rates are independent of waiting times.

Abstract

Why and how may some active regions (ARs) frequently produce coronal mass ejections (CMEs)? It is one of the key questions to deepen our understanding of the mechanisms and processes of energy accumulation and sudden release in ARs and to improve our capability of space weather prediction. Although some case studies have been made, the question is still far from fully answered. This issue is now being tried to address statistically through an investigation of waiting times of quasi-homologous CMEs from super ARs in solar cycle 23. It is found that the waiting times of quasi-homologous CMEs have a two-component distribution with a separation at about 18 hours. The first component is a Gaussian-like distribution with a peak at about 7 hours, which indicates a tight physical connection between these quasi-homologous CMEs. The likelihood of occurrences of two or more CMEs faster than 1200 km s-1 from the same AR within 18 hours is about 20%. Furthermore, the correlation analysis among CME waiting times, CME speeds and CME occurrence rates reveals that these quantities are independent to each other, suggesting that the perturbation by preceding CMEs rather than free energy input be the direct cause of quasi-homologous CMEs. The peak waiting time of 7 hours probably characterize the time scale of the growth of instabilities triggered by preceding CMEs. This study uncovers more clues from a statistical perspective for us to understand quasi-homologous CMEs as well as CME-rich ARs.