Time series data mining for the Gaia variability analysis

TL;DR

This paper discusses the development of scalable, open-source solutions for analyzing massive time series data from the Gaia mission to identify celestial variability.

Contribution

It introduces a distributed, data-oriented architecture with open-source tools to handle Gaia's petabyte-scale time series data for variability analysis.

Findings

Successfully processed over 800 billion observations

Developed scalable, distributed data processing pipeline

Enabled incremental analysis of large-scale astronomical data

Abstract

Gaia is an ESA cornerstone mission, which was successfully launched December 2013 and commenced operations in July 2014. Within the Gaia Data Processing and Analysis consortium, Coordination Unit 7 (CU7) is responsible for the variability analysis of over a billion celestial sources and nearly 4 billion associated time series (photometric, spectrophotometric, and spectroscopic), encoding information in over 800 billion observations during the 5 years of the mission, resulting in a petabyte scale analytical problem. In this article, we briefly describe the solutions we developed to address the challenges of time series variability analysis: from the structure for a distributed data-oriented scientific collaboration to architectural choices and specific components used. Our approach is based on Open Source components with a distributed, partitioned database as the core to handle incrementally: ingestion, distributed processing, analysis, results and export in a constrained time window.