The Locus Algorithm III: A Grid Computing system to generate catalogues of optimised pointings for Differential Photometry

TL;DR

This paper presents a Grid Computing system designed to efficiently generate optimized pointings for differential photometry across millions of celestial objects, leveraging high-performance computing for large-scale data analysis.

Contribution

It introduces a scalable Grid Computing architecture tailored for the Locus Algorithm, enabling the processing of extensive astronomical datasets for optimized observational planning.

Findings

Successfully processed over 61 million stars and nearly 24,000 quasars.

Demonstrated the effectiveness of HPC and Grid computing in large-scale astronomical data analysis.

Provided a flexible, layered system architecture for future extensions.

Abstract

This paper discusses the hardware and software components of the Grid Computing system used to implement the Locus Algorithm to identify optimum pointings for differential photometry of 61,662,376 stars and 23,799 quasars. The scale of the data, together with initial operational assessments demanded a High Performance Computing (HPC) system to complete the data analysis. Grid computing was chosen as the HPC solution as the optimum choice available within this project. The physical and logical structure of the National Grid computing Infrastructure informed the approach that was taken. That approach was one of layered separation of the different project components to enable maximum flexibility and extensibility.