Overview of the distributed image processing infrastructure to produce the Legacy Survey of Space and Time

¹ CNRS, CC-IN2P3, 21 avenue Pierre de Coubertin, CS70202, 69627 Villeurbanne CEDEX, France
² University of Edinburgh, Edinburgh, UK
³ Fermi National Accelerator Laboratory, Batavia, IL, USA
⁴ Lancaster University, Lancaster, UK
⁵ Vera C. Rubin Observatory Project Office, 950 N. Cherry Ave., Tucson, AZ 85719, USA
⁶ Brookhaven National Laboratory, Upton, NY, USA
⁷ STFC, Harwell, UK
⁸ SLAC National Accelerator Laboratory, 2575 Sand Hill Rd., Menlo Park, CA 94025, USA

^* e-mail: fabio@in2p3.fr

Published online: 6 May 2024

Abstract

The Vera C. Rubin Observatory is preparing to execute the most ambitious astronomical survey ever attempted, the Legacy Survey of Space and Time (LSST). Currently the final phase of construction is under way in the Chilean Andes, with the Observatory’s ten-year science mission scheduled to begin in 2025. Rubin’s 8.4-meter telescope will nightly scan the southern hemisphere collecting imagery in the wavelength range 320–1050 nm covering the entire observable sky every 4 nights using a 3.2 gigapixel camera, the largest imaging device ever built for astronomy. Automated detection and classification of celestial objects will be performed by sophisticated algorithms on high-resolution images to progressively produce an astronomical catalog eventually composed of 20 billion galaxies and 17 billion stars and their associated physical properties.

In this article we present an overview of the system currently being constructed to perform data distribution as well as the annual campaigns which reprocess the entire image dataset collected since the beginning of the survey. These processing campaigns will utilize computing and storage resources provided by three Rubin data facilities (one in the US and two in Europe). Each year a Data Release will be produced and disseminated to science collaborations for use in studies comprising four main science pillars: probing dark matter and dark energy, taking inventory of solar system objects, exploring the transient optical sky and mapping the Milky Way.

Also presented is the method by which we leverage some of the common tools and best practices used for management of large-scale distributed data processing projects in the high energy physics and astronomy communities. We also demonstrate how these tools and practices are utilized within the Rubin project in order to overcome the specific challenges faced by the Observatory.