A Gateway to Astronomical Image Processing: Vera C. RubinObservatory LSST Science Pipelines on AWS

Dino Bektesevic; Hsin-Fang Chiang; Kian-Tat Lim; Todd L. Miller; Greg Thain; Tim Jenness; James Bosch; Andrei Salnikov; Andrew Connolly

A Gateway to Astronomical Image Processing: Vera C. RubinObservatory LSST Science Pipelines on AWS

Instrumentation and Methods for Astrophysics 2020-11-13 v1

Authors: Dino Bektesevic , Hsin-Fang Chiang , Kian-Tat Lim , Todd L. Miller , Greg Thain , Tim Jenness , James Bosch , Andrei Salnikov , Andrew Connolly

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Abstract

The Legacy Survey of Space and Time, operated by the Vera C. Rubin Observatory, is a 10-year astronomical survey due to start operations in 2022 that will image half the sky every three nights. LSST will produce ~20TB of raw data per night which will be calibrated and analyzed in almost real time. Given the volume of LSST data, the traditional subset-download-process paradigm of data reprocessing faces significant challenges. We describe here, the first steps towards a gateway for astronomical science that would enable astronomers to analyze images and catalogs at scale. In this first step we focus on executing the Rubin LSST Science Pipelines, a collection of image and catalog processing algorithms, on Amazon Web Services (AWS). We describe our initial impressions on the performance, scalability and cost of deploying such a system in the cloud.

Keywords

virtual observatory astronomical surveys space missions and telescopes

Cite

@article{arxiv.2011.06044,
  title  = {A Gateway to Astronomical Image Processing: Vera C. RubinObservatory LSST Science Pipelines on AWS},
  author = {Dino Bektesevic and Hsin-Fang Chiang and Kian-Tat Lim and Todd L. Miller and Greg Thain and Tim Jenness and James Bosch and Andrei Salnikov and Andrew Connolly},
  journal= {arXiv preprint arXiv:2011.06044},
  year   = {2020}
}

Comments

5 pages, 6 figures, Gateways 2020 Conference see: https://osf.io/2rqfb/

A Gateway to Astronomical Image Processing: Vera C. RubinObservatory LSST Science Pipelines on AWS

Abstract

Keywords

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