Faro: A framework for measuring the scientific performance of petascale Rubin Observatory data products

Leanne P. Guy, Keith Bechtol, Jeffrey L. Carlin, Erik Dennihy, Peter S. Ferguson, K. Simon Krughoff, Robert H. Lupton, Colin T. Slater, Krzysztof Findeisen, Arun Kannawadi, Lee S. Kelvin, Nate B. Lust, Lauren A. Macarthur, Michael N. Martinez, Sophie L. Reed, Dan S. Taranu, W. Michael Wood-Vasey

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The Vera C. Rubin Observatory will advance many areas of astronomy over the next decade with its unique widefast-deep multi-color imaging survey, the Legacy Survey of Space and Time (LSST).1 The LSST will produce approximately 20TB of raw data per night, which will be automatically processed by the LSST Science Pipelines to generate science-ready data products-processed images, catalogs and alerts. To ensure that these data products enable transformative science with LSST, stringent requirements have been placed on their quality and scientific fidelity, for example on image quality and depth, astrometric and photometric performance, and object recovery completeness. In this paper we introduce faro, a framework for automatically and efficiently computing scientific performance metrics on the LSST data products for units of data of varying granularity, ranging from single-detector to full-survey summary statistics. By measuring and monitoring metrics, we are able to evaluate trends in algorithmic performance and conduct regression testing during development, compare the performance of one algorithm against another, and verify that the LSST data products will meet performance requirements by comparing to specifications. We present initial results using faro to characterize the performance of the data products produced on simulated and precursor data sets, and discuss plans to use faro to verify the performance of the LSST commissioning data products.

Original languageEnglish (US)
Title of host publicationSoftware and Cyberinfrastructure for Astronomy VII
EditorsJorge Ibsen, Gianluca Chiozzi
ISBN (Electronic)9781510653597
StatePublished - 2022
EventSoftware and Cyberinfrastructure for Astronomy VII 2022 - Montreal, Canada
Duration: Jul 17 2022Jul 21 2022

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


ConferenceSoftware and Cyberinfrastructure for Astronomy VII 2022

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


  • Data Management
  • LSST
  • Legacy Survey of Space and Time
  • Metric
  • Rubin Observatory
  • Verification


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