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

doi:10.1117/12.2628887

29 August 2022 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

Author Affiliations +

Proceedings Volume 12189, Software and Cyberinfrastructure for Astronomy VII; 121890M (2022) https://doi.org/10.1117/12.2628887
Event: SPIE Astronomical Telescopes + Instrumentation, 2022, Montréal, Québec, Canada

Abstract

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.

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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, and W. Michael Wood-Vasey "Faro: a framework for measuring the scientific performance of petascale Rubin Observatory data products", Proc. SPIE 12189, Software and Cyberinfrastructure for Astronomy VII, 121890M (29 August 2022); https://doi.org/10.1117/12.2628887

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