  The Vera C. Rubin Observatory will advance many areas of astronomy over the
next decade with its unique wide-fast-deep multi-color imaging survey, the
Legacy Survey of Space and Time (LSST). 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.
