Solving the global photometric self-calibration problem in LSST

We present an innovative method for photometric calibration of massive survey data that will be applied to the Large Synoptic Survey Telescope (LSST). LSST will be a wide-field ground-based system designed to obtain imaging data in six broad photometric bands (ugrizy, 320-1050 nm). Each sky position will be observed multiple times, with about a hundred or more observations per band collected over the main survey area (20,000 sq.deg.) during the anticipated 10 years of operations. Photometric zeropoints are required to be stable in time to 0.5% (rms), and uniform across the survey area to better than 1% (rms). The large number of measurements of each object taken during the survey allows identification of isolated non-variable sources, and forms the basis for LSST's global self-calibration method. Inspired by SDSS's uber-calibration procedure, the self-calibration determines zeropoints by requiring that repeated measurements of non-variable stars must be self-consistent when corrected for variations in atmospheric and instrumental bandpass shapes. This requirement constrains both the instrument throughput and atmospheric extinction. The atmospheric and instrumental bandpass shapes will be explicitly measured using auxiliary instrumentation. We describe the algorithm used, with special emphasis both on the challenges of controlling systematic errors, and how such an approach interacts with the design of the survey, and discuss ongoing simulations of its performance.