Bootstrap resampling as a tool for radio interferometric imaging fidelity assessment

We report on a numerical evaluation of the statistical bootstrap as a technique for radio interferometric imaging fidelity assessment. The development of a fidelity assessment technique is an important scientific prerequisite for automated pipeline reduction of data from modern radio interferometers. We evaluate the statistical performance of two bootstrap methods, the model-based bootstrap and subsample bootstrap, against a Monte Carlo parametric simulation, using interferometric polarization calibration and imaging as the representative problem under study. We find both statistical resampling techniques to be viable approaches to radio interferometric imaging fidelity assessment that merit further investigation. We also report on the development and implementation of a new self-calibration algorithm for radio interferometric polarimetry, which makes no approximations for the polarization source model.