Coronagraphic data post-processing using projections on instrumental modes

Abstract

High contrast astronomy has yielded the direct observations of over a dozen exoplanets and a multitude of brown dwarfs and circumstellar disks. Despite advances in coronagraphy and wavefront control, high contrast observations are still plagued by residual wavefront aberrations. Post-processing techniques can provide an additional boost in separating residual aberrations from an astrophysical signal. This work explores using a coronagraph instrument model to guide post-processing. We consider the propagation of signals and wavefront error through a coronagraphic instrument, and approach the post-processing problem using "robust observables." We model and approximate the instrument response function of a classical Lyot coronagraph (CLC) and find from it a projection that removes the dominant error modes.

We use this projection to post-process synthetically generated data, and assess the performance of the new model-based post-processing approach compared to using the raw intensity data by calculating their respective flux ratio detection limits. We extend our analysis to include the presence of a dark hole using a simulation of the CLC on the High-contrast imager for complex aperture telescopes (HiCAT) testbed. We find that for non-time-correlated wavefront errors, using the robust observables modestly increases our sensitivity to the signal of a binary companion for most of the range of separations over which our treatment is valid, for example, by up to 50% at 7.5[lambda]/D. For time-correlated wavefront errors, the results vary depending on the test statistic used and degree of correlation. The modest improvement using robust observables with non-time-correlated errors is shown to extend to a CLC with a dark hole created by the stroke minimization algorithm.

Future work exploring the inclusion of statistical whitening processes will allow for a more complete characterization of the robust observables with time-correlated noise. We discuss the dimensionality of coronagraph self-calibration problem and motivate future directions in the joint study of coronagraphy and post-processing.