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Data processing and algorithm development for the WFIRST-AFTA coronagraph: reduction of noise free simulated images, analysis and spectrum extraction with reference star differential imaging

Direct detection and characterization of mature giant or sub-Neptunes exoplanets in the visible require space-based instruments optimized for high-contrast imaging with contrasts of 10-9. In this context, the Wide-Field Infrared Survey Telescope - Astrophysics Focused Telescope Assets (WFIRST-AFTA) will reach raw contrasts of about 8×10-9 to 10-9 using state-of-the-art starlight suppression and wavefront control techniques. A ten-fold contrast improvement is therefore expected using post-processing techniques to reduce the speckle noise level to a factor of at least 10 lower in order to distinguish 10-9 planets from speckles. Point spread function (PSF) subtractions have been successfully applied to ground-based and space-based data with contrasts up to 10-6 but performance has yet to be demonstrated at higher contrast levels. We use both a classical PSF subtraction and the Karunhen-Loéve Image Projection (KLIP) algorithm to reduce noise free WFIRST-AFTA-like simulated images in the context of reference star differential imaging (RDI). The two WFIRST-AFTA baseline coronagraphs are considered for this study: the hybrid lyot coronagraph (HLC) for the imaging channel and the shaped-pupil coronagraph (SPC) for the integral field spectrograph channel (IFS). The two reduction methods are compared with respect to the amount and stability of the aberrations for detection in the imaging channel and preliminary spectra extractions are performed for characterization in the IFS channel.