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Directly observing exoplanets with coronagraphs is impeded by the presence ofspeckles from aberrations in the optical path, which can be mitigated inhardware with wavefront control as well as in post-processing. This workexplores using an instrument model in post-processing to separate astrophysicalsignals from residual aberrations in coronagraphic data. The effect ofwavefront error (WFE) on the coronagraphic intensity consists of a linearcontribution and a quadratic contribution. When either of the terms is muchlarger than the other, the instrument response can be approximated by atransfer matrix mapping WFE to detector plane intensity. From this transfermatrix, a useful projection onto instrumental modes that removes the dominanterror modes can be derived. We apply this projection to synthetically generatedRoman Space Telescope hybrid Lyot coronagraph (HLC) data to extract "robustobservables," which can be used instead of raw data for applications such asdetection testing. The projection improves planet flux ratio detection limitsby about 28% in the linear regime and by over a factor of 2 in the quadraticregime, illustrating that robust observables can increase sensitivity toastrophysical signals and improve the scientific yield from coronagraphic data.While this approach does not require additional information such asobservations of reference stars or modulations of a deformable mirror, it canand should be combined with these other techniques, acting as a model-informedprior in an overall post-processing strategy.

Coronagraphic Data Post-processing Using Projections on Instrumental Modes