| Abstract | Direct imaging of exoplanets is usually limited by quasi-static speckles. These uncorrected aberrations in a star’s point-spread function (PSF) obscure faint companions and limit the sensitivity of high-contrast imaging instruments. Most current approaches to processing differential imaging sequences like angular differential imaging and spectral differential imaging produce a self-calibrating data set that is combined using a linear least-squares solution to minimize the noise. Due to temporal and chromatic evolution of a telescope’s PSF, the best correlated reference images are usually the most contaminated by the planet, leading to self-subtraction and reducing the planet throughput. In this paper, we present an algorithm that directly optimizes the nonlinear equation for planet signal-to-noise ratio (S/N). This new algorithm does not require us to reject adjacent reference images and optimally balances noise reduction with self-subtraction. We then show how this algorithm can be applied to multiple images simultaneously for a further reduction in correlated noise, directly maximizing the S/N of the final combined image. Finally, we demonstrate the technique on an illustrative sequence of HR8799 using the new Julia-based Signal to Noise Analysis Pipeline. We show that S/N optimization can provide up to a 5× improvement in contrast close to the star. Applicable to both new and archival data, this technique will allow for the detection of fainter, lower mass, and closer-in companions, or achieve the same sensitivity with less telescope time. |
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