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Direct imaging of exoplanets is usually limited by quasi-static speckles.These uncorrected aberrations in a star's point spread function (PSF) obscurefaint companions and limit the sensitivity of high-contrast imaginginstruments. Most current approaches to processing differential imagingsequences like angular differential imaging (ADI) and spectral differentialimaging (SDI) produce a self-calibrating dataset that are combined in a linearleast squares solution to minimize the noise. Due to temporal and chromaticevolution of a telescope's PSF, the best correlated reference images areusually the most contaminated by the planet, leading to self-subtraction andreducing the planet throughput. In this paper, we present an algorithm thatdirectly optimizes the non-linear equation for planet signal to noise ratio(SNR). This new algorithm does not require us to reject adjacent referenceimages and optimally balances noise reduction with self-subtraction. We thenshow how this algorithm can be applied to multiple images simultaneously for afurther reduction in correlated noise, directly maximizing the SNR of the finalcombined image. Finally, we demonstrate the technique on an illustrativesequence of HR8799 using the new Julia-based Signal to Noise Analysis Pipeline(SNAP). We show that SNR optimization can provide up to a $5\times$ improvementin contrast close to the star. Applicable to both new and archival data, thistechnique will allow for the detection of lower mass, and closer in companions,or achieve the same sensitivity with less telescope time.

Improved Contrast in Images of Exoplanets using Direct SNR Optimization