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The recovery of an exoplanet's atmospheric parameters from its spectrumrequires accurate knowledge of the spectral errors and covariances.Unfortunately, the complex image processing used in high-contrastintegral-field spectrograph (IFS) observations generally produces spectralcovariances that are poorly understood and often ignored. In this work, we showhow to measure the spectral errors and covariances and include themself-consistently in parameter retrievals. By combining model exoplanet spectrawith a realistic noise model generated from GPI early science data, we showthat ignoring spectral covariance in high-contrast IFS data can both biasinferred parameters and lead to unreliable confidence regions on thoseparameters. This problem is made worse by the common practice of scaling the$\chi^2$ per degree of freedom to unity; the input parameters then fall outsidethe $95\%$ confidence regions in as many as ${\sim}80\%$ of noise realizations.The biases we observe can approach the typical levels of precision achieved inhigh-contrast spectroscopy. Accounting for realistic priors in fully Bayesianretrievals can also have a significant impact on the inferred parameters.Plausible priors on effective temperature and surface gravity can vary by anorder of magnitude across the confidence regions appropriate for objects withweak age constraints; priors for objects with good age constraints aredominated by modeling uncertainties. Our methods are directly applicable toexisting high-contrast IFSs including GPI and SPHERE, as well as upcominginstruments like CHARIS and, ultimately, WFIRST-AFTA.

The Measurement, Treatment, and Impact of Spectral Covariance and Bayesian Priors in Integral-Field Spectroscopy of Exoplanets