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Retrieval of exoplanetary atmospheric properties from their transmissionspectra commonly assumes that the errors in the data are Gaussian andindependent. However, non-Gaussian noise can occur due to instrumental orstellar systematics and merging discrete datasets. We investigate the effect ofcorrelated noise and constrain the potential biases incurred in the retrievedposteriors. We simulate multiple noise instances of synthetic data and performretrievals to obtain statistics of goodness-of-retrieval for varying noisemodels. We find that correlated noise allows for overfitting the spectrum,thereby yielding better goodness-of-fit on average but degrading the overallaccuracy of retrievals. In particular, correlated noise can manifest as anapparent non-Rayleigh slope in the optical range, leading to an incorrectestimate of cloud/haze parameters. We also find that higher precision causescorrelated results to be further off from the input values in terms ofestimated errors. As such, we emphasize that caution must be taken in analyzingretrieved posteriors and that estimated parameter uncertainties are bestunderstood as lower limits. Finally, we show that while correlated noise cannotbe be reliably distinguished with HST observations, inferring its presence andstrength may be possible with JWST observations.

Understanding the Effects of Systematics in Exoplanetary Atmospheric Retrievals