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The treatment of systematic noise is a significant aspect of transitexoplanet data processing due to the signal strength of systematic noiserelative to a transit signal. Typically the standard approach to transitdetection is to estimate and remove systematic noise independently of and priorto a transit detection test. If a transit signal is present in a light curve,the process of systematic noise removal may distort the transit signal byoverfitting and thereby reduce detection efficiency. We present a Bayesianframework for joint detection of transit signals and systematic noisecharacterization and describe the implementation of these detectors as optimalNeyman-Pearson likelihood ratio tests. The joint detectors reduce to closedform as matched filters under the assumption of a Gaussian Bayesian prior forthe systematic noise. The performance of the exploratory detectors wasevaluated in injection tests and show $\sim 2\%$ improvement in overalldetection efficiency relative to the standard approach. We find that jointdetection efficiency is specifically improved for short-period, lowtransit-depth exoplanet transits, providing evidence in support of thehypothesis that joint detection may indeed help to mitigate overfitting. Inaddition, an initial feasibility test to detect known exoplanets in Kepler datausing the joint detectors produced encouraging preliminary results.

Bayesian Methods for Joint Exoplanet Transit Detection and Systematic Noise Characterization