  The treatment of systematic noise is a significant aspect of transit
exoplanet data processing due to the signal strength of systematic noise
relative to a transit signal. Typically the standard approach to transit
detection is to estimate and remove systematic noise independently of and prior
to 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 by
overfitting and thereby reduce detection efficiency. We present a Bayesian
framework for joint detection of transit signals and systematic noise
characterization and describe the implementation of these detectors as optimal
Neyman-Pearson likelihood ratio tests. The joint detectors reduce to closed
form as matched filters under the assumption of a Gaussian Bayesian prior for
the systematic noise. The performance of the exploratory detectors was
evaluated in injection tests and show $\sim 2\%$ improvement in overall
detection efficiency relative to the standard approach. We find that joint
detection efficiency is specifically improved for short-period, low
transit-depth exoplanet transits, providing evidence in support of the
hypothesis that joint detection may indeed help to mitigate overfitting. In
addition, an initial feasibility test to detect known exoplanets in Kepler data
using the joint detectors produced encouraging preliminary results.
