Detection from a multi-channel sensor using a hierarchical Bayesian model

Direct imaging of exoplanets involves very low signal-to-noise ratio data, that need to be carefully acquired and processed. Multi-band devices enable the simultaneous record of images in different spectral bands. They can be used either for spectroscopy purposes or to improve detection capabilities. This work aims at detecting a potential source, when the source moves on a random background spatially and inter-spectrally correlated. A hierarchic Bayesian model is derived to take into account correlations and their randomness, and the high dynamic range involved in potentially low signal to noise ratio data. The point null hypothesis test is addressed using the posterior distribution of the likelihood ratio. Its percentiles are computed using a simple Markov Chain Monte Carlo method. This algorithm is illustrated using ID simulated data of a dual band signal.