Detection of faint exoplanets in multispectral data

The detection of extrasolar planets is extremely difficult due to their extreme faintness and proximity to their parent stars. Direct, ground-based detection is particularly challenging and despite the incredible contrast achieved by recently commissioned planet hunters, such as SPHERE and GPI, through the implementation of extreme adaptive optics and advanced coronagraphy, direct detection is still limited by the presence of residual speckles. Due to these speckles, data post-processing is essential to accurately extract exoplanet signals. This thesis develops a maximum-likelihood approach to the efficient detection and characterisation of planets in speckle noise when multi-spectral data are available. Computational approaches are implemented in order to dramatically increase the efficiency of the detection algorithm through the use of graphics processing units. A method to achromatise focal plane coronagraphs using a Wynne corrector is proposed. The impact of the achromatisation is investigated and shown to have significant impact on the broadband stability of the on-axis point spread function. This in turn is shown to positively affect the performance of the tested detection techniques. The performance of the developed detection algorithm, in terms of photometry and astrometry, is assessed extensively on data that has been simulated to replicate that produced by the integral field spectrograph of the SPHERE instrument. Superior detection signal-to-noise ratios are achieved versus spectral deconvolution, while also preserving photometric accuracy. The algorithm is also applied to real data, produced by SPHERE, of HR8799 in which two companions are present. The maximum likelihood detection pipeline is shown to extract both companion signals with high signal-to-noise while also calculating contrast curves for both companions that agree with accepted values