Maximum likelihood-based method for angular differential imaging

Abstract

In the context of the SPHERE planet finder project, we further develop a recently proposed method, based on detection theory, for the efficient detection of planets using angular differential imaging. The proposed method uses the fact that with the SPHERE instrument the field rotates during the night, and can additionally use the fact that at each acquisition time, two images are recorded by the IRDIS instrument in two different spectral channel. The method starts with the appropriate combination of images recorded at different times, and potentially in different spectral channels, into so-called pseudo-data. It then uses jointly all these pseudo-data in a Maximum-Likelihood (ML) framework to detect the position and amplitude of potential companions of the observed star, taking into account the mixture of photon and detector noises and a positivity constraint on the planet's amplitude. A reasonable detection criterion is also proposed; it is based on the computation of the noise propagation from the images to the estimated flux of the potential planet. The method is validated on data simulating realistic conditions of operation, including residual aberrations before and after the coronagraph, residual turbulence after adaptive optics correction, and noise.