Machine-learning approach for optimal self-calibration and fringe tracking in photonic nulling interferometry

Photonic technologies have enabled a generation of nulling interferometers, such as the guided light interferometric nulling technology instrument, potentially capable of imaging exoplanets and circumstellar structure at extreme contrast ratios by suppressing contaminating starlight, and paving the way to the characterization of habitable planet atmospheres. But even with cutting-edge photonic nulling instruments, the achievable starlight suppression (null-depth) is only as good as the instrument's wavefront control and its accuracy is only as good as the instrument's calibration. Here, we present an approach wherein outputs from non-science channels of a photonic nulling chip are used as a precise null-depth calibration method and can also be used in real time for fringe tracking. This is achieved using a deep neural network to learn the true in-situ complex transfer function of the instrument and then predict the instrumental leakage contribution (at millisecond timescales) for the science (nulled) outputs, enabling accurate calibration. In this method, this pseudo-real-time approach is used instead of the statistical methods used in other techniques (such as null self calibration, or NSC) and also resolves the severe effect of read-noise seen when NSC is used with some detector types.