Nulling interferometry is a promising technology to enable prospecting for and characterising sub-stellar companions at extremely close separations and high contrasts. The most scientifically rewarding observations will require extremely well corrected wavefronts in order to deliver consistent, deep nulls that suppress the flux from the central star. We present a two stage system whose first stage deploys a hybrid mode-selective photonic lantern to optimally inject starlight into a single mode fibre. Starlight that does not couple into the primary science fibre is used to sense wavefront errors, including petaling modes that are typically invisible to other methods. The architecture also yields wavefront corrections that are free of non-common path errors. Repeated over multiple telescopes, our system then feeds a second-stage kernel nuller chip implemented as an operating mode of Bifrost in the Asgard instrument suite. This operating mode will enable a variety of science cases including constraining the entropy of formation of giant exoplanets, studying debris disk formation and surveying lensing events for the detection of black holes, all of which drive the requirements for the instrument. We illustrate candidate designs and present early simulations of the modules, finding that Seidr is a feasible means of capitalising a historical window of opportunity to further high contrast and high angular resolution imaging.
Photonic lanterns (PLs) allow the decomposition of highly multimodal light into a simplified modal basis such as single-moded and/or few-moded. They are increasingly finding uses in astronomy, optics, and telecommunications. Calculating propagation through a PL using traditional algorithms takes ∼1 h per simulation on a modern CPU. We demonstrate that neural networks can bridge the disparate opto-electronic systems and, when trained, can achieve a speedup of over five orders of magnitude. We show that this approach can be used to model PLs with manufacturing defects and can be successfully generalized to polychromatic data. We demonstrate two uses of these neural network models: propagating seeing through the PL and performing global optimization for purposes such as PL funnels and PL nullers.
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