We present a laboratory analysis of the use of a 19-core photonic lantern (PL) in combination with neural network (NN) algorithms as an efficient focal plane wavefront sensor (FP-WFS) for adaptive optics (AO), measuring wavefront errors (WFE) such as low wind effect (LWE), Zernike modes, and Kolmogorov phase maps. The aberrated wavefronts were experimentally simulated using a spatial light modulator with combinations of different phase maps in both the approximately linear regime (average incident RMS WFE of 0.88 rad) and in the nonlinear regime (average incident RMS WFE of 1.5 rad). Results were analyzed using an NN to determine the transfer function of the relationship between the incident WFE at the input modes at the multimode input of the PL and the intensity distribution output at the multicore fiber outputs end of the PL. The root mean square error (RMSE) of the reconstruction of petal and LWE modes were just 2.87×10−2 rad and 2.07×10−1 rad respectively, in the nonlinear regime. The reconstruction RMSE for Zernike combinations ranged from 5.67×10−2 rad to 8.43×10−1 rad, depending on the number of Zernike terms and incident RMS WFE employed. These results demonstrate the promising potential of PLs as an innovative FP-WFS in conjunction with NNs.
Current astronomical detection of Positronium (Ps) atoms through gamma-ray emission is inherently limited by a 3-degree angular resolution. Alternatively, the triplet state of Ps is capable of producing a recombination spectrum in the near-infrared band, which would provide the potential to increase the angular resolution by a factor of 104 . The most promising signature is the Ps Balmer alpha line (Psα) at 1312.22nm. This observation scheme has never been implemented from ground-based telescopes due to the bright airglow. Now, the FBG-based OH suppression technique presents a promising solution for removing airglow emission lines surrounding the target signature. In this proceeding, we present the design and fabrication details of the first astronomy J-band FBG filters and early results of the OH suppression unit specifically developed for Ps detection.
A focal plane wavefront sensor offers major advantages to adaptive optics, including removal of non-commonpath error and providing sensitivity to blind modes (such as petalling). But simply using the observed point spread function (PSF) is not sufficient for wavefront correction, as only the intensity, not phase, is measured. Here we demonstrate the use of a multimode fiber mode converter (photonic lantern) to directly measure the wavefront phase and amplitude at the focal plane. Starlight is injected into a multimode fiber at the image plane, with the combination of modes excited within the fiber a function of the phase and amplitude of the incident wavefront. The fiber undergoes an adiabatic transition into a set of multiple, single-mode outputs, such that the distribution of intensities between them encodes the incident wavefront. The mapping (which may be strongly non-linear) between spatial modes in the PSF and the outputs is stable but must be learned. This is done by a deep neural network, trained by applying random combinations of spatial modes to the deformable mirror. Once trained, the neural network can instantaneously predict the incident wavefront for any set of output intensities. We demonstrate the successful reconstruction of wavefronts produced in the laboratory with low-wind-effect, and an on-sky demonstration of reconstruction of low-order modes consistent with those measured by the existing pyramid wavefront sensor, using SCExAO observations at the Subaru Telescope.
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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