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Here we present a laboratory analysis of the use of a 19-core photoniclantern (PL) in combination with neural network (NN) algorithms as an efficientfocal plane wavefront sensor (FP-WFS) for adaptive optics (AO), measuringwavefront errors such as low wind effect (LWE), Zernike modes and Kolmogorovphase maps. The aberrated wavefronts were experimentally simulated using aSpatial Light Modulator (SLM) with combinations of different phase maps in boththe linear regime (average incident RMS wavefront error (WFE) of 0.88 rad) andin the non-linear regime (average incident RMS WFE of 1.5 rad). Results wereanalysed using a NN to determine the transfer function of the relationshipbetween the incident wavefront error (WFE) at the input modes at the multimodeinput of the PL and the intensity distribution output at the multicore fibreoutputs end of the PL. The root mean square error (RMSE) of the reconstructionof petal and LWE modes were just $2.87\times10^{-2}$ rad and$2.07\times10^{-1}$ rad respectively, in the non-linear regime. Thereconstruction RMSE for Zernike combinations ranged from $5.67\times10^{-2}$rad to $8.43\times10^{-1}$ rad, depending on the number of Zernike terms andincident RMS WFE employed. These results demonstrate the promising potential ofPLs as an innovative FP-WFS in conjunction with NNs.

Demonstration of a photonic lantern focal-plane wavefront sensor:  measurement of atmospheric wavefront error modes and low wind effect in the  non-linear regime