  Capturing high resolution imagery of the Earth's surface often calls for a
telescope of considerable size, even from Low Earth Orbits (LEO). A large
aperture often requires large and expensive platforms. For instance, achieving
a resolution of 1m at visible wavelengths from LEO typically requires an
aperture diameter of at least 30cm. Additionally, ensuring high revisit times
often prompts the use of multiple satellites. In light of these challenges, a
small, segmented, deployable CubeSat telescope was recently proposed creating
the additional need of phasing the telescope's mirrors. Phasing methods on
compact platforms are constrained by the limited volume and power available,
excluding solutions that rely on dedicated hardware or demand substantial
computational resources. Neural Network (NN) are known for their
computationally efficient inference and reduced on board requirements.
Therefore we developed a NN based method to measure co phasing errors inherent
to a deployable telescope. The proposed technique demonstrates its ability to
detect phasing error at the targeted performance level (typically a wavefront
error (WFE) below 15 nm RMS for a visible imager operating at the diffraction
limit) using a point source. The robustness of the NN method is verified in
presence of high order aberrations or noise and the results are compared
against existing state of the art techniques. The developed NN model ensures
its feasibility and provides a realistic pathway towards achieving diffraction
limited images.
