Deep Learning-Based Coarse Alignment and Cophasing of a Distributed-Aperture Telescope. Application to the Small ExoLife Finder (SELF)
The ExoLife Finder (ELF), a 35--50-meter-class Fizeau interferometric telescope, was designed to detect biosignatures on exoplanets through direct imaging. The Small ExoLife Finder (SELF), a 3.5-meter prototype, serves as a testbed for the innovative technologies required for such a facility. A key challenge is the precise cophasing of its distributed aperture, where the optical path differences between subapertures must remain below a small fraction of the operating wavelength. This work investigates convolutional neural networks (CNNs) to estimate alignment errors directly from focal-plane images, enabling fast, data-driven coarse alignment to the few-micrometer level. A supervised regression framework was developed using a custom CNN to model the nonlinear mapping between subaperture misalignments and focal-plane intensity distributions. Training and validation datasets were generated from high-fidelity optical simulations of the simplified SELF system. Gaussian-noise augmentation was used to assess robustness, and several established CNN architectures were evaluated for comparison. The proposed approach reconstructed piston and tilt misalignments across all mirror pairs, demonstrating that focal-plane interference patterns can be exploited for alignment estimation. The custom CNN provided a favorable balance between estimation performance, robustness, and computational efficiency, with millisecond-scale inference times. A trade-off between performance and noise robustness was identified, with noise augmentation improving reconstruction at low signal-to-noise ratios but reduced performance under near noise-free conditions. These results demonstrate the feasibility of data-driven focal-plane wavefront sensing for distributed-aperture telescopes and provide a basis for autonomous, computationally efficient cophasing strategies for future high-resolution interferometric systems.