SynIM: a high-performance GPU-accelerated Python library for synthetic interaction and tomographic reconstruction matrices in next-generation adaptive optics

Abstract

Next-generation Adaptive Optics (AO) systems for 8-40m class telescopes, such as MORFEO (ELT) and MAVIS (VLT), demand high calibration accuracy. Controlling thousands of actuators makes experimental calibration unfeasible due to daytime overheads, environmental disturbances, and opto-mechanical aberrations. Consequently, model-based (synthetic) calibration has become the mandatory standard. We present SynIM, an open-source Python library designed for computing high-fidelity synthetic Interaction, Projection, and Covariance Matrices. SynIM leverages GPU acceleration via CuPy to handle the massive dimensionality of high-order systems. A core innovation is its handling of spatial geometry via composite affine transformations and absolute sub-pixel grid alignment. By merging DM and WFS shifts, rotations, and magnifications into a single operation, SynIM minimizes interpolation artifacts. SynIM introduces an optimized numerical derivative engine for slope computation that mathematically aligns spatial grids at the sub-pixel level, closely mimicking the physical behavior of Shack-Hartmann sensors. It outperforms geometric estimators like the G-tilt telescoping sum at high spatial frequencies, while yielding a substantial computational speed-up. Crucially, end-to-end MCAO simulations demonstrate that reconstructors built with SynIM deliver closed-loop AO performance practically equivalent to full physical optics models. SynIM natively supports SCAO, GLAO, MCAO, and LTAO configurations. It features optimized multi-WFS batch processing, modules for MMSE tomographic reconstructors, and full compatibility with SPRINT for online tracking. Currently driving the design and operational strategies for MORFEO, MAVIS, AOF, KAPA, and WST, SynIM stands as an essential tool for next-generation AO calibration.