Adaptive Optics Lab Upgrades Enable High Contrast Imaging with Four New Wavefront Sensors

Adaptive optics systems correct for atmospheric distortions to deliver exceptionally clear astronomical images, and a newly upgraded testbed at the University of California, Santa Cruz promises to push the boundaries of this technology. Rebecca Jensen-Clem, Vincent Chambouleyron, and Prince Javier, along with their colleagues, have rebuilt the Santa Cruz Extreme Adaptive Optics Lab (SEAL) to operate across both near-infrared and visible wavelengths, significantly expanding its capabilities. This reflective rebuild incorporates a suite of advanced wavefront sensors, including innovative designs like a lantern sensor, and integrates multiple real-time control systems, such as Catkit and CACAO. By combining these improvements with cutting-edge coronagraph technology, the team aims to develop and test the next generation of instruments for large telescopes, ultimately enabling the detection of faint signals from exoplanets and furthering our understanding of the universe.

Directly imaging planets around other stars requires suppressing starlight by enormous factors, presenting a significant technical challenge. Consequently, research focuses on developing and testing advanced wavefront control techniques, including coronagraphy and shaped pupil wavefront control, to enhance image contrast and enable the characterisation of exoplanetary systems. The lab provides a controlled environment for evaluating these technologies, bridging the gap between simulations and observations, and ultimately paving the way for future exoplanet exploration missions.

Real-Time Wavefront Control and Standardization

Research at SEAL encompasses a broad range of adaptive optics technologies and techniques. Core to this work is the development of real-time control software, such as CACAO, a comprehensive package for controlling adaptive optics systems. Scientists are also focusing on standardization efforts to ensure compatibility and data exchange across multiple telescopes and testbeds. A significant area of investigation is wavefront sensing, with researchers exploring various methods including multi-wavefront sensors and photonic lanterns to improve performance. These photonic lanterns, which reconstruct wavefronts from multiple sources, are being tested on real telescopes to validate their effectiveness.

Predictive control, a technique for improving adaptive optics performance by anticipating atmospheric changes, is also under investigation. Researchers are comparing data-driven and model-driven prediction methods to optimise performance. The lab also explores advanced system architectures and integration strategies, such as AstroPIC, a photonic integrated circuit coronagraph architecture. Coronagraphy, the technique of suppressing starlight to reveal faint companions, is a central focus, with researchers developing phase-apodized pupil Lyot coronagraphs and integrated photonic coronagraphs for future space telescopes. Machine learning is emerging as a powerful tool for enhancing adaptive optics, with researchers reviewing and applying machine learning methods for wavefront control. Scientists rebuilt the system with custom parabolic mirrors, enabling operation across both visible and near-infrared wavelengths, and integrated a suite of advanced wavefront sensors including Shack-Hartmann, pyramid, vector-Zernike, and lantern technologies. These enhancements allow for comprehensive testing of adaptive optics components and algorithms designed to correct for atmospheric distortions. The upgraded SEAL achieves remarkable wavefront control, demonstrated by residual error measurements of approximately 18 nanometers root mean square.

Closed-loop correction, initially performed with a larger-stroke deformable mirror and then refined with a second mirror, effectively flattens the wavefront. Experiments reveal that the system’s performance is limited by the “quilting effect” of one of the deformable mirrors and minor tilts in the segmented mirror, but these effects are well-characterized and understood. SEAL now supports two distinct visible science paths, direct PSF imaging and coronagraphic imaging using a vector vortex coronagraph, and a dedicated infrared imaging channel. In the infrared branch, scientists achieved a Strehl ratio of 98% after closing the loop with a visible wavefront sensor, corresponding to an estimated non-common path aberration (NCPA) of only 30 nanometers root mean square.

This high level of correction demonstrates the system’s ability to accurately compensate for aberrations even when switching between visible and infrared light. To facilitate flexible control and data acquisition, the team implemented Catkit2, a service-oriented software framework originally developed for the HiCAT testbed. This architecture distributes control across three computers, enabling concurrent operation of hardware components and reducing potential bottlenecks. These improvements include a rebuild with custom optics enabling both near-infrared and visible wavelength operation, and the integration of a suite of advanced wavefront sensors, such as Shack-Hartmann, pyramid, vector-Zernike, and lantern sensors. Furthermore, the lab now incorporates multiple real-time control software packages, expanding its capabilities for adaptive optics research. These advancements have facilitated substantial progress in wavefront sensing and control techniques. Researchers have successfully tested and refined methods like vector-Zernike wavefront sensing, pyramid wavefront sensing, and chopper-based techniques, achieving improved performance in dynamic range, linearity, and capture range. Notably, the lab’s implementation of the Fast Atmospheric Self-coherent camera Technique (FAST) demonstrated significant contrast improvements and enabled simultaneous multi-stage adaptive optics control at high speeds, paving the way for an upcoming on-sky deployment at the Shane Telescope.