The National Aeronautics and Space Administration’s (NASA) first dedicated exoplanetary spectroscopy mission, the Colorado Ultraviolet Transit Experiment (CUTE), is used to search for signatures of atmospheric escape, the process by which constituent gases depart a planetary atmosphere. Through transit spectroscopy, the signs of escape driven by the high level of ultraviolet (UV) radiation from their parent stars are detectable around close-in planets. CUTE is a 6U CubeSat developed and operated by the Laboratory for Atmospheric and Space Physics (LASP) of the University of Colorado in Boulder, Colorado, United States; it looks for these signs of escape by surveying close-in extrasolar planets in the near-UV (2479 to 3306 Å) with 208×84 mm Cassegrain telescope-fed, UV-enhanced charged coupled device. Funded through a NASA ROSES proposal in 2017 and forced to deal with a worldwide pandemic during the heart of its fabrication and test program, CUTE has demonstrated the capability of small satellites to launch on schedule and perform challenging astronomical measurements. We will highlight the CUTE mission’s science objectives, implementation, and tribulations on its road to delivering a successful science program while discussing lessons learned pertaining to the development of CubeSat programs and the application of those lessons for a CUTE-style follow-on mission in the future.

Electroforming replication technology at the Marshall Space Flight Center has a long heritage of producing high-quality, full-shell X-ray mirrors for various applications. Nickel alloys are electroformed onto a super-polished mandrel in the electroforming process and then separated to form the replicated full-shell optic. Various parameters in the electroplating configuration could result in the non-uniformity of the shell’s thickness. Thickness non-uniformities primarily occur due to the non-uniform electric field distribution in the electroforming tank during deposition. Using COMSOL Multiphysics simulations, we studied the electric field distributions during the deposition process. Using these studies, we optimized the electric field distribution and strength inside the tank using customized shields and insulating gaskets on the mandrel. These efforts reduced the thickness non-uniformity from over 20% to under 5%. Improving the thickness uniformity of the shell aids in better mounting and aligning shells in the optics module. Optimization of the electroforming process, in some cases, improved the optical performance of the shells. Using finite element modeling, we estimated the effect of electroforming stress on the figure errors of the replicated optics. We observed that the electroforming stress predominantly affects the figure toward the ends of the optics. We presented COMSOL optimization of the electroforming process and the experimental results validating these simulations. We also discuss modeling experimental results of the replication figure errors due to electroforming stresses.

Time delay error is a significant error source in adaptive optics (AO) systems. It arises from the latency between sensing the wavefront and applying the correction. Predictive control algorithms reduce the time delay error, providing significant performance gains, especially for high-contrast imaging. However, the predictive controller’s performance depends on factors such as the wavefront sensor (WFS) type, the measurement noise level, the AO system’s geometry, and the atmospheric conditions. We study the limits of prediction under different imaging conditions through spatiotemporal Gaussian process models. The method provides a predictive reconstructor that is optimal in the least-squares sense, conditioned on the fixed times series of WFS data and our knowledge of the atmospheric conditions. We demonstrate that knowledge is power in predictive AO control. With a Shack–Hartmann sensor-based extreme AO instrument, perfect knowledge of the wind and atmospheric profile and exact frozen flow evolution lead to a reduction of the residual wavefront phase variance up to a factor of 3.5 compared with a non-predictive approach. If there is uncertainty in the profile or evolution models, the gain is more modest. Still, assuming that only effective wind speed is available (without direction) led to reductions in variance by a factor of ∼2.3. We also study the value of data for predictive filters by computing the experimental utility for different scenarios to answer questions such as how many past telemetry frames should the prediction filter consider and whether is it always most advantageous to use the most recent data. We show that within the scenarios considered, more data provide a consistent increase in prediction accuracy. Furthermore, we demonstrate that given a computational limitation on how many past frames, we can use an optimized selection of n past frames, which leads to a 10% to 15% additional improvement in root mean square over using the n latest consecutive frames of data.