Suitability of GPUs for real-time control of large astronomical adaptive optics instruments

Adaptive optics (AO) is a technique for correcting aberrations introduced when light propagates through a medium, for example, the light from stars propagating through the turbulent atmosphere. The components of an AO instrument are: (1) a camera to record the aberrations, (2) a corrective mechanism to correct them, (3) a real-time controller (RTC) that processes the camera images and steers the corrective mechanism on milliseconds timescales. We have accelerated the image processing for the AO RTC with the use of graphics processing units (GPUs). It is crucial that the image is processed before the atmospheric turbulence has changed, i.e., in one or two milliseconds. The main task is to transfer the images to the GPU memory with a minimum delay. The key result of this paper is a demonstration that this can be done fast enough using commercial frame grabbers and standard CUDA tools. Our benchmarking image consists of 1.6×1061.6×106 pixels out of which 1.2×1061.2×106 are used in processing. The images are characterized and reduced into a set of 9248 numbers; about one-third of the total processing time is spent on this characterization. This set of numbers is then used to calculate the commands for the corrective system, which takes about two-third of the total time. The processing rate achieved on a single GPU is about 700 frames per second (fps). This increases to 1100 fps (1565 fps) if we use two (four) GPUs. The variation in processing time (jitter) has a root-mean-square value of 20–30 μμ s and about one outlier in a million cycles

Experience with Artificial Neural Networks Applied in Multi-object Adaptive Optics

The use of artificial Intelligence techniques has become widespread in many fields of science, due to their ability to learn from real data and adjust to complex models with ease. These techniques have landed in the field of adaptive optics, and are being used to correct distortions caused by atmospheric turbulence in astronomical images obtained by ground-based telescopes. Advances for multi-object adaptive optics are considered here, focusing particularly on artificial neural networks, which have shown great performance and robustness when compared with other artificial intelligence techniques. The use of artificial neural networks has evolved to the extent of the creation of a reconstruction technique that is capable of estimating the wavefront of light after being deformed by the atmosphere. Based on this idea, different solutions have been proposed in recent years, including the use of new types of artificial neural networks. The results of techniques based on artificial neural networks have led to further applications in the field of adaptive optics, which are included in here, such as the development of new techniques for solar observation or their application in novel types of sensors