Wavefront sensing with a brightest pixel selection algorithm

Astronomical adaptive optics systems with open-loop deformable mirror control have recently come on-line. In these systems, the deformable mirror surface is not included in the wavefront sensor paths, and so changes made to the deformable mirror are not fed back to the wavefront sensors. This gives rise to all sorts of linearity and control issues mainly centred on one question: Has the mirror taken the shape requested? Non-linearities in wavefront measurement and in the deformable mirror shape can lead to significant deviations in mirror shape from the requested shape. Here, wavefront sensor measurements made using a brightest pixel selection method are discussed along with the implications that this has for open-loop AO systems. Discussion includes elongated laser guide star spots and also computational efficiency.Comment: 10 pages, 12 figures, accepted by MNRA

On the Formation of Collective Memories: The Role of a Dominant Narrator.

To test our hypothesis that conversations can contribute to the formation of collective memory, we asked participants to study stories and to recall them individually (pregroup recollection), then as a group (group recounting), and then once again individually (postgroup recollection). One way that postgroup collective memories can be formed under these circumstances is if unshared pregroup recollections in the group recounting influences others\u27 postgroup recollections. In the present research, we explored (using tests of recall and recognition) whether the presence of a dominant narrator can facilitate the emergence of unshared pregroup recollections in a group recounting and whether this emergence is associated with changes in postgroup recollections. We argue that the formation of a collective memory through conversation is not inevitable but is limited by cognitive factors, such as conditions for social contagion, and by situational factors, such as the presence of a narrator

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

Photon counting strategies with low light level CCDs

Low light level charge coupled devices (L3CCDs) have recently been developed, incorporating on-chip gain. They may be operated to give an effective readout noise much less than one electron by implementing an on-chip gain process allowing the detection of individual photons. However, the gain mechanism is stochastic and so introduces significant extra noise into the system. In this paper we examine how best to process the output signal from an L3CCD so as to minimize the contribution of stochastic noise, while still maintaining photometric accuracy. We achieve this by optimising a transfer function which translates the digitised output signal levels from the L3CCD into a value approximating the photon input as closely as possible by applying thresholding techniques. We identify several thresholding strategies and quantify their impact on photon counting accuracy and effective signal-to-noise. We find that it is possible to eliminate the noise introduced by the gain process at the lowest light levels. Reduced improvements are achieved as the light level increases up to about twenty photons per pixel and above this there is negligible improvement. Operating L3CCDs at very high speeds will keep the photon flux low, giving the best improvements in signal-to-noise ratio.Comment: 7 pages, accepted by MNRA

On-sky tests of the CuReD and HWR fast wavefront reconstruction algorithms with CANARY

CuReD (Cumulative Reconstructor with domain Decomposition) and HWR (Hierarchical Wavefront Reconstructor) are novel wavefront reconstruction algorithms for the Shack–Hartmann wavefront sensor, used in the single-conjugate adaptive optics. For a high-order system they are much faster than the traditional matrix–vector-multiplication method. We have developed three methods for mapping the reconstructed phase into the deformable mirror actuator commands and have tested both reconstructors with the CANARY instrument. We find out that the CuReD reconstructor runs stably only if the feedback loop is operated as a leaky integrator, whereas HWR runs stably with the conventional integrator control. Using the CANARY telescope simulator we find that the Strehl ratio (SR) obtained with CuReD is slightly higher than that of the traditional least-squares estimator (LSE). We demonstrate that this is because the CuReD algorithm has a smoothing effect on the output wavefront. The SR of HWR is slightly lower than that of LSE. We have tested both reconstructors extensively on-sky. They perform well and CuReD achieves a similar SR as LSE. We compare the CANARY results with those from a computer simulation and find good agreement between the two