Adaptive optics (AO) is a technology that corrects in real time for the blurring e †ects of atmospheric turbulence, in principle allowing Earth-bound telescopes to achieve their di †raction limit and to "" see ÏÏ as clearly as if they were in space. The power of AO using natural guide stars has been amply demonstrated in recent years on telescopes up to 3È4 m in diameter. The next breakthrough in astronomical resolution was expected to occur with the implementation of AO on the new generation of large, 8È10 m diameter telescopes. In this paper we report the initial results from the Ðrst of these AO systems, now coming on line on the 10 m diameter Keck II Telescope. The results include the highest angular resolution images ever obtained from a single telescope and at 0.85 and 1.65 km wavelengths, respectively), as well (0A .022 0A .040 as tests of system performance on three astronomical targets.

Wave-front reconstruction with the use of the fast Fourier transform (FFT) and spatial filtering is shown to be computationally tractable and sufficiently accurate for use in large Shack-Hartmann-based adaptive optics systems (up to at least 10,000 actuators). This method is significantly faster than, and can have noise propagation comparable with that of, traditional vector-matrix-multiply reconstructors. The boundary problem that prevented the accurate reconstruction of phase in circular apertures by means of square-grid Fourier transforms (FTs) is identified and solved. The methods are adapted for use on the Fried geometry. Detailed performance analysis of mean squared error and noise propagation for FT methods is presented with the use of both theory and simulation.

The development of high brightness and short pulse width (< 200 picoseconds) x-ray lasers now offers biologists the possibility of high-resolution imaging of specimens in an aqueous environment without the blurring effects associated with natural motions and chemical erosion. As a step toward developing the capabilities of this type of x-ray microscopy, a tantalum x-ray laser at 44.83 angstrom wavelength was used together with an x-ray zone plate lens to image both unlabeled and selectively gold-labeled dried rat sperm nuclei. The observed images show approximately 500 angstrom features, illustrate the importance of x-ray microscopy in determining chemical composition, and provide information about the uniformity of sperm chromatin organization and the extent of sperm chromatin hydration.

Machine learning is finding increasingly broad application in the physical sciences.This most often involves building a model relationship between a dependent, measurable output and an associated set of controllable, but complicated, independent inputs. We present a tutorial on current techniques in machine learning ? a jumpingoff point for interested researchers to advance their work. We focus on deep neural networks with an emphasis on demystifying deep learning. We begin with background ideas in machine learning and some example applications from current research in plasma physics. We discuss supervised learning techniques for modeling complicated functions, beginning with familiar regression schemes, then advancing to more sophisticated deep learning methods. We also address unsupervised learning and techniques for reducing the dimensionality of input spaces. Along the way, we describe methods for practitioners to help ensure that their models generalize from their training data to as-yet-unseen test data. We describe classes of tasks -predicting scalars, handling images, fitting time-series -and prepare the reader to choose an appropriate technique. We finally point out some limitations to modern machine learning and speculate on some ways that practitioners from the physical sciences may be particularly suited to help. a) Electronic mail: spears9@llnl.gov 1 arXiv:1712.08523v1 [physics.comp-ph]

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