LLNL, Livermore, California, USA
The Advanced Radiographic Capability (ARC) currently under development for the National Ignition Facility (NIF) will provide short (1-50 picoseconds) ultra high power (>1 Petawatt) laser pulses used for a variety of diagnostic purposes on NIF ranging from a high energy x-ray pulse source for backlighter imaging to an experimental platform for fast-ignition. A single NIF Quad (4 beams) is being upgraded to support experimentally driven, autonomous operations using either ARC or existing NIF pulses. Using its own seed oscillator, ARC generates short, wide bandwidth pulses that propagate down the existing NIF beamlines for amplification before being redirected through large aperture gratings that perform chirped pulse compression, generating a series of high-intensity pulses within the target chamber. This significant effort to integrate the ARC adds 40% additional control points to the existing NIF Quad and will be deployed in several phases over the coming year. This talk discusses some new unique ARC software controls used for short pulse operation on NIF and integration techniques being used to expedite deployment of this new diagnostic.
LLNL, Livermore, California, USA
To optimize laser performance and minimize operating costs for high energy laser shots it is necessary to locally shadow, or block, flaws from laser light exposure in the beamline optics. Blockers are important for temporarily shadowing a flaw on an optic until the optic can be removed and repaired. To meet this need, a combination of image analysis and machine learning techniques have been developed to accurately define the list of locations where blockers should be applied. The image analysis methods extract and measure evidence of flaw candidates and their correlated downstream hot spots and this information is passed to machine learning algorithms which rank the probability that candidates are flaws that require blocking. Preliminary results indicate this method will increase the percentage of true positives from less than 20% to about 90%, while significantly reducing recall – the total number of candidates brought forward for review.
