Maintaining optimum performance for beam alignment at the National Ignition Facility

Automatic alignment plays a crucial role in aligning 192 laser beams in the National Ignition Facility to achieve inertial confinement fusion by irradiating a millimeter scale fusion target within a 50-micron alignment accuracy. Cameras placed along the beam path capture the images that are processed by image processing algorithms to measure the position of a beam for subsequent adjustment. Each algorithm is implemented with three main components: an off-normal detector, an algorithm, and an uncertainty quantifier. While the algorithm estimates the position of the beam from the image, the off-normal detector ensures that the beam-feature is legitimate and not spurious noise. Many algorithms operate with externally defined parameters that can be optimized based on the condition or statistics of any specific beamline image. These operating conditions are constantly changing as camera pixels are exposed to neutron radiation during high-yield, inertial confinement fusion experiments. Consequently, periodic off-line tests, online tests, and simulations are performed to predict how pixel degradation or changes of imaging conditions affect alignment accuracy. Hardware aging, camera noise, artifacts from spurious light sources and reflections, and ongoing replacement of critical optics are challenges that must be addressed on a continual basis. The objective of this work is to describe these ongoing efforts that demonstrate the accuracy and robustness of the automatic alignment system. We show examples from laser beam alignment, target-diagnostics alignment, and the second/third harmonic generator angle adjustment. As a result of these maintenance activities, we have identified that the automatic alignment algorithms need to be updated periodically, and specific image processing parameters need to be optimized to match the current imaging conditions of specific beamlines for specific alignment tasks.