This study presents a new feed-forward approach based on machine learning, and it has largely solved the problem.” For several years we’ve had trouble with artifacts in the images from our X-ray microscopes. Steve Kevan, ALS director, said, “This is a very important advance for the ALS and ALS-U. ![]() “Whatever the accelerator is, and whatever the conventional solution is, this solution can be on top of that.” “That’s the beauty of this,” said Hiroshi Nishimura, a Berkeley Lab affiliate who retired last year and had engaged in early discussions and explorations of a machine-learning solution to the longstanding light-beam size-stability problem. The successful demonstration at the ALS shows how the technique could also generally be applied to other light sources, and will be especially beneficial for specialized studies enabled by an upgrade of the ALS known as the ALS-U project. The machine-learning algorithm also recommended adjustments to the magnets to optimize the electron beam.īecause the size of the electron beam mirrors the resulting light beam produced by the magnets, the algorithm also optimized the light beam that is used to study material properties at the ALS. The neural network recognized patterns in this data and identified how different device parameters affected the width of the electron beam. In this study, researchers fed electron-beam data from the ALS, which included the positions of the magnetic devices used to produce light from the electron beam, into the neural network. The machine-learning algorithms used at the ALS are referred to as a form of “ neural network” because they are designed to recognize patterns in the data in a way that loosely resembles human brain functions. Machine learning is a form of artificial intelligence in which computer systems analyze a set of data to build predictive programs that solve complex problems. Credit: Lawrence Berkeley National Laboratory When the so-called “feed-forward” correction is implemented, the fluctuations in the vertical beam size are stabilized down to the sub-percent level (see yellow-highlighted section) from levels that otherwise range to several percent. This chart shows how vertical beam-size stability greatly improves when a neural network is implemented during Advanced Light Source operations. The tools are detailed in a study published on November 6, 2019, in the journal Physical Review Letters. Synchrotron designers and operators have wrestled for decades with a variety of approaches to compensate for the most stubborn of these fluctuations.Īnd now, a large team of researchers at Berkeley Lab and UC Berkeley has successfully demonstrated how machine-learning tools can improve the stability of the light beams’ size for experiments via adjustments that largely cancel out these fluctuations - reducing them from a level of a few percent down to 0.4 percent, with submicron (below 1 millionth of a meter) precision. And little tweaks to enhance light-beam properties at these individual beamlines can feed back into the overall light-beam performance across the entire facility. Many of these synchrotron facilities deliver different types of light for dozens of simultaneous experiments. Researchers have found ways to upgrade these machines to produce more intense, focused, and consistent light beams that enable new, and more complex and detailed studies across a broad range of sample types.īut some light-beam properties still exhibit fluctuations in performance that present challenges for certain experiments. ![]() Synchrotrons like the Advanced Light Source at the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) allow scientists to explore samples in a variety of ways using this light, in fields ranging from materials science, biology, and chemistry to physics and environmental science. Synchrotron light sources are powerful facilities that produce light in a variety of “colors,” or wavelengths - from the infrared to X-rays - by accelerating electrons to emit light in controlled beams. Credit: Lawrence Berkeley National Laboratory Successful demonstration of algorithm by Berkeley Lab-University of California Berkeley, team shows technique could be viable for scientific light sources around the globe. Demanding experiments require that the corresponding light-beam size be stable on time scales ranging from less than seconds to hours to ensure reliable data. When stabilized by a machine-learning algorithm, the beam has a horizontal size dimension of 49 microns root mean squared and a vertical size dimension of 48 microns root mean squared. This image shows the profile of an electron beam at Berkeley Lab’s Advanced Light Source synchrotron, represented as pixels measured by a charged coupled device (CCD) sensor.
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