Daniel Ratner, head of SLAC’s machine learning initiative, explains the lab’s unique opportunities to advance scientific discovery through machine learning.
DOE/SLAC National Accelerator Laboratory
Machine learning is ubiquitous in science and technology these days. It outperforms traditional computational methods in many areas, for instance by vastly speeding up tedious processes and handling huge batches of data. At the Department of Energy’s SLAC National Accelerator Laboratory, machine learning is already opening new avenues to advance the lab’s unique scientific facilities and research.
For example, SLAC scientists have already used machine learning techniques to operate accelerators more efficiently, to speed up the discovery of new materials, and to uncover distortions in space-time caused by astronomical objects up to 10 million times faster than traditional methods.
The term “machine learning” broadly refers to techniques that let computers “learn by example” by inferring their own conclusions from large sets of