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| A representation of a particle beam traveling through an accelerator. Illustration Credit: Greg Stewart/SLAC National Laboratory |
The algorithm pairs machine-learning techniques with beam physics equations to avoid massive data crunching.
Whenever SLAC National Accelerator Laboratory’s linear accelerator is on, packs of around a billion electrons each travel together at nearly the speed of light through metal piping. These electron bunches form the accelerator’s particle beam, which is used to study the atomic behavior of molecules, novel materials and many other subjects. But trying to estimate what a particle beam actually looks like as it travels through an accelerator is difficult, often leaving scientists with only a rough approximation of how a beam will behave during an experiment.
Now, researchers at the Department of Energy’s SLAC National Accelerator Laboratory, the DOE’s Argonne National Laboratory and the University of Chicago have developed an algorithm that more precisely predicts a beam’s distribution of particle positions and velocities as it zips through an accelerator. This detailed beam information will help scientists perform their experiments more reliably – a need that is becoming increasingly important as accelerator facilities operate at higher and higher energies and generate more complex beam profiles. The researchers detailed their algorithm and method in April in Physical Review Letters.
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