Author Name: Desrina R

Category Name: Science and Technology

Experiments at LCLS run around the clock, in two 12-hour shifts per day. At the start of each shift, operators must tweak the accelerator's performance to prepare the X-ray beam for the next experiment. Sometimes, additional tweaking is needed during a shift as well. In the past, operators have spent hundreds of hours each year on this task, called accelerator tuning.

Now, SLAC researchers have developed a new tool, using machine learning, that may make part of the tuning process five times faster compared to previous methods

Tuning the beam

Producing LCLS's powerful X-ray beam starts with the preparation of a high-quality electron beam. Some of the electrons' energy then gets converted into X-ray light inside special magnets. The properties of the electron beam, which needs to be dense and tightly focused, are a critical factor in how good the X-ray beam will be.

"Even a small difference in the density of the electron beam can have a huge difference in the amount of X-rays you get out at the end," says Daniel Ratner, head of SLAC's machine learning initiative and a member of the team that developed the new technique.

The accelerator uses a series of 24 special magnets, called quadrupole magnets, to focus the electron beam similarly to how glass lenses focus light. Traditionally, human operators carefully turned knobs to adjust individual magnets between shifts to make sure the accelerator was producing the X-ray beam needed for a particular experiment. This process took up a lot of the operators' time - time they could spend on other important tasks that improve the beam for experiments.

A few years ago, LCLS operators adopted a computer algorithm that automated and sped up this magnet tuning. However, it came with its own disadvantages. It aimed at improving the X-ray beam by making random adjustments to the magnets' strengths. But unlike human operators, this algorithm had no prior knowledge of the accelerator's structure and couldn't make educated guesses in its tuning that might have ultimately led to even better results.

A standard EDITORIAL TRACKING SYSTEM is utilized for manuscript submission, review, editorial processing and tracking which can be securely accessed by the authors, reviewers and editors for monitoring and tracking the article processing. Manuscripts can be uploaded online at Editorial Tracking System https://www.imedpub.com/submissions/american-computer-science-information-technology.html) or forwarded to the Editorial Office at computersci@peerjournal.com

Media Contact:

Desrina R
Journal Manager
American journal of computer science and information technology