Lecture Details

SLAC Public Lecture Series

Past Lecture

Super-Human Operator: Controlling Accelerators with Machine Learning

Auralee Edelen
Tuesday, October 01, 2019 07:30 pm
Description: 

Particle accelerators are used every day in a wide range of scientific, medical and industrial applications. But did you know that the task of operating these machines is far from mundane? For example, for every experiment at SLAC’s X-ray laser, the Linac Coherent Light Source, human operators regularly adjust several dozen variables to carefully shape the beam, bring it to the correct energy for that particular experiment and maintain stable operation. This is no small feat for a beam that has to travel about a mile and go through plenty of nonlinear “beam gymnastics” along the way! Accelerator researchers are starting to turn to machine learning to see if we can make it easier to create new types of challenging beam setups and to speed up routine tuning. This public lecture will take you on a journey through accelerator tuning and discuss some of the ways accelerator researchers are starting to use machine learning to help out with this challenging task.

About the Speaker:

Auralee Edelen earned her undergraduate degree in physics from Rensselaer Polytechnic Institute with minors in philosophy of science and mathematics, psychology, and science, technology and society. After graduating, she became a research engineer in the Theory, Modeling, and Analysis Branch of the Naval Surface Warfare Center in Bethesda, Maryland, where she worked on the complex problem of reducing the electromagnetic signatures of submarines. In 2012, she brought her interest in complex systems, optimization and machine learning to Colorado State University, where she became interested in applying her skills to particle accelerators.  For her PhD, she used neural networks to model and control the accelerator system at the Fermi National Accelerator Laboratory.  After completing her thesis, Auralee joined SLAC as a research associate in the Accelerator Research Division, where she continues to work with a growing group of coworkers and collaborators on applying machine learning to particle accelerators.

 

Registration is not required. Seating is on a first-come, first served basis.  

We will also be streaming the lecture live onour Facebook page a few minutes before the start time.

Auralee Edelen earned her undergraduate degree in physics from Rensselaer Polytechnic Institute with minors in philosophy of science and mathematics, psychology, and science, technology and society. After graduating, she became a research engineer in the Theory, Modeling, and Analysis Branch of the Naval Surface Warfare Center in Bethesda, Maryland, where she worked on the complex problem of reducing the electromagnetic signatures of submarines. In 2012, she brought her interest in complex systems, optimization and machine learning to Colorado State University, where she became interested in applying her skills to particle accelerators.  For her PhD, she used neural networks to model and control the accelerator system at the Fermi National Accelerator Laboratory.  After completing her thesis, Auralee joined SLAC as a research associate in the Accelerator Research Division, where she continues to work with a growing group of coworkers and collaborators on applying machine learning to particle accelerators.

 

Registration is not required. Seating is on a first-come, first served basis.  

We will also be streaming the lecture live onour Facebook page a few minutes before the start time.