...
| Table of Contents |
|---|
| maxLevel | 2 |
|---|
| exclude | 2 Comments |
|---|
|
Upcoming Seminars
...
Machine Learning-based Beam Size Stabilization at ALS
Date: August 7, 2020 1:00pm (remote)
Speaker: Simon Leeman (LBNL)
In state-of-the-art synchrotron light sources the overall source stability is limited by the achievable level of electron beam size stability. This source size stability is presently on the few- percent level, which is still 1-2 orders of magnitude larger than already demonstrated stability of source position/angle (slow/fast orbit feedbacks) and current (top-off injection). Until now, source size stabilization has been achieved through corrections based on a combination of static predetermined physics models and lengthy calibration measurements (feed-forward tables), periodically repeated to counteract drift in the accelerator and instrumentation. We now demonstrate for the first time [PRL 123 194801 (2019)], how application of machine learning allows for a physics- and model-independent stabilization of source size relying only on previously existing instrumentation in ALS. Such feed-forward correction based on neural networks that can be continuously online-retrained achieves source size stability as low as 0.2 microns rms (0.4%) which results in overall source stability approaching the sub-percent noise floor of the most sensitive experiments.
High-dimensional geometry and the landscapes of deep neural networks
Date: July 31, 2020 1:00pm (remote)
...
