Upcoming Seminar
Past Seminars
Date: Aug 17, 2016
Speakers: Anna Leskova, Hananiel Setiawan, Tanner M. Worden, Juhao Wu
Title: Machine Learning and Optimization to Enhance the FEL Brightness
Abstract: Recent studies on enhancing the FEL brightness via machine learning and optimization will be reported. The topics are tapered FEL and improved SASE. The existing popular machine learning approaches will be reviewed and selected based on the characteristics of different tasks. Numerical simulation and preliminary LCLS experiment results will be presented.
Date: July 6, 2016
Speaker: Mitch McIntire
Location: Truckee Room, B52-206 T
Title: Automated tuning at LCLS using Bayesian optimization
Abstract: The LCLS free-electron laser has historically been tuned by hand by the machine operators. Existing tuning procedures account for hundreds of hours of machine time per year, and so efforts are underway to reduce this tuning time via automation. We introduce an approach for automated tuning using Bayesian optimization with statistical models called Gaussian processes. Initial testing has shown that this method can substantially reduce tuning time and is potentially a significant improvement on existing automated tuning methods. In this talk I'll describe Bayesian optimization and Gaussian processes and share some details and insights of implementation, as well as our preliminary results.
Date: June 15, 2016
Speaker: David Schneider
Title: Using Deep Learning to Sort Down Data
Abstract:
We worked on data from a two color experiment (each pulse has two bunches at different energy levels). The sample reacts differently depending on which of the colors lased and the energy in the lasing. We used deep learning to train a convolutional neural network to predict these lasing and energy levels from the xtcav diagnostic images. We then sorted down the data taken of the sample based on these values and identified differences in how the sample reacted. Scientific results from the experiment will start with an analysis of these differences. We used guided back propagation to see what the neural network identified as important and were able to obtain images that isolate the lasing portions of the xtcav images.
slides: 
xtcav_mlearn.pdf