Applying Machine Learning Methods to Laser Acceleration of Protons: Lessons Learned from Synthetic Data
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Date
2023-05
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Publisher
The Ohio State University
Abstract
Neural networks and other machine learning algorithms are beginning to be used in ultra-intense laser
physics. Compared to other machine learning applications, ultra-intense laser systems are typically data poor because of limitations on the number of shots per second. An important concern is that the machine learning method being used remains accurate even when trained on a relatively small number of data points. By using synthetic data based on a model proposed by Fuchs et al. 2006, we seek to explore the speed and accuracy of three different machine learning methods including a neural network with two hidden layers. The results of this project can potentially be applied to training neural networks on real experimental data sets.
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Keywords
Machine Learning, Neural Network, Laser Physics, Gaussian Process, Support Vector Machines