Researchers develop clever algorithm to improve our understanding of particlebeams in accelerators physrevlett
via gradient descent. The neural network transformation that minimizes the loss function generates the beam distribution that has the highest likelihood of matching the real initial beam distribution.Comparisons between the synthetic and reconstructed beam probability distributions using our method. – Plots of the mean predicted phase space density projections in 4D transverse phase space.
Second order moments of beam reconstruction during training for each phase space coordinate. Dashed lines denote ground truth values. Vertical lines denote snapshot locations after burn-in period.Reconstruction results from experimental measurements at AWA. Comparison between measured and predicted beam centroids and second-order beam moments on the diagnostic screen as a function of geometric quadrupole focusing strength . Points denote training samples and crosses denote test samples.
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