Abstract
Machine learning (ML) has the potential for significant impact on the modeling, operation, and control of particle accelerators due to its ability to model nonlinear behavior, interpolate on complicated surfaces, and adapt to system changes over time. Anomaly detection in particular has been highlighted as an area where ML can significantly impact the operation of accelerators. These algorithms work by identifying subtle behaviors of key variables prior to negative events. Efforts to apply ML to anomaly detection have largely focused on subsystems such as RF cavities, superconducting magnets, and losses in rings. However, dedicated efforts to understand how to apply ML for anomaly detection in linear accelerators have been limited. In this paper the use of autoencoders is explored to identify anomalous behavior in measured data from the Fermilab low-energy linear accelerator.
Highlights
In recent years machine learning (ML) has been identified as having the potential for significant impact on the modeling, operation, and control of particle accelerators [1,2]
Once trained output from the latent space of the autoencoder was compared against the toroid current measurements collected during that same period
In this paper autoencoders have been used as a means of anomaly detection and root cause analysis in the Fermilab linac
Summary
In recent years machine learning (ML) has been identified as having the potential for significant impact on the modeling, operation, and control of particle accelerators [1,2]. These techniques are attractive due to their ability to model nonlinear behavior, interpolate on complicated surfaces, and adapt to system changes over time. When combined with optimization algorithms, these inverse models have demonstrated improved switching times between operational configurations in free-electron lasers [7]. Significant speed up has been demonstrated in multi-objective optimization of accelerators by using neural networks as surrogate models [8]
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