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Autoencoder Based Analysis of RF Parameters in the Fermilab Low Energy Linac

Jonathan P. Edelen, Christopher C. Hall
2021 Information  
In this paper the use of autoencoders is explored to identify anomalous behavior in measured data from the Fermilab low-energy linear accelerator.  ...  Anomaly detection in particular has been highlighted as an area where ML can significantly impact the operation of accelerators.  ...  In this work autoencoders are used to better correlate changes in the RF cavities to the changes in the transmission at the low energy proton linac at Fermi National Laboratory (Fermilab).  ... 
doi:10.3390/info12060238 fatcat:z7lbu4kz3jcmbkx3e4d6z5jbju

Toward the End-to-End Optimization of Particle Physics Instruments with Differentiable Programming: a White Paper [article]

Tommaso Dorigo, Andrea Giammanco, Pietro Vischia, Max Aehle, Mateusz Bawaj, Alexey Boldyrev, Pablo de Castro Manzano, Denis Derkach, Julien Donini, Auralee Edelen, Federica Fanzago, Nicolas R. Gauger (+24 others)
2022 arXiv   pre-print
pipeline and the use of deep learning techniques may allow the simultaneous optimization of all design parameters.  ...  On the other hand, massive potential gains in performance over standard, "experience-driven" layouts are in principle within our reach if an objective function fully aligned with the final goals of the  ...  Acknowledgements We wish to thank all the participants of the first MODE workshop on differentiable programming that took place in Louvain-la-Neuve (Belgium) from 6 to 8 September 2021, for the fruitful  ... 
arXiv:2203.13818v1 fatcat:omwufbmervgjdmksu5scwld52q