An adaptive approach to machine learning for compact particle accelerators

Abstract Machine learning (ML) tools are able to learn relationships between the inputs and outputs of large complex systems directly from data. However, for time-varying systems, the predictive capabilities of ML tools degrade if the systems are no longer accurately represented by the data with whi...

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Autores principales:	Alexander Scheinker, Frederick Cropp, Sergio Paiagua, Daniele Filippetto
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1b5bd259315f456690de4a256e722122
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spelling	oai:doaj.org-article:1b5bd259315f456690de4a256e7221222021-12-02T18:51:07ZAn adaptive approach to machine learning for compact particle accelerators10.1038/s41598-021-98785-02045-2322https://doaj.org/article/1b5bd259315f456690de4a256e7221222021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98785-0https://doaj.org/toc/2045-2322Abstract Machine learning (ML) tools are able to learn relationships between the inputs and outputs of large complex systems directly from data. However, for time-varying systems, the predictive capabilities of ML tools degrade if the systems are no longer accurately represented by the data with which the ML models were trained. For complex systems, re-training is only possible if the changes are slow relative to the rate at which large numbers of new input-output training data can be non-invasively recorded. In this work, we present an approach to deep learning for time-varying systems that does not require re-training, but uses instead an adaptive feedback in the architecture of deep convolutional neural networks (CNN). The feedback is based only on available system output measurements and is applied in the encoded low-dimensional dense layers of the encoder-decoder CNNs. First, we develop an inverse model of a complex accelerator system to map output beam measurements to input beam distributions, while both the accelerator components and the unknown input beam distribution vary rapidly with time. We then demonstrate our method on experimental measurements of the input and output beam distributions of the HiRES ultra-fast electron diffraction (UED) beam line at Lawrence Berkeley National Laboratory, and showcase its ability for automatic tracking of the time varying photocathode quantum efficiency map. Our method can be successfully used to aid both physics and ML-based surrogate online models to provide non-invasive beam diagnostics.Alexander ScheinkerFrederick CroppSergio PaiaguaDaniele FilippettoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Alexander Scheinker Frederick Cropp Sergio Paiagua Daniele Filippetto An adaptive approach to machine learning for compact particle accelerators
description	Abstract Machine learning (ML) tools are able to learn relationships between the inputs and outputs of large complex systems directly from data. However, for time-varying systems, the predictive capabilities of ML tools degrade if the systems are no longer accurately represented by the data with which the ML models were trained. For complex systems, re-training is only possible if the changes are slow relative to the rate at which large numbers of new input-output training data can be non-invasively recorded. In this work, we present an approach to deep learning for time-varying systems that does not require re-training, but uses instead an adaptive feedback in the architecture of deep convolutional neural networks (CNN). The feedback is based only on available system output measurements and is applied in the encoded low-dimensional dense layers of the encoder-decoder CNNs. First, we develop an inverse model of a complex accelerator system to map output beam measurements to input beam distributions, while both the accelerator components and the unknown input beam distribution vary rapidly with time. We then demonstrate our method on experimental measurements of the input and output beam distributions of the HiRES ultra-fast electron diffraction (UED) beam line at Lawrence Berkeley National Laboratory, and showcase its ability for automatic tracking of the time varying photocathode quantum efficiency map. Our method can be successfully used to aid both physics and ML-based surrogate online models to provide non-invasive beam diagnostics.
format	article
author	Alexander Scheinker Frederick Cropp Sergio Paiagua Daniele Filippetto
author_facet	Alexander Scheinker Frederick Cropp Sergio Paiagua Daniele Filippetto
author_sort	Alexander Scheinker
title	An adaptive approach to machine learning for compact particle accelerators
title_short	An adaptive approach to machine learning for compact particle accelerators
title_full	An adaptive approach to machine learning for compact particle accelerators
title_fullStr	An adaptive approach to machine learning for compact particle accelerators
title_full_unstemmed	An adaptive approach to machine learning for compact particle accelerators
title_sort	adaptive approach to machine learning for compact particle accelerators
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/1b5bd259315f456690de4a256e722122
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An adaptive approach to machine learning for compact particle accelerators

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