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Laser wakefield accelerator modelling with variational neural networks 被引量:4
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作者 M.J.V.Streeter C.Colgan +23 位作者 C.C.Cobo C.Arran E.E.Los r.watt N.Bourgeois L.Calvin J.Carderelli N.Cavanagh S.J.D.Dann R.Fitzgarrald E.Gerstmayr A.S.Joglekar B.Kettle P.Mckenna C.D.Murphy Z.Najmudin P.Parsons Q.Qian P.P.Rajeev C.P.Ridgers D.R.Symes A.G.R.Thomas G.Sarri S.P.D.Mangles 《High Power Laser Science and Engineering》 SCIE CAS CSCD 2023年第1期67-74,共8页
A machine learning model was created to predict the electron spectrum generated by a GeV-class laser wakefield accelerator.The model was constructed from variational convolutional neural networks,which mapped the resu... A machine learning model was created to predict the electron spectrum generated by a GeV-class laser wakefield accelerator.The model was constructed from variational convolutional neural networks,which mapped the results of secondary laser and plasma diagnostics to the generated electron spectrum.An ensemble of trained networks was used to predict the electron spectrum and to provide an estimation of the uncertainty of that prediction.It is anticipated that this approach will be useful for inferring the electron spectrum prior to undergoing any process that can alter or destroy the beam.In addition,the model provides insight into the scaling of electron beam properties due to stochastic fluctuations in the laser energy and plasma electron density. 展开更多
关键词 laser plasma interactions particle acceleration neural networks machine learning
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