The next generation of high-power lasers enables repetition of experiments at orders of magnitude higher frequency than what was possible using the prior generation.Facilities requiring human intervention between lase...The next generation of high-power lasers enables repetition of experiments at orders of magnitude higher frequency than what was possible using the prior generation.Facilities requiring human intervention between laser repetitions need to adapt in order to keep pace with the new laser technology.A distributed networked control system can enable laboratory-wide automation and feedback control loops.These higher-repetition-rate experiments will create enormous quantities of data.A consistent approach to managing data can increase data accessibility,reduce repetitive data-software development and mitigate poorly organized metadata.An opportunity arises to share knowledge of improvements to control and data infrastructure currently being undertaken.We compare platforms and approaches to state-of-the-art control systems and data management at high-power laser facilities,and we illustrate these topics with case studies from our community.展开更多
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
基金A.J.acknowledges the support from DOE Grant#DESC0016804.
文摘The next generation of high-power lasers enables repetition of experiments at orders of magnitude higher frequency than what was possible using the prior generation.Facilities requiring human intervention between laser repetitions need to adapt in order to keep pace with the new laser technology.A distributed networked control system can enable laboratory-wide automation and feedback control loops.These higher-repetition-rate experiments will create enormous quantities of data.A consistent approach to managing data can increase data accessibility,reduce repetitive data-software development and mitigate poorly organized metadata.An opportunity arises to share knowledge of improvements to control and data infrastructure currently being undertaken.We compare platforms and approaches to state-of-the-art control systems and data management at high-power laser facilities,and we illustrate these topics with case studies from our community.
基金supported by UK STFC ST/V001639/1,UK EPSRC EP/V049577/1 and EP/V044397/1Horizon 2020 funding under European Research Council(ERC)Grant Agreement No.682399+1 种基金support from the Royal Society URF-R1221874support from US DOE grant DESC0016804
文摘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.
