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
The interaction of relativistically intense lasers with opaque targets represents a highly non-linear,multi-dimensional parameter space.This limits the utility of sequential 1D scanning of experimental parameters for ...The interaction of relativistically intense lasers with opaque targets represents a highly non-linear,multi-dimensional parameter space.This limits the utility of sequential 1D scanning of experimental parameters for the optimization of secondary radiation,although to-date this has been the accepted methodology due to low data acquisition rates.High repetition-rate(HRR)lasers augmented by machine learning present a valuable opportunity for efficient source optimization.Here,an automated,HRR-compatible system produced high-fidelity parameter scans,revealing the influence of laser intensity on target pre-heating and proton generation.A closed-loop Bayesian optimization of maximum proton energy,through control of the laser wavefront and target position,produced proton beams with equivalent maximum energy to manually optimized laser pulses but using only 60%of the laser energy.This demonstration of automated optimization of laser-driven proton beams is a crucial step towards deeper physical insight and the construction of future radiation sources.展开更多
We present the development and characterization of a high-stability,multi-material,multi-thickness tape-drive target for laser-driven acceleration at repetition rates of up to 100 Hz.The tape surface position was meas...We present the development and characterization of a high-stability,multi-material,multi-thickness tape-drive target for laser-driven acceleration at repetition rates of up to 100 Hz.The tape surface position was measured to be stable on the sub-micrometre scale,compatible with the high-numerical aperture focusing geometries required to achieve relativistic intensity interactions with the pulse energy available in current multi-Hz and near-future higher repetition-rate lasers(>kHz).Long-term drift was characterized at 100 Hz demonstrating suitability for operation over extended periods.The target was continuously operated at up to 5 Hz in a recent experiment for 70,000 shots without intervention by the experimental team,with the exception of tape replacement,producing the largest data-set of relativistically intense laser–solid foil measurements to date.This tape drive provides robust targetry for the generation and study of high-repetitionrate ion beams using next-generation high-power laser systems,also enabling wider applications of laser-driven proton sources.展开更多
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
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.
基金support from the UK STFC grants ST/V001639/1 with the XFEL Physical Sciences Hub and ST/P002021/1the UK EPSRC grants EP/V049577/1 and EP/R006202/1+5 种基金as well as the U.S.DOE Office of Science,Fusion Energy Sciences under FWP No.100182in part by the National Science Foundation under Grant No.1632708 and Award No.PHY–1903414M.J.V.S.acknowledges support from the Royal Society URFR1221874support from the DOE NNSA SSGF program under DE-NA0003960support from the U.S.DOE grant DESC0016804support from the project‘Advanced research using high-intensity laser-produced photons and particles’(CZ.02.1.01/0.0/0.0/16_019/0000789)from the European Regional Development Fund(ADONIS)。
文摘The interaction of relativistically intense lasers with opaque targets represents a highly non-linear,multi-dimensional parameter space.This limits the utility of sequential 1D scanning of experimental parameters for the optimization of secondary radiation,although to-date this has been the accepted methodology due to low data acquisition rates.High repetition-rate(HRR)lasers augmented by machine learning present a valuable opportunity for efficient source optimization.Here,an automated,HRR-compatible system produced high-fidelity parameter scans,revealing the influence of laser intensity on target pre-heating and proton generation.A closed-loop Bayesian optimization of maximum proton energy,through control of the laser wavefront and target position,produced proton beams with equivalent maximum energy to manually optimized laser pulses but using only 60%of the laser energy.This demonstration of automated optimization of laser-driven proton beams is a crucial step towards deeper physical insight and the construction of future radiation sources.
基金Special thanks go to the staff at the Central Laser Facility who provided laser operational support,mechanical and electrical support and computational and administrative support throughout the experiment.We acknowledge funding from UK STFC,Grant Nos.ST/P002021/1 and ST/V001639/1U.S.DOE Office of Science,Fusion Energy Sciences under FWP No.100182+2 种基金in part by the National Science Foundation under Grant No.1632708G.D.G.acknowledges support from the DOE NNSA SSGF program under DE-NA0003960This work has been partially supported by the project Advanced Research Using High-intensity Laser-produced Photons and Particles(CZ.02.1.01/0.0/0.0/16_019/0000789)from the European Regional Development Fund(ADONIS).
文摘We present the development and characterization of a high-stability,multi-material,multi-thickness tape-drive target for laser-driven acceleration at repetition rates of up to 100 Hz.The tape surface position was measured to be stable on the sub-micrometre scale,compatible with the high-numerical aperture focusing geometries required to achieve relativistic intensity interactions with the pulse energy available in current multi-Hz and near-future higher repetition-rate lasers(>kHz).Long-term drift was characterized at 100 Hz demonstrating suitability for operation over extended periods.The target was continuously operated at up to 5 Hz in a recent experiment for 70,000 shots without intervention by the experimental team,with the exception of tape replacement,producing the largest data-set of relativistically intense laser–solid foil measurements to date.This tape drive provides robust targetry for the generation and study of high-repetitionrate ion beams using next-generation high-power laser systems,also enabling wider applications of laser-driven proton sources.
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.
