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
Next generation high-power laser facilities are expected to generate hundreds-of-MeV proton beams and operate at multiHz repetition rates, presenting opportunities for medical, industrial and scientific applications r...Next generation high-power laser facilities are expected to generate hundreds-of-MeV proton beams and operate at multiHz repetition rates, presenting opportunities for medical, industrial and scientific applications requiring bright pulses of energetic ions. Characterizing the spectro-spatial profile of these ions at high repetition rates in the harsh radiation environments created by laser–plasma interactions remains challenging but is paramount for further source development.To address this, we present a compact scintillating fiber imaging spectrometer based on the tomographic reconstruction of proton energy deposition in a layered fiber array. Modeling indicates that spatial resolution of approximately 1 mm and energy resolution of less than 10% at proton energies of more than 20 MeV are readily achievable with existing 100 μm diameter fibers. Measurements with a prototype beam-profile monitor using 500 μm fibers demonstrate active readouts with invulnerability to electromagnetic pulses, and less than 100 Gy sensitivity. The performance of the full instrument concept is explored with Monte Carlo simulations, accurately reconstructing a proton beam with a multiple-component spectro-spatial profile.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financially supported by STFC,Dstl and EPSRC(grant numbers EP/R006202/1,EP/V049232/1 and EP/P020607/1)by Laserlab-Europe(grant agreement number 871124,European Union’s Horizon 2020 research and innovation program).
文摘Next generation high-power laser facilities are expected to generate hundreds-of-MeV proton beams and operate at multiHz repetition rates, presenting opportunities for medical, industrial and scientific applications requiring bright pulses of energetic ions. Characterizing the spectro-spatial profile of these ions at high repetition rates in the harsh radiation environments created by laser–plasma interactions remains challenging but is paramount for further source development.To address this, we present a compact scintillating fiber imaging spectrometer based on the tomographic reconstruction of proton energy deposition in a layered fiber array. Modeling indicates that spatial resolution of approximately 1 mm and energy resolution of less than 10% at proton energies of more than 20 MeV are readily achievable with existing 100 μm diameter fibers. Measurements with a prototype beam-profile monitor using 500 μm fibers demonstrate active readouts with invulnerability to electromagnetic pulses, and less than 100 Gy sensitivity. The performance of the full instrument concept is explored with Monte Carlo simulations, accurately reconstructing a proton beam with a multiple-component spectro-spatial profile.
