Proton radiography has proved increasingly successful as a diagnostic for electric and magnetic fields in high-energy-density physics experiments.Most experiments use target-normal sheath acceleration sources with a w...Proton radiography has proved increasingly successful as a diagnostic for electric and magnetic fields in high-energy-density physics experiments.Most experiments use target-normal sheath acceleration sources with a wide energy range in the proton beam,since the velocity spread can help differentiate between electric and magnetic fields and provide time histories in a single shot.However,in magnetized plasma experiments with strong background fields,the broadband proton spectrum leads to velocity-spread-dependent displacement of the beam and significant blurring of the radiograph.We describe the origins of this blurring and show how it can be removed from experimental measurements,and we outline the conditions under which such deconvolutions are successful.As an example,we apply this method to a magnetized plasma experiment that used a background magnetic field of 3 T and in which the strong displacement and energy spread of the proton beam reduced the spatial resolution from tens of micrometers to a few millimeters.Application of the deconvolution procedure accurately recovers radiographs with resolutions better than 100μm,enabling the recovery of more accurate estimates of the path-integrated magnetic field.This work extends accurate proton radiography to a class of experiments with significant background magnetic fields,particularly those experiments with an applied external magnetic field.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the support of LLNL Academic Partnerships(Grant No.B618488)EUROfusion Enabling Research Grant Nos.AWP17-ENR-IFE-CCFE-01 and AWP17-ENR-IFECEA-02,and UK EPSRC Grant Nos.EP/P026796/1 and EP/R029148/1.
文摘Proton radiography has proved increasingly successful as a diagnostic for electric and magnetic fields in high-energy-density physics experiments.Most experiments use target-normal sheath acceleration sources with a wide energy range in the proton beam,since the velocity spread can help differentiate between electric and magnetic fields and provide time histories in a single shot.However,in magnetized plasma experiments with strong background fields,the broadband proton spectrum leads to velocity-spread-dependent displacement of the beam and significant blurring of the radiograph.We describe the origins of this blurring and show how it can be removed from experimental measurements,and we outline the conditions under which such deconvolutions are successful.As an example,we apply this method to a magnetized plasma experiment that used a background magnetic field of 3 T and in which the strong displacement and energy spread of the proton beam reduced the spatial resolution from tens of micrometers to a few millimeters.Application of the deconvolution procedure accurately recovers radiographs with resolutions better than 100μm,enabling the recovery of more accurate estimates of the path-integrated magnetic field.This work extends accurate proton radiography to a class of experiments with significant background magnetic fields,particularly those experiments with an applied external magnetic field.
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.
