X-ray absorption spectroscopy is a well-accepted diagnostic for experimental studies of warm dense matter.It requires a short-lived X-ray source of sufficiently high emissivity and without characteristic lines in the ...X-ray absorption spectroscopy is a well-accepted diagnostic for experimental studies of warm dense matter.It requires a short-lived X-ray source of sufficiently high emissivity and without characteristic lines in the spectral range of interest.In the present work,we discuss how to choose an optimum material and thickness to get a bright source in the wavelength range 2A–6A(∼2 keV to 6 keV)by considering relatively low-Z elements.We demonstrate that the highest emissivity of solid aluminum and silicon foil targets irradiated with a 1-ps high-contrast sub-kJ laser pulse is achieved when the target thickness is close to 10μm.An outer plastic layer can increase the emissivity even further.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 study was supported financially by the Russian Foundation for Basic Research(Grant No.20-02-00790)the Joint Institute for High Temperatures of the Russian Academy of Sciences(Topic Grant No.01201357846)The UK team received financial support from the Engineering and Physical Sciences Research Council(Grant Nos.EP/L01663X/1 and EP/H012605/1).
文摘X-ray absorption spectroscopy is a well-accepted diagnostic for experimental studies of warm dense matter.It requires a short-lived X-ray source of sufficiently high emissivity and without characteristic lines in the spectral range of interest.In the present work,we discuss how to choose an optimum material and thickness to get a bright source in the wavelength range 2A–6A(∼2 keV to 6 keV)by considering relatively low-Z elements.We demonstrate that the highest emissivity of solid aluminum and silicon foil targets irradiated with a 1-ps high-contrast sub-kJ laser pulse is achieved when the target thickness is close to 10μm.An outer plastic layer can increase the emissivity even further.
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.
