The recent advent of deep artificial neural networks has resulted in a dramatic increase in performance for object classification and detection.While pre-trained with everyday objects,we find that a state-of-the-art o...The recent advent of deep artificial neural networks has resulted in a dramatic increase in performance for object classification and detection.While pre-trained with everyday objects,we find that a state-of-the-art object detection architecture can very efficiently be fine-tuned to work on a variety of object detection tasks in a high-power laser laboratory.In this paper,three exemplary applications are presented.We show that the plasma waves in a laser±plasma accelerator can be detected and located on the optical shadowgrams.The plasma wavelength and plasma density are estimated accordingly.Furthermore,we present the detection of all the peaks in an electron energy spectrum of the accelerated electron beam,and the beam charge of each peak is estimated accordingly.Lastly,we demonstrate the detection of optical damage in a high-power laser system.The reliability of the object detector is demonstrated over1000 laser shots in each application.Our study shows that deep object detection networks are suitable to assist online and offline experimental analysis,even with small training sets.We believe that the presented methodology is adaptable yet robust,and we encourage further applications in Hz-level or kHz-level high-power laser facilities regarding the control and diagnostic tools,especially for those involving image data.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
The Centre for Advanced Laser Applications in Garching,Germany,is home to the ATLAS-3000 multi-petawatt laser,dedicated to research on laser particle acceleration and its applications.A control system based on Tango C...The Centre for Advanced Laser Applications in Garching,Germany,is home to the ATLAS-3000 multi-petawatt laser,dedicated to research on laser particle acceleration and its applications.A control system based on Tango Controls is implemented for both the laser and four experimental areas.The device server approach features high modularity,which,in addition to the hardware control,enables a quick extension of the system and allows for automated data acquisition of the laser parameters and experimental data for each laser shot.In this paper we present an overview of our implementation of the control system,as well as our advances in terms of experimental operation,online supervision and data processing.We also give an outlook on advanced experimental supervision and online data evaluation–where the data can be processed in a pipeline–which is being developed on the basis of this infrastructure.展开更多
Laser-plasma physics has developed rapidly over the past few decades as lasers have become both more powerful and more widely available.Early experimental and numerical research in this field was dominated by single-s...Laser-plasma physics has developed rapidly over the past few decades as lasers have become both more powerful and more widely available.Early experimental and numerical research in this field was dominated by single-shot experiments with limited parameter exploration.However,recent technological improvements make it possible to gather data for hundreds or thousands of different settings in both experiments and simulations.This has sparked interest in using advanced techniques from mathematics,statistics and computer science to deal with,and benefit from,big data.At the same time,sophisticated modeling techniques also provide new ways for researchers to deal effectively with situation where still only sparse data are available.This paper aims to present an overview of relevant machine learning methods with focus on applicability to laser-plasma physics and its important sub-fields of laser-plasma acceleration and inertial confinement fusion.展开更多
Technology based on high-peak-power lasers has the potential to provide compact and intense radiation sources for a wide range of innovative applications.In particular,electrons that are accelerated in the wakefield o...Technology based on high-peak-power lasers has the potential to provide compact and intense radiation sources for a wide range of innovative applications.In particular,electrons that are accelerated in the wakefield of an intense laser pulse oscillate around the propagation axis and emit X-rays.This betatron source,which essentially reproduces the principle of a synchrotron at the millimeter scale,provides bright radiation with femtosecond duration and high spatial coherence.However,despite its unique features,the usability of the betatron source has been constrained by its poor control and stability.In this article,we demonstrate the reliable production of X-ray beams with tunable polarization.Using ionization-induced injection in a gas mixture,the orbits of the relativistic electrons emitting the radiation are reproducible and controlled.We observe that both the signal and beam profile fluctuations are significantly reduced and that the beam pointing varies by less than a tenth of the beam divergence.The polarization ratio reaches 80%,and the polarization axis can easily be rotated.We anticipate a broad impact of the source,as its unprecedented performance opens the way for new applications.展开更多
Presented is a novel way to combine snapshot compressive imaging and lateral shearing interferometry in order to capture the spatio-spectral phase of an ultrashort laser pulse in a single shot.A deep unrolling algorit...Presented is a novel way to combine snapshot compressive imaging and lateral shearing interferometry in order to capture the spatio-spectral phase of an ultrashort laser pulse in a single shot.A deep unrolling algorithm is utilized for snapshot compressive imaging reconstruction due to its parameter efficiency and superior speed relative to other methods,potentially allowing for online reconstruction.The algorithm’s regularization term is represented using a neural network with 3D convolutional layers to exploit the spatio-spectral correlations that exist in laser wavefronts.Compressed sensing is not typically applied to modulated signals,but we demonstrate its success here.Furthermore,we train a neural network to predict the wavefronts from a lateral shearing interferogram in terms of Zernike polynomials,which again increases the speed of our technique without sacrificing fidelity.This method is supported with simulation-based results.While applied to the example of lateral shearing interferometry,the methods presented here are generally applicable to a wide range of signals,including Shack-Hartmann-type sensors.The results may be of interest beyond the context of laser wavefront characterization,including within quantitative phase imaging.展开更多
基金support by the operating resources of the Centre for Advanced Laser Applications(CALA)support from the Alexander von Humboldt Stiftung+1 种基金support from the BMBF under contract number 05K19WMBsupport from the German Research Agency,DFG Project No.453619281
文摘The recent advent of deep artificial neural networks has resulted in a dramatic increase in performance for object classification and detection.While pre-trained with everyday objects,we find that a state-of-the-art object detection architecture can very efficiently be fine-tuned to work on a variety of object detection tasks in a high-power laser laboratory.In this paper,three exemplary applications are presented.We show that the plasma waves in a laser±plasma accelerator can be detected and located on the optical shadowgrams.The plasma wavelength and plasma density are estimated accordingly.Furthermore,we present the detection of all the peaks in an electron energy spectrum of the accelerated electron beam,and the beam charge of each peak is estimated accordingly.Lastly,we demonstrate the detection of optical damage in a high-power laser system.The reliability of the object detector is demonstrated over1000 laser shots in each application.Our study shows that deep object detection networks are suitable to assist online and offline experimental analysis,even with small training sets.We believe that the presented methodology is adaptable yet robust,and we encourage further applications in Hz-level or kHz-level high-power laser facilities regarding the control and diagnostic tools,especially for those involving image data.
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Federal Republic of Germany and the Free State of Bavaria for funding the CALA infrastructure(15171 E 0002)and its operation.the Independent Junior Research Group“Characterization and control of high-intensity laser pulses for particle acceleration”,DFG Project No.453619281.+1 种基金N.W.was supported via the IMPULSE project by the European Union Framework Program for Research and Innovation Horizon 2020 under grant agreement No.871161.the Bundesministerium für Bildung und Forschung(BMBF)within project 01IS17048.J.G.acknowledges support from the German Academic scholarship foundation.
文摘The Centre for Advanced Laser Applications in Garching,Germany,is home to the ATLAS-3000 multi-petawatt laser,dedicated to research on laser particle acceleration and its applications.A control system based on Tango Controls is implemented for both the laser and four experimental areas.The device server approach features high modularity,which,in addition to the hardware control,enables a quick extension of the system and allows for automated data acquisition of the laser parameters and experimental data for each laser shot.In this paper we present an overview of our implementation of the control system,as well as our advances in terms of experimental operation,online supervision and data processing.We also give an outlook on advanced experimental supervision and online data evaluation–where the data can be processed in a pipeline–which is being developed on the basis of this infrastructure.
基金The authors acknowledge the use of GPT-3[288](text-davinci-003)in the copy-editing process of this manuscript.
文摘Laser-plasma physics has developed rapidly over the past few decades as lasers have become both more powerful and more widely available.Early experimental and numerical research in this field was dominated by single-shot experiments with limited parameter exploration.However,recent technological improvements make it possible to gather data for hundreds or thousands of different settings in both experiments and simulations.This has sparked interest in using advanced techniques from mathematics,statistics and computer science to deal with,and benefit from,big data.At the same time,sophisticated modeling techniques also provide new ways for researchers to deal effectively with situation where still only sparse data are available.This paper aims to present an overview of relevant machine learning methods with focus on applicability to laser-plasma physics and its important sub-fields of laser-plasma acceleration and inertial confinement fusion.
基金the Agence Nationale pour la Recherche through the FENICS Project No.ANR-12-JS04-0004-01the Agence Nationale pour la Recherche through the FEMTOMAT Project No.ANR-13-BS04-0002+4 种基金the X-Five project(Contract No.339128)the LUCELX project(ANR-13-BS04-0011)the EuCARD2/ANAC2 EC FP7 project(Contract No.312453)the GARC project 15-03118Ssupport from the European Union’s Horizon 2020 research and innovation program under Grant Agreement No.654148 Laserlab-Europe.
文摘Technology based on high-peak-power lasers has the potential to provide compact and intense radiation sources for a wide range of innovative applications.In particular,electrons that are accelerated in the wakefield of an intense laser pulse oscillate around the propagation axis and emit X-rays.This betatron source,which essentially reproduces the principle of a synchrotron at the millimeter scale,provides bright radiation with femtosecond duration and high spatial coherence.However,despite its unique features,the usability of the betatron source has been constrained by its poor control and stability.In this article,we demonstrate the reliable production of X-ray beams with tunable polarization.Using ionization-induced injection in a gas mixture,the orbits of the relativistic electrons emitting the radiation are reproducible and controlled.We observe that both the signal and beam profile fluctuations are significantly reduced and that the beam pointing varies by less than a tenth of the beam divergence.The polarization ratio reaches 80%,and the polarization axis can easily be rotated.We anticipate a broad impact of the source,as its unprecedented performance opens the way for new applications.
基金supported by the Independent Junior Research Group‘Characterization and control of high-intensity laser pulses for particle acceleration’,DFG Project No.453619281We would also like to acknowledge UKRI-STFC grant ST/V001655/1.
文摘Presented is a novel way to combine snapshot compressive imaging and lateral shearing interferometry in order to capture the spatio-spectral phase of an ultrashort laser pulse in a single shot.A deep unrolling algorithm is utilized for snapshot compressive imaging reconstruction due to its parameter efficiency and superior speed relative to other methods,potentially allowing for online reconstruction.The algorithm’s regularization term is represented using a neural network with 3D convolutional layers to exploit the spatio-spectral correlations that exist in laser wavefronts.Compressed sensing is not typically applied to modulated signals,but we demonstrate its success here.Furthermore,we train a neural network to predict the wavefronts from a lateral shearing interferogram in terms of Zernike polynomials,which again increases the speed of our technique without sacrificing fidelity.This method is supported with simulation-based results.While applied to the example of lateral shearing interferometry,the methods presented here are generally applicable to a wide range of signals,including Shack-Hartmann-type sensors.The results may be of interest beyond the context of laser wavefront characterization,including within quantitative phase imaging.