Data-driven science and machine learning methods in laser–plasma physics

Döpp, A.; Eberle, Christoph; Howard, Sunny; Irshad, Faran; Lin, Jingquan; Streeter, Matthew

doi:10.1017/hpl.2023.47

Cited by 45 publications

(3 citation statements)

References 270 publications

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“…One key aspect is in better reproducibility and control of important properties such as beam charge, energy, and pointing stability [18][19][20] or improving the beam quality (e.g. the beam emittance or energy spread) [21][22][23]; often by employing machine learning techniques and automation [24][25][26]. These improvements are required in view, e.g.…”

Section: Introductionmentioning

confidence: 99%

Laser polarization control of ionization-injected electron beams and x-ray radiation in laser wakefield accelerators

Mukherjee,

Seipt

2024

Plasma Phys. Control. Fusion

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In this paper we have studied the influence of the laser polarization on the dynamics of the ionization-injected electron beams and subsequently the properties of the emitted betatron radiation in laser wakefield accelerators (LWFAs). While ionizing by a strong field laser radiation, generated photo-electrons carry a residual transverse momentum in excess of the ionization potential via the above threshold ionization process. This above threshold ionization (ATI) momentum explicitly depends on the polarization state of the ionizing laser and eventually governs the dynamics of the electron beam trapped inside the wake potential. In order to systematically investigate the effect of the laser polarization, here, we have employed complete three-dimensional (3-D) Particle-in-Cell (PIC) simulations in the nonlinear bubble regime of the LWFAs. We focus, in particular, on the effects the laser polarization has on the ionization injection mechanism, and how these features affect the final beam properties, such as beam charge, energy, energy spread, and transverse emittance. We have also found that as the laser polarization gradually changes from linear to circular, the helicityof the electron trajectory, and hence the angular momentum carried by the beam increases significantly. Studies have been further extended to reveal the effect of laser polarization on the radiation emitted by the accelerated electrons. The far-field radiation spectra have been calculated for the linear (LP) and circular polarization (CP) states of the laser. It has been shown that the spatial distributions and the polarization properties (Stokes parameters) of the emitted radiation in the above two cases are substantially different. Therefore, our study provides a facile and efficient alternative to regulate the properties of the accelerated electron beams and x-ray radiation in LWFAs, utilizing ionization injection mechanism.

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Section: Introductionmentioning

confidence: 99%

Laser polarization control of ionization-injected electron beams and x-ray radiation in laser wakefield accelerators

Mukherjee,

Seipt

2024

Plasma Phys. Control. Fusion

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“…The power of optimal techniques and strength of neural learning are utilized in artificial intelligence computing approaches to address a variety of challenging real-world scenarios [25,26]. Numerous divisions of applied sciences, including space science [27], plasma science [28], quantum mechanics [29], thermal physics [30], hydrodynamic [31], electrical motors and generators [32,33], electromagnetism [34], weather science [35], optical science [36], algebraic expressions [37], neuroinformatics [38], and microengineering [39], are finding extensive use for neural intelligence computing frameworks. These soft computing approaches outperformed all conventional methodologies and performed remarkably well in the aforementioned applications [40].…”

Section: Introductionmentioning

confidence: 99%

Scaled Conjugate Gradient Neural Intelligence for Motion Parameters Prediction of Markov Chain Underwater Maneuvering Target

Ali,

Zuberi,

Qing

et al. 2024

JMSE

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This study proposes a novel application of neural computing based on deep learning for the real-time prediction of motion parameters for underwater maneuvering object. The intelligent strategy utilizes the capabilities of Scaled Conjugate Gradient Neural Intelligence (SCGNI) to estimate the dynamics of underwater target that adhere to discrete-time Markov chain. Following a state-space methodology in which target dynamics are combined with noisy passive bearings, nonlinear probabilistic computational algorithms are frequently used for motion parameters prediction applications in underwater acoustics. The precision and robustness of SCGNI are examined here for effective motion parameter prediction of a highly dynamic Markov chain underwater passive vehicle. For investigating the effectiveness of the soft computing strategy, a steady supervised maneuvering route of undersea passive object is designed. In the framework of bearings-only tracking technology, system modeling for parameters prediction is built, and the effectiveness of the SCGNI is examined in ideal and cluttered marine atmospheres simultaneously. The real-time location, velocity, and turn rate of dynamic target are analyzed for five distinct scenarios by varying the standard deviation of white Gaussian observed noise in the context of mean square error (MSE) between real and estimated values. For the given motion parameters prediction problem, sufficient Monte Carlo simulation results support SCGNI’s superiority over typical generalized pseudo-Bayesian filtering strategies such as Interacting Multiple Model Extended Kalman Filter (IMMEKF) and Interacting Multiple Model Unscented Kalman Filter (IMMUKF).

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“…The approach used by Huang et al serves as a compelling example in a greater trend towards novel diagnostics that capture the spatiotemporal structure of intense beams—particles and photons alike. Due to the inherent limitations of 2D detectors to capture 3D structures, snapshot approaches inherently rely on data-driven techniques 16 . Measurements resemble tomographic reconstruction as they capture the beam under scrutiny at different “angles”; by combining multiple diagnostics that each provide a partial constraint, the properties of the beam can be inferred.…”

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confidence: 99%