AI-based electron distribution reconstruction from two screen magnetic spectrometer.

Area of Science:

• Plasma Physics
• Particle Accelerators
• Machine Learning Applications

Background:

• Accurate characterization of fast electron beams is crucial for validating theoretical models in laser-plasma interactions.
• Two-screen magnetic spectrometers are standard tools for measuring electron energy and angular distributions.
• Current data analysis methods are complex, often requiring manual intervention and heuristic approaches.

Purpose of the Study:

• To develop an automated method for reconstructing electron energy and angular distributions.
• To overcome the limitations of traditional, complex data analysis techniques.
• To enable more efficient and accurate experimental validation in laser-plasma physics.

Main Methods:

• A deep neural network was trained using synthetic data generated via numerical simulations.
• Data augmentation techniques were employed to enhance the training dataset due to the unavailability of labeled experimental data.
• The network was designed for simultaneous reconstruction of both energy and angular electron distributions.

Main Results:

• The deep neural network successfully performed automatic and simultaneous reconstruction of electron distributions.
• A cosine similarity of 0.79 was achieved between experimental data and simulated data derived from the network's predictions.
• The proposed method offers a significant improvement over existing heuristic and manual analysis techniques.

Conclusions:

• Deep neural networks provide an effective solution for analyzing complex diagnostic data from fast particle beams.
• The developed method streamlines the process of electron distribution reconstruction, enhancing experimental research in laser-plasma interactions.
• This approach holds promise for broader applications in particle diagnostics and scientific discovery.