Reconstructing magnetic deflections from sets of proton images using differential evolution

Area of Science:

• Physics
• Applied Physics
• Computational Physics

Background:

• Proton imaging visualizes electromagnetic fields by analyzing spatial variations in proton fluence.
• Caustics, resulting from nonlinear proton trajectory deflections, hinder accurate field reconstruction.
• Existing methods struggle with the complex field information obscured by caustics.

Purpose of the Study:

• To develop a new method for reconstructing path-integrated magnetic fields from proton images, specifically addressing the challenge of caustics.
• To improve the accuracy and uniqueness of field reconstruction in the presence of nonlinearities and overlapping proton trajectories.
• To provide a robust technique for analyzing electromagnetic fields in experimental volumes.

Main Methods:

• Utilizes multiple proton images of the same object, acquired at different proton energies.
• Employs a differential evolution algorithm for iterative estimation of the underlying deflection function.
• Reconstructs path-integrated magnetic fields by simultaneously matching proton fluence across multiple energy levels.

Main Results:

• The developed method successfully reconstructs underlying fields from both linear and nonlinear deflection features, including caustics.
• Reconstruction accuracy is demonstrated using synthetic data for various cylindrically symmetric field geometries and noise levels.
• Experimental data validation shows the method's capability to solve for fields underlying complex proton image features.

Conclusions:

• The novel multi-energy proton imaging reconstruction method effectively overcomes the limitations imposed by caustics.
• This approach offers a significant advancement in accurately determining electromagnetic fields from proton imaging data.
• The technique is robust to noise and does not assume linearity, broadening its applicability in scientific research.