Proton-impact ionisation cross sections for nanodosimetric track structure simulations.

Area of Science:

• Atomic and Molecular Physics
• Radiation Physics
• Computational Physics

Background:

• Monte Carlo simulations of particle track structure necessitate precise ion- and electron-impact cross-section data.
• Existing cross-section data are often scarce and exhibit inconsistencies across different experimental groups.
• Accurate data are vital for reliable simulations in fields like radiation dosimetry and material science.

Purpose of the Study:

• To review and compile existing literature data for ionisation cross sections (CSs) of nitrogen and propane for proton impact.
• To implement these reviewed cross-section data into the PTra simulation code.
• To benchmark the PTra code by comparing its simulated particle-track parameters with experimental measurements.

Main Methods:

• Literature data on proton-impact ionisation cross sections for nitrogen were collected and reviewed.
• Proton-impact cross-section data for propane were derived using methane data due to a lack of direct measurements.
• The PTra code was utilized to perform Monte Carlo simulations incorporating the compiled cross-section data.
• Simulated particle-track parameters were compared against experimental data obtained from an ion-counting nanodosimeter.

Main Results:

• A comprehensive dataset of ionisation cross sections for nitrogen and propane under proton impact was compiled and implemented.
• The PTra code, utilizing the implemented cross-section data, demonstrated good agreement with experimental particle-track parameters.
• The study successfully addressed the scarcity of reliable cross-section data for propane by utilizing methane data.

Conclusions:

• The PTra code, enhanced with reviewed and implemented cross-section data, provides a reliable tool for simulating particle tracks.
• The methodology of using surrogate data (methane for propane) is effective in addressing data gaps for specific materials.
• Accurate cross-section data are fundamental for advancing the precision of radiation transport and dosimetry simulations.