Worked on the XENONnT/straxen repository to refactor the S2 correction pipeline, focusing on improving maintainability and extensibility. Replaced a fixed set of intermediate corrections with a flexible binary encoding system, which streamlined the logic for generating corrected S2 areas and their top fractions. This approach simplified future updates and reduced code complexity, making it easier to extend the correction process as requirements evolve. Utilized Python for scientific computing and data analysis, applying principles from physics simulation to ensure accurate results. The work addressed a critical refactor task, enhancing the overall structure and adaptability of the S2 correction application.