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    This study presents a generalized parallel decomposition for Mueller matrices, improving analysis of polarimetric data. The new method removes normalization constraints, enhancing Mueller matrix decomposition applications.

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    Area of Science:

    • Optics and Photonics
    • Materials Science
    • Data Analysis

    Background:

    • Mueller polarimetry is crucial for analyzing light-matter interactions.
    • Accurate interpretation of Mueller matrices relies on decomposition theorems.
    • Existing parallel decomposition methods have normalization limitations.

    Purpose of the Study:

    • To present the most general formulation for parallel decomposition of a Mueller matrix.
    • To overcome critical limitations of previous Mueller matrix decomposition approaches.
    • To generalize polarimetric subtraction and integrate the passivity criterion.

    Main Methods:

    • Developed a novel mathematical framework for Mueller matrix parallel decomposition.
    • Relaxed the exigency of normalized Mueller matrices for parallel components.
    • Integrated the passivity criterion into arbitrary Mueller matrix decomposition.

    Main Results:

    • Achieved a more general parallel decomposition of Mueller matrices.
    • Eliminated the need for equal transmittances in parallel components.
    • Generalized polarimetric subtraction and arbitrary decomposition.

    Conclusions:

    • The new Mueller matrix decomposition method offers broader applicability.
    • This advancement enhances the analysis and interpretation of polarimetric data.
    • The generalized approach facilitates more robust material characterization.