Maximum likelihood method for calibration of Mueller polarimeters in reflection configuration

Area of Science:

• Optical Engineering
• Metrology
• Polarimetry

Background:

• Accurate calibration of Mueller polarimeters is crucial for precise optical measurements.
• Noise in measurements can significantly degrade the performance of calibration algorithms.
• Existing calibration methods may struggle under low signal-to-noise ratio (SNR) conditions.

Purpose of the Study:

• To evaluate and compare the performance of an extended eigenvalue calibration method (ECM) and a maximum likelihood (ML) method for Mueller polarimeters.
• To determine which calibration method provides better precision in the presence of noise.
• To assess the applicability of these methods at varying signal-to-noise ratios.

Main Methods:

• Numerical simulations were conducted to model polarimeter behavior under different noise levels.
• Real-world experiments were performed using a broadband infrared polarimeter.
• The calibration precision of the extended ECM and ML methods was quantitatively assessed.

Main Results:

• The maximum likelihood (ML) method demonstrated superior calibration precision compared to the extended eigenvalue calibration method (ECM).
• The ML method maintained high performance even at lower signal-to-noise ratios where the extended ECM was less effective.
• Both simulation and experimental results consistently showed the advantage of the ML approach.

Conclusions:

• The maximum likelihood (ML) method is recommended for calibrating Mueller polarimeters, particularly when dealing with noisy data or low signal conditions.
• The ML method offers a more robust and precise calibration solution than the extended ECM.
• This finding has implications for improving the accuracy of polarimetric measurements in various scientific and industrial applications.