Single-shot polarimetry of vector beams by supervised learning

Area of Science:

• Optics and Photonics
• Machine Learning Applications

Background:

• Vector beams, states of light encoding multiple polarizations, hold significant potential for advanced metrology and communication.
• Current methods for measuring vector beam polarizations are often complex, limiting their practical scalability and compactness.

Purpose of the Study:

• To develop a novel, single-shot polarimetry technique for vector beams that overcomes the limitations of conventional methods.
• To enable scalable, compact, and accurate measurement of multiple polarizations in structured light.

Main Methods:

• Mapping vector beam polarization states into spatial intensity distributions via light scattering.
• Utilizing supervised learning algorithms for single-shot analysis of the scattered light patterns.
• Characterizing polarization states without employing traditional polarization optics.

Main Results:

• Demonstrated accurate characterization of structured light encoding up to nine polarizations.
• Achieved measurement accuracy exceeding 95% for each Stokes parameter.
• Successfully classified vector beams with an unknown number of polarization modes, a capability absent in conventional techniques.

Conclusions:

• The developed method provides a fast, compact, and accurate polarimeter for vector beams.
• This technique has the potential to significantly impact optical devices used in sensing, imaging, and computing.
• Enables broader adoption of polarization-structured light in various scientific and technological applications.