Mentors
Project Description
Structured light – i.e. light with spatially-tailored intensity, phase, or polarization properties – has become a ubiquitous tool across myriad applications in optics; including biological imaging, quantum optics, telecommunications, and more. In particular, it has been shown that light beams which carry orbital angular momentum (OAM) can propagate for kilometer-scale distances in both free-space and optical fibers, opening up many new avenues for linear and nonlinear photonics due to the robustness and multiplicity of this, in principle, infinite basis.
Targeting specific single, or superpositions of, OAM modes generally requires the use of digital holograms encoded by spatial light modulators (SLMs). These SLM-based generation systems can flexibly excite pure OAM modes; however, optimization and alignment of these systems is non-trivial, and they are often plagued by alignment drift during long-duration experiments. Recently, the High Dimensional Photonics Lab has had encouraging results that indicate that the combination of Multi-Plane Light Conversion (MPLC) – a spatial phase modulation technique for exciting OAM modes with nearly zero theoretical loss – and machine learning methods can be combined to facilitate automatic optimization and alignment of OAM excitation systems.
In this project, we will build upon these results to directly apply machine learning algorithms to optimize and stabilize high power laser generation at novel colors using the nonlinear interaction between OAM modes in optical fibers.
Research Participant
Program: PURSUE REU
Hear what Linrui Tan’s taking with him from his time at BU.
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Research Goals
Learning Goals
Timeline
Weeks 1-2: Gain familiarity with the lab, basics of OAM excitation, begin learning about nonlinear optics.
Weeks 3-4: Gain understanding of the existing GA code, implement in the lab, get first calibration data.
Weeks 5-6: Refine the GA code, compare to human performance, extract key parameters.
Weeks 7-8: Implement the optimized GA to study drift compensation and parameter optimization.
Weeks 9-10: Wrap up experiments, final data collection, prepare for poster presentation.