Motivation
The beam of a laser is largely defined by the transverse modal profiles of the lasing modes. As such, it can be useful to be able to determine:
- What are the transverse modal profiles? (modal recovery)
- What transverse modes are present in a multi-moded beam? (modal decomposition)
The Methods
Provided we have a set of multi-mode near-field images and we know beforehand how much power is in each mode in each image, we can use pixel-wise linear least squares to estimate the modal intensity profile (ie perform modal recovery).
Now, if we know the mode profiles but want to know how much of each mode is present in a multi-mode image/beam, we can use one of many modal decomposition methods. Generally these involve optimizing the modal power coefficients as to obtain a multi-mode near-field that most closely matches the experimental result.
Now, machine learning can be used for both modal decomposition and modal recovery. More can be read on the corresponding page.
Resources
- ModeAnalysis.jl: Julia package that implements some basic modal analysis algorithms
- ModeAnalysisML.jl: Julia package that implements machine learning based modal analysis algorithms