Work

Beaconless adaptive optics for atmospheric laser propagation with multi-plane convolutional neural network

Year: 2025

Type: article

Abstract: We report on the development of a machine learning method to emulate the action of adaptive optics for atmospheric laser propagation in the absence of a dedicated beacon laser. Our technique measures scattered-light intensity profiles recorded in image planes nearly conjugate to the object plane and uses a convolutional neural network to correlate those images to a phase profile, which produces an improved figure of merit. This technique offers the possibility of emulating phase-conjugate adaptive optics in laser-propagation applications where a scattering surface exists at the object plane, but a beacon is unavailable. We quantify the improvement in the figure of merit relative to a beam that has been tip-tilt corrected to remain centered in the object plane.

Source: Optics Express

DOI: 10.1364/oe.561077

Authors: Gregory P. DiComo, George A. Witt, Joshua J. Isaacs, Michael H. Helle

Institutions: United States Naval Research Laboratory, Naval Research Laboratory Plasma Physics Division, University of Maryland, College Park

Language: English

Topic: Adaptive optics and wavefront sensing

Subfield: Atomic and Molecular Physics, and Optics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,270

Funders: Office of Naval Research, Naval Research Enterprise Internship Program