Machine Learning for the Enhancement of Adaptive Optics
Name
MIT-LIN-151375.pdf
Description
Main Report
Size
3.47 MB
Format
Adobe PDF
Checksum (MD5)
300d982c72a2cf50cd590d5200f2f606
Author(s)
Hall, Robert
Chen, Justin
Date Issued
February 17, 2026
Abstract
Optical systems (telescopes, lasers, microscopes,
etc.) have degraded performance over long distances
due to scintillation caused by Earth’s atmosphere,
where adaptive optics (AO) is often used to enhance
its signal-to-noise (SNR) ratio or image quality. Astronomers
have found success in laser-based adaptive
optics where they survey the atmosphere with a laser
and subtract its effects on the resultant image. Although
effective in most cases, these systems can be extremely
costly, are computationally intensive in real time, and
fall short in some edge cases. We propose an autoencoder/
decoder and a generalized sequence to sequence
model (LSTM) as a cost-effective method to off-load
computational complexity from real time and enhance
performance in edge cases. This study utilizes four
simulated datasets of wavefront sensor frames for a
variety of atmospheric conditions, done in collaboration
with MIT Lincoln Laboratory [1]–found auto-encoding
performance just shy of traditional methodology and
found LSTM performance that predicts well the general
shape on the WFS, but suffers from scaling issues.
Subjects
Adaptive Optics
MIT Department
Lincoln Laboratory
Persistent DSpace Link
