Wavefront reconstruction and prediction with convolutional neural networks

Robin Swanson; Masen Lamb; Carlos Correia; Suresh Sivanandam; Kiriakos Kutulakos

doi:10.1117/12.2312590

17 July 2018 Wavefront reconstruction and prediction with convolutional neural networks

Robin Swanson, Masen Lamb, Carlos Correia, Suresh Sivanandam, Kiriakos Kutulakos

Proceedings Volume 10703, Adaptive Optics Systems VI; 107031F (2018) https://doi.org/10.1117/12.2312590
Event: SPIE Astronomical Telescopes + Instrumentation, 2018, Austin, Texas, United States

Abstract

While deep learning has led to breakthroughs in many areas of computer science, its power has yet to be fully exploited in the area of adaptive optics (AO) and astronomy as a whole. In this paper we describe the first steps taken to apply deep, convolutional neural networks to the problem of wavefront reconstruction and prediction and demonstrate their feasibility of use in simulation. Our preliminary results show we are able to reconstruct wavefronts comparably well to current state of the art methods. We further demonstrate the ability to predict future wavefronts up to five simulation steps with under 1nm RMS wavefront error.

Conference Presentation

Citation Download Citation

Robin Swanson, Masen Lamb, Carlos Correia, Suresh Sivanandam, and Kiriakos Kutulakos "Wavefront reconstruction and prediction with convolutional neural networks", Proc. SPIE 10703, Adaptive Optics Systems VI, 107031F (17 July 2018); https://doi.org/10.1117/12.2312590

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available