Training and using the deep learning wavefront sensor

Camilo Weinberger; Felipe Guzman; Esteban Vera

Training and using the deep learning wavefront sensor

Camilo Weinberger, Felipe Guzman, Esteban Vera

SCHOOL OF ELECTRICAL ENGINEERING

Research output: Contribution to journal › Conference article › peer-review

Abstract

We propose an Adaptive Optics system controlled by a Slow Deep Learning Wavefront Sensor architecture that can predict and correct the first Zernike modes of low refresh frequency atmospheric effects to anticipate the AO in astronomical observations.

Original language	English
Article number	JW2A.24
Journal	Optics InfoBase Conference Papers
State	Published - 2020
Event	Computational Optical Sensing and Imaging, COSI 2020 - Part of Imaging and Applied Optics Congress 2020 - Virtual, Online, United States Duration: 22 Jun 2020 → 26 Jun 2020

Cite this

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title = "Training and using the deep learning wavefront sensor",

abstract = "We propose an Adaptive Optics system controlled by a Slow Deep Learning Wavefront Sensor architecture that can predict and correct the first Zernike modes of low refresh frequency atmospheric effects to anticipate the AO in astronomical observations.",

author = "Camilo Weinberger and Felipe Guzman and Esteban Vera",

note = "Publisher Copyright: {\textcopyright} 2020 The Author(s); Computational Optical Sensing and Imaging, COSI 2020 - Part of Imaging and Applied Optics Congress 2020 ; Conference date: 22-06-2020 Through 26-06-2020",

year = "2020",

language = "English",

journal = "Optics InfoBase Conference Papers",

issn = "2162-2701",

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T1 - Training and using the deep learning wavefront sensor

AU - Weinberger, Camilo

AU - Guzman, Felipe

AU - Vera, Esteban

PY - 2020

Y1 - 2020

N2 - We propose an Adaptive Optics system controlled by a Slow Deep Learning Wavefront Sensor architecture that can predict and correct the first Zernike modes of low refresh frequency atmospheric effects to anticipate the AO in astronomical observations.

AB - We propose an Adaptive Optics system controlled by a Slow Deep Learning Wavefront Sensor architecture that can predict and correct the first Zernike modes of low refresh frequency atmospheric effects to anticipate the AO in astronomical observations.

UR - http://www.scopus.com/inward/record.url?scp=85118640980&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:85118640980

SN - 2162-2701

JO - Optics InfoBase Conference Papers

JF - Optics InfoBase Conference Papers

M1 - JW2A.24

T2 - Computational Optical Sensing and Imaging, COSI 2020 - Part of Imaging and Applied Optics Congress 2020

Y2 - 22 June 2020 through 26 June 2020

ER -

Training and using the deep learning wavefront sensor

Abstract

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