Design and training of a deep neural network for estimating the optical gain in pyramid wavefront sensors

Camilo Weinberger; Felipe Guzmán; Jorge Tapia; Benoit Neichel; Esteban Vera

Design and training of a deep neural network for estimating the optical gain in pyramid wavefront sensors

Camilo Weinberger, Felipe Guzmán, Jorge Tapia, Benoit Neichel, Esteban Vera

SCHOOL OF ELECTRICAL ENGINEERING

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

Abstract

This work shows the design and training of a convolutional neural network to improve the linear response of a modulated pyramid wavefront sensor, allowing to estimate and compensate for the optical gain in real time.

Original language	English
Article number	JF1B.6
Journal	Optics InfoBase Conference Papers
State	Published - 2022
Event	Propagation Through and Characterization of Atmospheric and Oceanic Phenomena, pcAOP 2022 - Vancouver, Canada Duration: 11 Jul 2022 → 15 Jul 2022

Cite this

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title = "Design and training of a deep neural network for estimating the optical gain in pyramid wavefront sensors",

abstract = "This work shows the design and training of a convolutional neural network to improve the linear response of a modulated pyramid wavefront sensor, allowing to estimate and compensate for the optical gain in real time.",

author = "Camilo Weinberger and Felipe Guzm{\'a}n and Jorge Tapia and Benoit Neichel and Esteban Vera",

note = "Publisher Copyright: {\textcopyright} 2022 The Author(s).; Propagation Through and Characterization of Atmospheric and Oceanic Phenomena, pcAOP 2022 ; Conference date: 11-07-2022 Through 15-07-2022",

year = "2022",

language = "English",

journal = "Optics InfoBase Conference Papers",

issn = "2162-2701",

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T1 - Design and training of a deep neural network for estimating the optical gain in pyramid wavefront sensors

AU - Weinberger, Camilo

AU - Guzmán, Felipe

AU - Tapia, Jorge

AU - Neichel, Benoit

AU - Vera, Esteban

PY - 2022

Y1 - 2022

N2 - This work shows the design and training of a convolutional neural network to improve the linear response of a modulated pyramid wavefront sensor, allowing to estimate and compensate for the optical gain in real time.

AB - This work shows the design and training of a convolutional neural network to improve the linear response of a modulated pyramid wavefront sensor, allowing to estimate and compensate for the optical gain in real time.

UR - http://www.scopus.com/inward/record.url?scp=85139185003&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:85139185003

SN - 2162-2701

JO - Optics InfoBase Conference Papers

JF - Optics InfoBase Conference Papers

M1 - JF1B.6

T2 - Propagation Through and Characterization of Atmospheric and Oceanic Phenomena, pcAOP 2022

Y2 - 11 July 2022 through 15 July 2022

ER -

Design and training of a deep neural network for estimating the optical gain in pyramid wavefront sensors

Abstract

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