Predictive learn and apply: MAVIS application-learn

Hao Zhang; Jesse Cranney; Nicolas Doucet; Yuxi Hong; Damien Gratadour; Hatem Ltaief; David Keyes; François Rigaut

doi:10.1117/12.2561913

Predictive learn and apply: MAVIS application-learn

Hao Zhang^*, Jesse Cranney, Nicolas Doucet, Yuxi Hong, Damien Gratadour, Hatem Ltaief, David Keyes, François Rigaut

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

The Learn and Apply reconstruction scheme uses the knowledge of atmospheric turbulence to generate a tomographic reconstructor, and its performance is enhanced by the real-time identification of the atmosphere and the wind profile. In this paper we propose a turbulence profiling method that is driven by the atmospheric model. The vertical intensity distribution of turbulence, wind speed and wind direction can be simultaneously estimated from the Laser Guide Star measurements. We introduce the implementation of such a method on a GPU accelerated non-linear least-squares solver, which significantly increases the computation efficiency. Finally, we present simulation results to demonstrate the convergence quality from numerically generated telemetry, the end-to-end Adaptive Optics simulation results, and a time-to-solution analysis, all based on the MAVIS system design.

Original language	English (US)
Title of host publication	Adaptive Optics Systems VII
Editors	Laura Schreiber, Dirk Schmidt, Elise Vernet
Publisher	SPIE
ISBN (Electronic)	9781510636835
DOIs	https://doi.org/10.1117/12.2561913
State	Published - 2020
Event	Adaptive Optics Systems VII 2020 - Virtual, Online, United States Duration: Dec 14 2020 → Dec 22 2020

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	11448
ISSN (Print)	0277-786X
ISSN (Electronic)	1996-756X

Conference

Conference	Adaptive Optics Systems VII 2020
Country/Territory	United States
City	Virtual, Online
Period	12/14/20 → 12/22/20

Bibliographical note

Publisher Copyright:
© 2020 SPIE.

Keywords

Adaptive optics
Learn and Apply
MAVIS
Real-time processing
Stochastic Levenberg-Marquardt
Turbulence profiling

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

Access to Document

10.1117/12.2561913

Cite this

Zhang, H., Cranney, J., Doucet, N., Hong, Y., Gratadour, D., Ltaief, H., Keyes, D., & Rigaut, F. (2020). Predictive learn and apply: MAVIS application-learn. In L. Schreiber, D. Schmidt, & E. Vernet (Eds.), Adaptive Optics Systems VII Article 114482C (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11448). SPIE. https://doi.org/10.1117/12.2561913

@inproceedings{509dc81aecdc44bcb498142f2d844172,

title = "Predictive learn and apply: MAVIS application-learn",

abstract = "The Learn and Apply reconstruction scheme uses the knowledge of atmospheric turbulence to generate a tomographic reconstructor, and its performance is enhanced by the real-time identification of the atmosphere and the wind profile. In this paper we propose a turbulence profiling method that is driven by the atmospheric model. The vertical intensity distribution of turbulence, wind speed and wind direction can be simultaneously estimated from the Laser Guide Star measurements. We introduce the implementation of such a method on a GPU accelerated non-linear least-squares solver, which significantly increases the computation efficiency. Finally, we present simulation results to demonstrate the convergence quality from numerically generated telemetry, the end-to-end Adaptive Optics simulation results, and a time-to-solution analysis, all based on the MAVIS system design.",

keywords = "Adaptive optics, Learn and Apply, MAVIS, Real-time processing, Stochastic Levenberg-Marquardt, Turbulence profiling",

author = "Hao Zhang and Jesse Cranney and Nicolas Doucet and Yuxi Hong and Damien Gratadour and Hatem Ltaief and David Keyes and Fran{\c c}ois Rigaut",

note = "Publisher Copyright: {\textcopyright} 2020 SPIE.; Adaptive Optics Systems VII 2020 ; Conference date: 14-12-2020 Through 22-12-2020",

year = "2020",

doi = "10.1117/12.2561913",

language = "English (US)",

series = "Proceedings of SPIE - The International Society for Optical Engineering",

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}

Zhang, H, Cranney, J, Doucet, N, Hong, Y, Gratadour, D, Ltaief, H , Keyes, D & Rigaut, F 2020, Predictive learn and apply: MAVIS application-learn. in L Schreiber, D Schmidt & E Vernet (eds), Adaptive Optics Systems VII., 114482C, Proceedings of SPIE - The International Society for Optical Engineering, vol. 11448, SPIE, Adaptive Optics Systems VII 2020, Virtual, Online, United States, 12/14/20. https://doi.org/10.1117/12.2561913

Predictive learn and apply: MAVIS application-learn. / Zhang, Hao; Cranney, Jesse; Doucet, Nicolas et al.
Adaptive Optics Systems VII. ed. / Laura Schreiber; Dirk Schmidt; Elise Vernet. SPIE, 2020. 114482C (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11448).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Zhang, Hao

AU - Cranney, Jesse

AU - Doucet, Nicolas

AU - Hong, Yuxi

AU - Gratadour, Damien

AU - Ltaief, Hatem

AU - Keyes, David

AU - Rigaut, François

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Predictive learn and apply: MAVIS application-learn

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this