Forecasting Wavefront Corrections in an Adaptive Optics System. (arXiv:2112.01437v1 [astro-ph.IM])

<a href="http://arxiv.org/find/astro-ph/1/au:+Hafeez_R/0/1/0/all/0/1">Rehan Hafeez</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Archinuk_F/0/1/0/all/0/1">Finn Archinuk</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Fabbro_S/0/1/0/all/0/1">Sébastien Fabbro</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Teimoorinia_H/0/1/0/all/0/1">Hossen Teimoorinia</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Veran_J/0/1/0/all/0/1">Jean-Pierre Véran</a>

We use telemetry data from the Gemini North ALTAIR adaptive optics system to

investigate how well the commands to the wavefront correctors (Tip/Tilt and

high-order turbulence) can be forecasted in order to reduce lag error and

improve delivered image quality. We show that a high level of reduction ($sim$

5 for Tip-Tilt and $sim$ 2 for high-order modes) can be achieved by using a

“forecasting module” based on a linear auto-regressive models with only a few

coefficients ($sim$ 30 for Tip-Tilt and $sim$ 5 for high-order modes) to

complement the existing integral servo-controller. Updating this module to

adapt to evolving observing conditions is computationally inexpensive and

requires less than 10 seconds worth of telemetry data. We also use several

machine learning models to evaluate whether further improvements could be

achieved with a more sophisticated non-linear model. Our attempts showed no

perceptible improvements over linear auto-regressive predictions, even for

large lags where residuals from the linear models are high, suggesting that

non-linear wavefront distortions for ALTAIR at the Gemini North telescope may

not be forecasted with the current setup.

We use telemetry data from the Gemini North ALTAIR adaptive optics system to

investigate how well the commands to the wavefront correctors (Tip/Tilt and

high-order turbulence) can be forecasted in order to reduce lag error and

improve delivered image quality. We show that a high level of reduction ($sim$

5 for Tip-Tilt and $sim$ 2 for high-order modes) can be achieved by using a

“forecasting module” based on a linear auto-regressive models with only a few

coefficients ($sim$ 30 for Tip-Tilt and $sim$ 5 for high-order modes) to

complement the existing integral servo-controller. Updating this module to

adapt to evolving observing conditions is computationally inexpensive and

requires less than 10 seconds worth of telemetry data. We also use several

machine learning models to evaluate whether further improvements could be

achieved with a more sophisticated non-linear model. Our attempts showed no

perceptible improvements over linear auto-regressive predictions, even for

large lags where residuals from the linear models are high, suggesting that

non-linear wavefront distortions for ALTAIR at the Gemini North telescope may

not be forecasted with the current setup.

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