Olivier Beltramo-Martin’s work is dedicated to enhance the science exploitation of Adaptive-optics assisted astronomical instruments that equip ground-based telescopes, through optimal real-time control for tomographic systems, PSF determination for post-processing of AO images and atmospheric turbulence characterization. He achieved his PhD graduation supervised by G. Rousset in 2014 at the Observatoire de Meudon, LESIA on the demonstration on lasers-based multi-objects adaptive optics with the pathfinder CANARY. After a 2 years-long break for teaching fundamental physics in high-school, he’s got back in the field and now occupied a post-doc position at LAM since 2016 and ONERA/LAM since 2019. He’s responsible for the APPLY project WP2 dedicated todvanced PSF estimation algorithms, and involved in the post-processing WPs of MAVIS and HARMONI. He’s also pushing hard for the dissemination of PSF reconstruction activity into the astronomical community by chairing the workshop AO4ASTRO.
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Tomography requires the knowledge of the statistical turbulence parameters, commonly recovered from the system telemetry using a dedicated profiling technique. For demonstration purposes with the MOAO pathfinder CANARY, this identification is performed thanks to the Learn & Apply (L&A) algorithm, that consists in model-fitting the covariance matrix of WFS measurements dependant on relevant parameters: Cn2(h) profile, outer scale profile and system mis-registration.
We explore an upgrade of this algorithm, the Learn 3 Steps (L3S) approach, that allows one to dissociate the identification of the altitude layers from the ground in order to mitigate the lack of convergence of the required empirical covariance matrices therefore reducing the required length of data time-series for reaching a given accuracy. For nominal observation conditions, the L3S can reach the same level of tomographic error in using five times less data frames than the L&A approach.
The L3S technique has been applied over a large amount of CANARY data to characterize the turbulence above the William Herschel Telescope (WHT). These data have been acquired the 13th, 15th, 16th, 17th and 18th September 2013 and we find 0.67"/8.9m/3.07m.s−1 of total seeing/outer scale/wind-speed, with 0.552"/9.2m/2.89m.s−1 below 1.5 km and 0.263"/10.3m/5.22m.s−1 between 1.5 and 20 km. We have also determined the high altitude layers above 20 km, missed by the tomographic reconstruction on CANARY , have a median seeing of 0.187" and have occurred 16% of observation time.
We have developed a Point Spread Function (PSF)-Reconstruction algorithm dedicated to MOAO systems using system telemetry to estimate the PSF potentially anywhere in the observed field, a prerequisite to deconvolve AO-corrected science observations in Integral Field Spectroscopy (IFS). Additionally the ability to accurately reconstruct the PSF is the materialization of the broad and fine-detailed understanding of the residual error contributors, both atmospheric and opto-mechanical.
In this paper we compare the classical PSF-r approach from Véran (1) that we take as reference on-axis using the truth-sensor telemetry to one tailored to atmospheric tomography by handling the off-axis data only.
We've post-processed over 450 on-sky CANARY data sets with which we observe 92% and 88% of correlation on respectively the reconstructed Strehl Ratio (SR)/Full Width at Half Maximum (FWHM) compared to the sky values. The reference method achieves 95% and 92.5% exploiting directly the measurements of the residual phase from the Canary Truth Sensor (TS).
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