Statistical analysis and lessons learned of SPHERE adaptive optics performance

D. Mouillet; J. Milli; J.-F. Sauvage; T. Fusco; J.-L. Beuzit; A. Vigan; D. Albert; A. Boccaletti; F. Cantalloube; G. Chauvin; C. Correia; P. Delorme; K. Dohlen; M. Kasper; A.-M. Lagrange; N. Meunier; C. Pannetier

doi:10.1117/12.2313277

10 July 2018 Statistical analysis and lessons learned of SPHERE adaptive optics performance

D. Mouillet, J. Milli, J.-F. Sauvage, T. Fusco, J.-L. Beuzit, A. Vigan, D. Albert, A. Boccaletti, F. Cantalloube, G. Chauvin, C. Correia, P. Delorme, K. Dohlen, M. Kasper, A.-M. Lagrange, N. Meunier, C. Pannetier

Author Affiliations +

Proceedings Volume 10703, Adaptive Optics Systems VI; 107031Q (2018) https://doi.org/10.1117/12.2313277
Event: SPIE Astronomical Telescopes + Instrumentation, 2018, Austin, Texas, United States

Abstract

The SPHERE instrument, dedicated to high contrast imaging on VLT, has been routinely operated for more than 3 years, over a large range of conditions and producing observations from visible to NIR. A central part of the instrument is the high order adaptive optics system, named SAXO, designed to deliver high Strehl image quality with a balanced performance budget for bright stars up to magnitude R=9. We take benefit now from the very large set of observations to revisit the assumptions and analysis made at the time of the design phase: we compare the actual AO behavior as a function of expectations. The data set consists of the science detector data, for both coronagraphic images and non-coronagraphic PSF calibrations, but also of AO internal data from the high frequency sensors and statistics computations from the real-time computer which are systematically archived, and finally of environmental data, monitored at VLT level. This work is supported and made possible by the SPHERE « Data Center » infrastructure hosted at Grenoble which provides an efficient access and the capability for the homogeneous analysis of this large and statistically-relevant data set. We review in a statistical manner the actual AO performance as a function of external conditions for different regimes and we discuss the possible performance metrics, either derived from AO internal data or directly from the high contrast images. We quantify the dependency of the actual performance on the most relevant environmental parameters. By comparison to earlier expectations, we conclude on the reliability of the usual AO modeling. We propose some practical criteria to optimize the queue scheduling and the expression of observer requirements ; finally, we revisit what could be the most important AO specifications for future high contrast imagers as a function of the primary science goals, the targets and the turbulence properties.

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D. Mouillet, J. Milli, J.-F. Sauvage, T. Fusco, J.-L. Beuzit, A. Vigan, D. Albert, A. Boccaletti, F. Cantalloube, G. Chauvin, C. Correia, P. Delorme, K. Dohlen, M. Kasper, A.-M. Lagrange, N. Meunier, and C. Pannetier "Statistical analysis and lessons learned of SPHERE adaptive optics performance", Proc. SPIE 10703, Adaptive Optics Systems VI, 107031Q (10 July 2018); https://doi.org/10.1117/12.2313277

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