101. Temporal convergence of phase spatial covariance matrix measurements in tomographic adaptive optics
- Author
-
O. Martin, Eric Gendron, Fabrice Vidal, Gérard Rousset, Laboratoire d'études spatiales et d'instrumentation en astrophysique (LESIA), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Haute résolution angulaire en astrophysique, Laboratoire d'études spatiales et d'instrumentation en astrophysique = Laboratory of Space Studies and Instrumentation in Astrophysics (LESIA), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)
- Subjects
Tomographic reconstruction ,Covariance function ,Computer science ,business.industry ,Astrophysics::Instrumentation and Methods for Astrophysics ,System identification ,Phase (waves) ,Estimator ,Matrix (mathematics) ,Convergence (routing) ,Computer vision ,Tomography ,Artificial intelligence ,[PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph] ,Adaptive optics ,business ,Algorithm - Abstract
International audience; The identification of spatial covariance matrices is required in adaptive optics in order to perform tomographic reconstruction with optimal estimators. We use on-sky measurements from Canary, the on-sky demonstrator of MOAO for EAGLE, to study the statistical convergence of the spatial covariance of Shack-Hartmann measurements. We describe a new, faster, analytical approximated model for this spatial covariance, and finally bring into light a new procedure for model identification, reducing the tomographic error. We quantify the gain brought by the new approach on both numerical simulations and on-sky data.
- Published
- 2012