Distributed Kalman filtering compared to Fourier domain preconditioned conjugate gradient for laser guide star tomography on extremely large telescopes

This paper discusses the performance and cost of two computationally efficient Fourier-based tomographic wavefront reconstruction algorithms for wide-field laser guide star (LGS) adaptive optics (AO). The first algorithm is the iterative Fourier domain preconditioned conjugate gradient (FDPCG) algorithm developed by Yang et al. [Appl. Opt. 45, 5281 (2006)], combined with pseudo-open-loop control (POLC). FDPCG’s computational cost is proportional to N log(N), where N denotes the dimensionality of the tomography problem. The second algorithm is the distributed Kalman filter (DKF) developed by Massioni et al. [J. Opt. Soc. Am. A 28, 2298 (2011)], which is a noniterative spatially invariant controller. When implemented in the Fourier domain, DKF’s cost is also proportional to N log(N). Both algorithms are capable of estimating spatial frequency components of the residual phase beyond the wavefront sensor (WFS) cutoff frequency thanks to regularization, thereby reducing WFS spatial aliasing at the expense of more computations. We present performance and cost analyses for the LGS multiconjugate AO system under design for the Thirty Meter Telescope, as well as DKF’s sensitivity to uncertainties in wind profile prior information. We found that, provided the wind profile is known to better than 10% wind speed accuracy and 20 deg wind direction accuracy, DKF, despite its spatial invariance assumptions, delivers a significantly reduced wavefront error compared to the static FDPCG minimum variance estimator combined with POLC. Due to its nonsequential nature and high degree of parallelism, DKF is particularly well suited for real-time implementation on inexpensive off-the-shelf graphics processing units

Adaptive optics telemetry standard: Design and specification of a novel data exchange format

Context. The amount of adaptive optics (AO) telemetry generated by visible/near-infrared ground-based observatories is ever greater, leading to a growing need for a standardised data exchange format to support performance analysis, AO research, and development activities that involve large-scale telemetry mining, processing, and curation.Aims. This paper introduces the Adaptive Optics Telemetry (AOT) data exchange format as a standard for sharing AO telemetry from visible/infrared ground-based observatories. AOT is based on the flexible image transport system (FITS) and aims to provide unambiguous and consistent data access across various systems and configurations, including natural and single- or multiple-laser guide-star AO systems.Methods. We designed AOT with a focus on two key use cases: atmospheric turbulence parameter estimation and point-spread function reconstruction. We prototyped and tested the design using existing AO telemetry datasets from multiple systems: single conjugate with natural and laser guide stars, tomographic systems with multi-channel wavefront sensors, and single- and multi-wavefront correctors in systems featuring either a Shack-Hartmann or Pyramid as the main wavefront sensor.Results. The AOT file structure has been thoroughly defined, with specified data fields, descriptions, data types, units, and expected dimensions. To support this format, we have developed a Python package that enables the data conversion, reading, writing, and exploration of AOT files; it has been made publicly available and is compatible with a general-purpose Python package manager. We have demonstrated the flexibility of the AOT format by packaging data from five different instruments, installed on different telescopes

SAXO+ upgrade : second stage AO system end-to-end numerical simulations

SAXO+ is a proposed upgrade to SAXO, the AO system of the SPHERE instrument on the ESO Very Large Telescope. It will improve the capabilities of the instrument for the detection and characterization of young giant planets. It includes a second stage adaptive optics system composed of a dedicated near-infrared wavefront sensor and a deformable mirror. This second stage will remove the residual wavefront errors left by the current primary AO loop (SAXO). This paper focuses on the numerical simulations of the second stage (SAXO+) and concludes on the impact of the main AO parameters used to build the design strategy. Using an end-to-end AO simulation tool (COMPASS), we investigate the impact of several parameters on the performance of the AO system. We measure the performance in minimizing the star residuals in the coronagraphic image. The parameters that we study are : the second stage frequency, the photon flux on each WFS, the first stage gain and the DM number of actuators of the second stage. We show that the performance is improved by a factor 10 with respect to the current AO system (SAXO). The optimal second stage frequency is between 1 and 2 kHz under good observing conditions. In a red star case, the best SAXO+ performance is achieved with a low first stage gain of 0.05, which reduces the first stage rejection.Comment: 10 pages, 8 figures. Submitted to AO4ELT7 conference proceeding

Multi-core fibre-fed integral-field unit (MCIFU):Overview and first-light

The Multi-Core Integral-Field Unit (MCIFU) is a new diffraction-limited near-infrared integral-field unit for exoplanet atmosphere characterization with extreme adaptive optics (xAO) instruments. It has been developed as an experimental pathfinder for spectroscopic upgrades for SPHERE+/VLT and other xAO systems. The wavelength range covers 1.0 um to 1.6um at a resolving power around 5000 for 73 points on-sky. The MCIFU uses novel astrophotonic components to make this very compact and robust spectrograph. We performed the first successful on-sky test with CANARY at the 4.2 meter William Herschel Telescope in July 2019, where observed standard stars and several stellar binaries. An improved version of the MCIFU will be used with MagAO-X, the new extreme adaptive optics system at the 6.5 meter Magellan Clay telescope in Chile. We will show and discuss the first-light performance and operations of the MCIFU at CANARY and discuss the integration of the MCIFU with MagAO-X.</p

High performance control for adaptive optics systems: from models to on-sky data... and back.

Séminaire de Gemini South Observatory, Chili, 2015

Perturbation modeling and high performance control for AO systems: from models to on-sky data... and back.

Séminaire du Laboratoire d’Astrophysique de Marseille, 2015

Adaptive optics control in retinal imaging

International audienc

Delay-Based Non-linear Observers for Congestion Control in Communication Networks

International audienceThe purpose of this contribution is to investigate the construction and use of delay-based observers in communication networks, starting from a state-space model of an elementary network configuration. This work is a step towards the design of a source-rate congestion and delay control algorithm adapted to applications with real-time constraints, such as video streaming. Our ultimate aim is to design an “end to end” control scheme implemented at source level, using only feedback information from the destinations transmitted through the network as part of the general “best effort” data stream, and which could therefore be deployed on essentially every existing network infrastructure, especially those ruled by the Internet Protocol. Obviously, observer-based control structures provide an appropriate conceptual framework to deal with such measurement constraints. In this particular application, we propose to combine a linear state-feedback plus disturbance feedforward control with a non-linear observer of network congestion fed with measurements of source-to-destination transmission delay

Adaptive optics control in retinal imaging

International audienc

Dual Control of Linearly Parameterised Models via Prediction of Posterior Densities

International audienceA suboptimal dual control policy is presented for linearly parameterised systems with unknown parameters, additive Gaussian noise and quadratic cost. The dual effect of the control action is taken into account through the prediction of the future posterior densities of the model parameters θ. If θ has a normal prior density, the model response is linear in θ and contains no autoregressive part, then the posterior densities of θ are normal and their covariance matrices are known functions of the control actions. Replacing the unknown future posterior means by the current parameter estimates, one can easily approximate the costto- go. Two examples of FIR models illustrate the superiority ofthis dual control policy over two classical passive policies, namely heuristic certainty equivalence control and open-loop-feedback-optimal control