Identifying optical turbulence profiles for realistic tomographic error in adaptive optics

For extremely large telescopes, adaptive optics will be required to correct the Earth’s turbulent atmosphere. The performance of tomographic adaptive optics is strongly dependent on the vertical distribution (profile) of this turbulence. An important way in which this manifests is the tomographic error, arising from imperfect measurement and reconstruction of the turbulent phase at altitude. Conventionally, a small number of reference profiles are used to obtain this error in simulation; however these profiles are not constructed to be representative in terms of tomographic error. It is therefore unknown whether these simulations are providing realistic performance estimates. Here, we employ analytical adaptive optics simulation that drastically reduces computation times to compute tomographic error for 10 691 measurements of the turbulence profile gathered by the Stereo-SCIDAR instrument at ESO Paranal. We assess for the first time the impact of the profile on tomographic error in a statistical manner. We find, in agreement with previous work, that the tomographic error is most directly linked with the distribution of turbulence into discrete, stratified layers. Reference profiles are found to provide mostly higher tomographic error than expected, which we attribute to the fact that these profiles are primarily composed of averages of many measurements resulting in unrealistic, continuous distributions of turbulence. We propose that a representative profile should be defined with respect to a particular system, and that as such simulations with a large statistical sample of profiles must be an important step in the design process

Reference optical turbulence and wind profiles for single conjugate and extreme adaptive optics

We present a simple method of extracting a small number of reference optical turbulence and wind profiles from a large data set for single conjugate and extreme adaptive optics (AO) simulations. These reference profiles can be used in slow end-to-end AO simulations to represent the variability of the atmosphere. The method is based on the assumption that performance for these systems is correlated with integrated atmospheric parameters r0, θ0, and τ0. Profiles are selected from a large data set that conforms concurrently to the distributions of these parameters, and hence represents the variability of the atmosphere as seen by the AO system. We also extend the equivalent layers method of profile compression to include wind profiles. The method is applied to stereo-SCIDAR data from ESO Paranal to extract five turbulence and wind profiles that cover a broad range in atmospheric variability, and we show using analytical AO simulation that this correlates to the equivalent range of AO-corrected Strehl ratios

Limitations imposed by optical turbulence profile structure and evolution on tomographic reconstruction for the ELT

The performance of tomographic adaptive optics (AO) systems is intrinsically linked to the vertical profile of optical turbulence. First, a sufficient number of discrete turbulent layers must be reconstructed to model the true continuous turbulence profile. Secondly over the course of an observation, the profile as seen by the telescope changes and the tomographic reconstructor must be updated. These changes can be due to the unpredictable evolution of turbulent layers on meteorological time-scales as short as minutes. Here, we investigate the effect of changing atmospheric conditions on the quality of tomographic reconstruction by coupling fast analytical AO simulation to a large data base of 10 691 high-resolution turbulence profiles measured over two years by the Stereo-SCIDAR instrument at ESO Paranal, Chile. This work represents the first investigation of these effects with a large, statistically significant sample of turbulence profiles. The statistical nature of the study allows us to assess not only the degradation and variability in tomographic error with a set of system parameters (e.g. number of layers and temporal update period), but also the required parameters to meet some error threshold. In the most challenging conditions where the profile is rapidly changing, these parameters must be far more tightly constrained in order to meet this threshold. By providing estimates of these constraints for a wide range of system geometries as well as the impact of different temporal optimization strategies we may assist the designers of tomographic AO for the extremely large telescope to dimension their systems