Representative optical turbulence profiles for ESO Paranal by hierarchical clustering.

Abstract

Knowledge of the optical turbulence profile is important in adaptive optics (AO) systems, particularly tomographic AO systems such as those to be employed by the next generation of 40-m class extremely large telescopes. Site characterization and monitoring campaigns have produced large quantities of turbulence profiling data for sites around the world. However AO system design and performance characterization is dependent on Monte Carlo simulations that cannot make use of these large data sets due to long computation times. Here we address the question of how to reduce these large data sets into small sets of profiles that can feasibly be used in such Monte Carlo simulations, whilst minimizing the loss of information inherent in this effective compression of the data. We propose hierarchical clustering to partition the data set according to the structure of the turbulence profiles and extract a single profile from each cluster. This method is applied to the Stereo-SCIDAR (SCIntillation Detection And Ranging) data set from ESO Paranal containing over 10 000 measurements of the turbulence profile from 83 nights. We present two methods of extracting turbulence profiles from the clusters, resulting in two sets of 18 profiles providing subtly different descriptions of the variability across the entire data set. For generality we choose integrated parameters of the turbulence to measure the representativeness of our profiles and compare to others. Using these criteria we also show that such variability is difficult to capture with small sets of profiles associated with integrated turbulence parameters such as seeing.