The optical turbulence profile is a key parameter in tomographic reconstruction. With interest in tomographic adaptive optics for the next generation of ELTs, turbulence profiling campaigns have produced large quantities of data for observing sites around the world. In order to be useful for Monte Carlo AO simulation, these large datasets must be reduced to a small number of profiles. There is commonly large variation in the structure of the turbulence, therefore statistics such as the median and interquartile range of each altitude bin become less representative as features in the profile are averaged out. Here we present the results of the use of a hierarchical clustering method to reduce the 2018A Stereo-SCIDAR dataset from ESO Paranal, consisting of over 10,000 turbulence profiles measured over 83 nights, to a small set of 18 that represent the most commonly observed profiles....
|Title of host publication||Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series|
|Publication status||Published - Jul 2018|