AOtools - a Python package for adaptive optics modelling and analysis

AOtools is a Python package that is open-source and aimed at providing tools for adaptive optics users and researchers. We present version 1.0, which contains tools for adaptive optics processing, including analysing data in the pupil plane, images and point spread functions in the focal plane, wavefront sensors, modelling of atmospheric turbulence, physical optical propagation of wavefronts, and conversion between frequently used adaptive optics and astronomical units. The main drivers behind AOtools is that it should be easy to install and use. To achieve this the project features extensive documentation, automated unit testing and is registered on the Python Package Index. AOtools is under continuous active development to expand the features available, and we encourage everyone involved in adaptive optics to become involved and contribute to the project

FAST: Fourier domain adaptive optics simulation tool for bidirectional ground-space optical links through atmospheric turbulence

Free space optical links between the ground and space may be severely degraded by atmospheric turbulence. Adaptive Optics, a technique allowing partial correction of this degradation, is beginning to see use in the field with the potential to achieve more robust and higher bandwidth links. Here we present a simulation tool, FAST, which utilises an analytical Fourier domain Adaptive Optics model developed for astronomy. Using the reciprocity principle, the simulation may be applied either to downlink post-compensated or uplink pre-compensated beams. We show that FAST gives similar results to full end-to-end simulations with wave-optical propagation whilst being between 10 and 200 times faster, enabling the characterisation of optical links with complex Adaptive Optics systems in timely fashion

Representative optical turbulence profiles for ESO Paranal by hierarchical clustering

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

Wind-driven halo in high-contrast images

Context. The wind-driven halo is a feature that is observed in images that were delivered by the latest generation of ground-based instruments that are equipped with an extreme adaptive optics system and a coronagraphic device, such as SPHERE at the Very Large Telescope (VLT). This signature appears when the atmospheric turbulence conditions vary faster than the adaptive optics loop can correct for. The wind-driven halo is observed as a radial extension of the point spread function along a distinct direction (this is sometimes referred to as the butterfly pattern). When this is present, it significantly limits the contrast capabilities of the instrument and prevents the extraction of signals at close separation or extended signals such as circumstellar disks. This limitation is consequential because it contaminates the data for a substantial fraction of the time: about 30% of the data produced by the VLT/SPHERE instrument are affected by the wind-driven halo. Aims. This paper reviews the causes of the wind-driven halo and presents a method for analyzing its contribution directly from the scientific images. Its effect on the raw contrast and on the final contrast after post-processing is demonstrated. Methods. We used simulations and on-sky SPHERE data to verify that the parameters extracted with our method can describe the wind-driven halo in the images. We studied the temporal, spatial, and spectral variation of these parameters to point out its deleterious effect on the final contrast. Results. The data-driven analysis we propose provides information to accurately describe the wind-driven halo contribution in the images. This analysis confirms that this is a fundamental limitation of the finally reached contrast performance. Conclusions. With the established procedure, we will analyze a large sample of data delivered by SPHERE in order to propose post-processing techniques that are tailored to removing the wind-driven halo