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The impact of large-scale constellations of satellites, is a concern for ground-based astronomers. In recent years there has been a significant increase in the number of satellites in low-Earth orbit and this trend is set to continue. The large number of satellites increases the probability that one will enter the field of view of a ground-based telescope at the right solar angle to appear bright enough that it can corrupt delicate measurements. We present a new tool ‘Astrosat’ that will project satellite orbits onto the RA/Dec. coordinate system for a given observer location and time and field of view. This enables observers to mitigate the effects of satellite trails through their images by either avoiding the intersection, post-processing using the information as a prior or shuttering the observation for the duration of the transit. We also provide some analysis on the apparent brightness of the largest of the constellations, Starlink, as seen by a typical observatory and as seen with the naked eye. We show that a naked eye observer can typically expect to see a maximum of 5 Starlink satellites at astronomical twilight, when the sky is dark. With the intended 40 000 satellites in the constellation that number would increase to 30.

FAST allows for the rapid simulation of ground-space optical links with AO pre/post compensation. It employs an analytical AO model and enables link characterisation up to 200 times faster than Monte Carlo wave-optical simulations.

The performance of tomographic adaptive optics systems will depend on the vertical profile of turbulence in the atmosphere. Since the profile is changing over time, to maintain optimal correction the tomographic reconstructor must be continually updated with new profile information. Several reconstructor parameters must then be chosen to optimise performance given the constraints of real-time computing resources: the number of reconstructed layers, reoptimisation period and averaging time. We analyse the effect of changing these parameters by coupling fast Fourier-domain AO simulation with a large database of over 10,000 high resolution turbulence profiles measured by the Stereo-SCIDAR at Paranal.

Stereo scintillation detection and ranging (S-SCIDAR) is a development of the well-established SCIDAR turbulence profiling technique. An S-SCIDAR instrument has been installed at the focus of one of the 1.8m Auxiliary Telescopes at Paranal observatory since April 2016. We discuss the limitations imposed by the Paranal S-SCIDAR instrument’s finite pixel size and exposure time. We present Monte Carlo simulation results quantifying the errors due to finite spatial and temporal sampling. We have reprocessed the existing S-SCIDAR dataset to compensate for these error sources; we discuss the impact of these corrections on the measured turbulence statistics.

The performance of tomographic adaptive optics systems is intrinsically linked to the vertical profile of optical turbulence. Firstly, 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 timescales as short as minutes. Here we investigate the effect of changing atmospheric conditions on the quality of tomographic reconstruction by coupling fast analytical adaptive optics simulation to a large database 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, 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 optimisation strategies we may assist the designers of tomographic AO for the ELT to dimension their systems., Comment: 13 pages, 13 figures. Accepted MNRAS March 2020

Context. The wind driven halo is a feature observed within the images delivered by the latest generation of ground-based instruments equipped with an extreme adaptive optics system and a coronagraphic device, such as SPHERE at the VLT. This signature appears when the atmospheric turbulence conditions are varying faster than the adaptive optics loop can correct. The wind driven halo shows as a radial extension of the point spread function along a distinct direction (sometimes referred to as the butterfly pattern). When 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 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 to analyze 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 are capable of describing the wind driven halo present 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 does provide information to accurately describe the wind driven halo contribution in the images. This analysis justifies why this is a fundamental limitation to the final contrast performance reached.Conclusions. With the established procedure, we will analyze a large sample of data delivered by SPHERE in order to propose, in the future, post-processing techniques tailored to remove the wind driven halo., Astronomy and Astrophysics - A&A, EDP Sciences, In press

Knowledge of the Earth's atmospheric optical turbulence is critical for astronomical instrumentation. Not only does it enable performance verification and optimization of the existing systems, but it is required for the design of future instruments. As a minimum this includes integrated astro-atmospheric parameters such as seeing, coherence time, and isoplanatic angle, but for more sophisticated systems such as wide-field adaptive optics enabled instrumentation the vertical structure of the turbulence is also required. Stereo-SCIDAR (Scintillation Detection and Ranging) is a technique specifically designed to characterize the Earth's atmospheric turbulence with high-altitude resolution and high sensitivity. Together with ESO (European Southern Observatory), Durham University has commissioned a Stereo-SCIDAR instrument at Cerro Paranal, Chile, the site of the Very Large Telescope (VLT), and only 20 km from the site of the future Extremely Large Telescope (ELT). Here we provide results from the first 18 months of operation at ESO Paranal including instrument comparisons and atmospheric statistics. Based on a sample of 83 nights spread over 22 months covering all seasons, we find the median seeing to be 0.64″ with 50 per cent of the turbulence confined to an altitude below 2 km and 40 per cent below 600 m. The median coherence time and isoplanatic angle are found as 4.18 ms and 1.75″, respectively. A substantial campaign of inter-instrument comparison was also undertaken to assure the validity of the data. The Stereo-SCIDAR profiles (optical turbulence strength and velocity as a function of altitude) have been compared with the Surface-Layer Slope Detection And Ranging, Multi-Aperture Scintillation Sensor-Differential Image Motion Monitor, and the European Centre for Medium Range Weather Forecasts model. The correlation coefficients are between 0.61 (isoplanatic angle) and 0.84 (seeing).

AOtools is a Python package which 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., Accepted in Optics Express

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., Accepted MNRAS June 2019

We present the results from a Monte Carlo computer simulation of adaptive optics (AO) pre-compensated laser uplink propagation through the Earth’s atmospheric turbulence from the ground to orbiting satellites. The simulation includes the so-called point-ahead angle and tests several potential AO mitigation modes such as tip/tilt or full AO from the downlink beam, and a laser guide star at the point ahead angle. The performance of these modes, as measured by metrics relevant for free-space optical communication, are compared with no correction and perfect correction. The aim of the study is to investigate fundamental limitations of free-space optical communications with AO pre-compensation and a point-ahead angle, therefore the results represent an upper bound of AO corrected performance, demonstrating the potential of pre-compensation technology. Performance is assessed with varying launch aperture size, wavelength, launch geometry, ground layer turbulence strength (i.e. day/night), elevation angle and satellite orbit (Low-Earth and Geostationary). By exploring this large parameter space we are able examine trends on performance with the aim of informing the design of future optical ground stations and demonstrating and quantifying the potential upper bounds of adaptive optics performance in free-space optical communications.

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 (ELTs). Site characterisation and monitoring campaigns have produced large quantities of turbulence profiling data for sites around the world. However AO system design and performance characterisation is dependent on Monte-Carlo simulations that cannot make use of these large datasets due to long computation times. Here we address the question of how to reduce these large datasets into small sets of profiles that can feasibly be used in such Monte-Carlo simulations, whilst minimising the loss of information inherent in this effective compression of the data. We propose hierarchical clustering to partition the dataset according to the structure of the turbulence profiles and extract a single profile from each cluster. This method is applied to the Stereo-SCIDAR dataset from ESO Paranal containing over 10000 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 dataset. For generality we choose integrated parameters of the turbulence to measure the representativeness of our profiles and compare to others. Using these criterion we also show that such variability is difficult to capture with small sets of profiles associated with integrated turbulence parameters such as seeing., Accepted MNRAS September 2018

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.