Apply algorithm, described by Jackson et al. (2012). Raven saw first light in early 2014 and is currently undergoing a commissioning process which should soon see it available for science (Lardière et al., 2014).
3.4.5
VLT Adaptive Optics Facility
UT4 at the VLT is in the process of an upgrade to turn it into a dedicated “adaptive telescope”, called the Adaptive Optics Facility (AOF). After this process all instruments will feature AO correction with tomographic AO configurations available (Kuntschner et al., 2012). All correction is performed by an adaptive secondary mirror with 1170 voice coil actuators. The AOF contains four sodium LGSs, each of which is a 22 W fibre laser made by TOPTICA. These are launched from the side of the telescope (Arsenault et al., 2012).
Two AO systems will be installed, GRAAL and GALACSI. Both systems use four iden- tically designed 40×40 sub-aperture SH LGS WFSs with a dedicated tip-tilt NGS WFS. GRAAL performs GLAO only and feeds HAWK-I, a wide-field imager whilst GALACSI can perform either GLAO or LTAO and feeds the MUSE visible light Integral Field Unit (IFU) spectrograph. In its GLAO mode GALACSI is expected to double flux per channel of the IFU and in LTAO mode to provide Strehl ratios up to 0.1 at a wavelength of 650 nm (Paufique et al., 2012; Ströbele et al., 2012).
Development for the AOF is well underway and commissioning is due to begin in 2015, lasting until 2017, at which point the system will be ready for science (Arsenault et al., 2014).
3.5
Review Summary
In this chapter, current facility NGS and LGS have been outlined. Currently operating tomo- graphic AO systems, including CANARY, Raven, GeMS and ARGOS have been discussed in detail, and the upcoming upgrade to the VLT, AOF, has been considered.
Tomographic AO systems are now beginning to be made available, though in many respects, tomographic AO development is still in its infancy. The method for retrieving LGS uplink tip-tilt information described in § 6 does not place any additional requirements for equipment on AO systems, but does require that the LGSs are launched from within the pupil. This means it could be applicable for the GeMS, CANARY and ARGOS systems, as well as future LGS systems which feature centre launched LGSs.
Chapter
4
A Tomographic LGS Adaptive Optics
Simulation
4.1
Introduction to Simulation in Adaptive Optics
Simulation plays a large role in the development of Adaptive Optics (AO) systems. AO systems are optically complex and require expensive components such as Deformable Mirrors (DMs), Wavefront Sensors (WFSs) and a Real-Time Control System (RTCS), which are non- trivial to setup and optimise. Simulations allow AO scientists to experiment with different optical layouts and components without needing to obtain and prepare them physically. Novel concepts can be explored quickly and performance gains estimated – allowing the AO scientist to decide whether such a concept will provide suitable gains.
Equally important, parameters for new or existing systems can be optimised in a sys- tematic way as it is possible to replicate a configuration in simulation and alter only one variable at a time. This is not an option on facility systems due to time constraints and the random nature of atmospheric turbulence. Accurate simulation is used to tolerance an AO system, for example, finding the performance sensitivity to misalignments of optical compo- nents. All the simulated data on a system can be combined to form an error budget which evaluates all the expected contributions of error to give overall performance estimates.
All AO simulation packages must provide some common functions. First, an atmosphere with a number of turbulence layer is modeled. Many AO simulations generate random phase screens adhering to some theoretical atmospheric statistical description, and are hence de- scribed as being “Monte-Carlo” simulations. One or more guide stars must then be simulated and the phase distortion for a given direction found by propagating light through the cor- responding section of the various atmospheric turbulence layers. It is usually adequate to approximate this light propagation using a geometrical approach (Hardy, 1998), so phase perturbations can simply be summed from one height layer to the next. If the guide star is a Laser Guide Star (LGS) then the cone effect and, if desired, uplink turbulence must also be considered.
Next, WFSs must be simulated. These take the propagated light from a guide star and then model the effects of the optics and detector. Often the WFS will be of the Shack- Hartmann (SH) type, as this is most commonly found in current AO systems. Information
from the WFSs will be passed through a reconstructor and commands sent to a simulated DM. This will create a phase shape to be subtracted from a propagated target astronomical science object phase to finally give a corrected phase. This can then be used to model the corrected science Point Spread Function (PSF) and system performance can be evaluated.
Alternatively, analytical codes also exist which use mathematical descriptions of indi- vidual components to evaluate the expected performance of a system (Rigaut et al., 1998; Jolissaint et al., 2006b). These can be run in much less time than Monte-Carlo codes as they do not have to simulate individual iterations of the system to produce a performance estimate. They do not provide the accuracy of a Monte-Carlo code as it is difficult to create a purely mathematical description of all components of an AO system, including effects such as misalignments, and so various assumptions must be made. Typically, an analytical code is used to roughly estimate performance and a parameter window within which a Monte-Carlo code can investigate further.
In this chapter, existing AO simulations are discussed, limited to Monte-Carlo codes which are available free, open-source and distributed with access to source code. This is vital to be able to alter and extend the code to simulate new AO concepts, not built- in by the code’s author. The suitability of these codes to be used to simulate the LGS uplink tip-tilt retrieval algorithm, where the uplink path of the LGS is accounted for to predict the tip-tilt signal for LGS alone, is discussed before requirements for a new code with which to perform the simulation is considered. A new AO simulation, the Python Adaptive Optics Simulation (PyAOS), written by the author of this thesis in the Python programming language and designed with flexibility and accurate simulation of LGSs as a priority, is introduced and described. Comparisons with an existing and mature code is performed and the data presented.