Abstract
Estimating turbulence parameters is essential during commissioning and optimising adaptive optics or fringe tracking systems. It also gained new relevance with free-space optical communication applications. The estimation of such parameters is done under the assumption of stationarity. Yet, the stationarity time scale of the atmospheric turbulence is unknown. The breakdown of this assumption leads to incorrect estimates and added error terms. In this paper, we illustrate stationarity detection with unit root testing and the pitfalls of its application to turbulence parameter time series.

Abstract
To facilitate easy prediction and estimation of Adaptive Optics performance, we have created a fast algorithm named TipTop. This algorithm generates the expected AO Point Spread Function (PSF) for any existing AO observing mode (SCAO, LTAO, MCAO, GLAO) and any set of atmospheric conditions. Developed in Python, TipTop is based on an analytical approach, with simulations performed in the Fourier domain, enabling very fast computation times (less than a second per PSF) and efficient exploration of the extensive parameter space. TipTop can be used for several applications, from assisting in the observation preparation with the Exposure Time Calculator (ETC), to providing PSF models for post-processing. TipTop can also be used to help users in selecting the best NGSs asterism and optimizing their observation. Over the past years, the code has been intensively tested against different other simulation tools, showing very good agreements. TipTop is also currently deployed for VLT instruments, as proof of concepts in preparation of the ELT. The code is available here: https://tiptop.readthedocs.io/en/main/, and we encourage all future observers of the ELT to test it and provide feedback !

Abstract
The first scientific observations with adaptive optics (AO) at W. M. Keck Observatory (WMKO) began in 1999. Through 2023, over 1200 refereed science papers have been published using data from the WMKO AO systems. The scientific competitiveness of AO at WMKO has been maintained through a continuous series of AO and instrument upgrades and additions. This tradition continues with AO being a centerpiece of WMKO's scientific strategic plan for 2035. We will provide an overview of the current and planned AO projects from the context of this strategic plan. The current projects include implementation of new real-time controllers, the KAPA laser tomography system and the HAKA high-order deformable mirror system, the development of multiple advanced wavefront sensing and control techniques, the ORCAS space-based guide star project, and three new AO science instruments. We will also summarize steps toward the future strategic directions which are centered on ground-layer, visible and high-contrast AO.

Abstract
The Real Time Controllers (RTCs) for the W. M. Keck Observatory Adaptive Optics (AO) systems have been upgraded from a Field Programmable Gate Array (FPGA) to a Graphics Processing Unit (GPU) based solution. The previous RTCs, operating since 2007, had reached their limitations after upgrades to support new hardware including an Infra-Red (IR) Tip/Tilt (TT) Wave Front Sensor (WFS) on Keck I and a Pyramid WFS on Keck II. The new RTC, fabricated by a Microgate-led consortium with SUT leading the computation engine development, provides a flexible platform that improves processing bandwidth and allows for easier integration with new hardware and control algorithms. Along with the new GPU-based RTC, the upgrade includes a new hardware Interface Module (IM), new OCAM2K EMCCD cameras, and a new Telemetry Recording Server (TRS). The first system upgrade to take advantage of the new RTC is the Keck I All-sky Precision Adaptive Optics (KAPA) Laser Tomography AO (LTAO) system, which uses the larger and more sensitive OCAM2K EMCCD camera, tomographic reconstruction from four Laser Guide Stars (LGS), and improvements to the IR TT WFS. On Keck II the new RTC will enable a new higher-order Deformable Mirror (DM) as part of the HAKA (High order Advanced Keck Adaptive optics) project, which will also use an EMCCD camera. In the future, the new RTC will allow the possibility for new developments such as the proposed 'IWA (Infrared Wavefront sensor Adaptive optics) system. The new RTC saw first light in 2021. The Keck I system was released for science observations in late 2023, with the Keck II system released for science in early 2024.

Abstract
METIS, the Mid-infrared ELT Imager and Spectrograph, will operate an internal Single Conjugate Adaptive Optics (SCAO) system, which will mainly serve the science cases targeting exoplanets and disks around bright stars. The Extremely Large Telescope (ELT) is expected to have its first light in 2028, and the entire instrument recently passed its final design phase. The adaptive optics (AO) of METIS SCAO is designed to correct for atmospheric distortions, and is essential for diffraction-limited observations with METIS. The computational and data transfer requirements for these next generation ELT AO Real-Time Computers (RTCs) are enormous, and require advanced data processing and pipelining techniques. METIS SCAO will use a pyramid wavefront sensor (WFS), which captures incoming wavefronts at 1 kHz with a raw throughput of 148 MB/s. The RTC will ingest these WFS images on a frame-by-frame basis, compute the corrections and send them to the deformable mirror M4 and the tip/tilt mirror M5. The RTC is split up into two distinct systems: the Hard Real-Time Computer (HRTC) and the Soft Real-Time Computer (SRTC). The HRTC is responsible for computing the time sensitive wavefront control loop, while the SRTC is responsible for supervising and optimising the HRTC. A working prototype for the HRTC has been completed and operates with an RTC computation time of roughly 372 mu s. This computation is memory limited and runs on two NVIDIA A100 GPUs. This paper shows a breakdown of the HRTC on a CUDA kernel level, focusing on the tasks that run on the GPUs. We also present the performance of the HRTC and possible improvements for it.

Abstract
Context. The deployment of meter-scale (hitherto pre-focal) adaptive deformable mirrors finds some prominent examples in the leading ground-based visible to near-infrared facilities (e.g. the Very Large Telescope (VLT), the Large Binocular Telescope (LBT), or the Magellan Telescope) and is being adopted by several others (e.g. the Multiple Mirror Telescope (MMT) or Subaru). Furthermore, two out of the three giant segmented-mirror telescopes now under design will feature them. In all these cases, the proprietary technology is based on voice-coils and is limited in force, stroke, and velocity. Aims. Because of the nature of their purpose, that is, adaptive wave-front correction, any kind of optimality relies on the control of a subset of principal wave-front components or eigenmodes, for short, a basis of functions in a mathematical sense. Here we provide algorithmic procedures for generating such eigenbases, also called Karhunen–Loève (KL) modes, that integrate force limitations in their definitions whilst maintaining standard orthonormality, statistical independence, and deformable mirror span. Methods. The double-diagonalisation method was revisited to build KL modes ranked by the force applied on the actuators. Results. We analysed this new KL basis for von Kármán turbulence statistics and present the fitting error and the distribution of positions and forces. We further illustrate their use in the case of the quaternary mirror control for the European Extremely Large Telescope, and we include the outer actuator minioning and force policy constraints. © The Authors 2024.