The next generation of Adaptive Optics (AO) systems on large telescopes will require immense computation performance and memory bandwidth, both of which are challenging with the technology available today. The objective of this work is to create a future-proof adaptive optics platform on an FPGA architecture, which scales with the number of subapertures, pixels per subaperture and external memory. We have created a scalable adaptive optics platform with an off-the-shelf FPGA development board, which provides an AO reconstruction time only limited by the external memory bandwidth. SPARC uses the same logic resources irrespective of the number of subapertures in the AO system. This paper is aimed at embedded developers who are interested in the FPGA design and the accompanying hardware interfaces. The central theme of this paper is to show how scalability is incorporated at different levels of the FPGA implementation. This work is a continuation of Part 1 of the paper which explains the concept, objectives, control scheme and method of validation used for testing the platform.

We demonstrate a novel architecture for Adaptive Optics (AO) control based on FPGAs (Field Programmable Gate Arrays), making active use of their configurable parallel processing capability. SPARC's unique capabilities are demonstrated through an implementation on an off-the-shelf inexpensive Xilinx VC-709 development board. The architecture makes SPARC a generic and powerful Real-time Control (RTC) kernel for a broad spectrum of AO scenarios. SPARC is scalable across different numbers of subapertures and pixels per subaperture. The overall concept, objectives, architecture, validation and results from simulation as well as hardware tests are presented here. For Shack-Hartmann wavefront sensors, the total AO reconstruction time ranges from a median of 39.4µs (11 × 11 subapertures) to 1.283 ms (50 × 50 subapertures) on the development board. For large wavefront sensors, the latency is dominated by access time (∼1 ms) of the standard DDR memory available on the board. This paper is divided into two parts. Part 1 is targeted at astronomers interested in the capability of the current hardware. Part 2 explains the FPGA implementation of the wavefront processing unit, the reconstruction algorithm and the hardware interfaces of the platform. Part 2 mainly targets the embedded developers interested in the hardware implementation of SPARC.

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We present the status and plans for the Keck All sky Precision Adaptive optics (KAPA) program. KAPA includes four key science programs, an upgrade to the Keck I laser guide star (LGS) adaptive optics (AO) facility to improve image quality and sky coverage, AO telemetry based point spread function (PSF) estimates for all science exposures, and an educational component focused on broadening the participation of women and underrepresented groups in instrumentation. For the purpose of this conference we will focus on the AO facility upgrade which includes implementation of a new laser, wavefront sensor and real-time controller to support laser tomography, the laser tomography system itself, and modifications to an existing near-infrared tip-tilt sensor to support multiple natural guide star (NGS) and focus measurements.

Accurately simulating the atmospheric turbulence behaviour is always challenging. The well-known FFT based method falls short in correctly predicting both the low and high frequency behaviours. Sub-harmonic compensation aids in low-frequency correction but does not solve the problem for all screen size to outer scale parameter ratios (G/L 0 ). FFT-based simulation gives accurate result only for relatively large screen size to outer scale parameter ratio (G/L 0 ). In this work, we have introduced a Gaussian phase autocorrelation matrix to compensate for any sort of residual errors after applying for a modified subharmonics compensation. With this, we have solved problems such as under sampling at the high-frequency range, unequal sampling/weights for subharmonics addition at low-frequency range and the patch normalization factor. Our approach reduces the maximum error in phase structure-function in the simulation with respect to theoretical prediction to within 1.8%, G/L 0 = 1/1000.