Towards virtual access of adaptive optics telemetry data

TL;DR

This paper introduces a Python package that standardizes access to adaptive optics telemetry data, enabling broader usability for various AO systems and supporting diverse research and operational needs.

Contribution

It presents an object-oriented Python tool that abstracts AO telemetry data into a standard model, facilitating data sharing and analysis across different systems.

Findings

Supports data from multiple AO systems including AOF and Keck.

Enables access to raw and processed AO telemetry data.

Facilitates research in PSF derivation, turbulence analysis, and system optimization.

Abstract

Large amounts of Adaptive-Optics (AO) control loop data and telemetry are currently inaccessible to end-users. Broadening access to those data has the potential to change the AO landscape on many fronts, addressing several use-cases such as derivation of the system's PSF, turbulence characterisation and optimisation of system control. We address one of the biggest obstacles to sharing these data: the lack of standardisation, which hinders access. We propose an object-oriented Python package for AO telemetry, whose data model abstracts the user from an underlining archive-ready data exchange standard based on the Flexible Image Transport System (FITS). Its design supports data from a wide range of existing and future AO systems, either in raw format or abstracted from actual instrument details. We exemplify its usage with data from active AO systems on 10m-class observatories, of which two are currently supported (AOF and Keck), with plans for more.