LBT SOUL data as a science test bench for MICADO PSF-R tool

TL;DR

This paper evaluates a PSF reconstruction algorithm for adaptive optics systems using real LBT SOUL data, demonstrating high accuracy in reconstructing PSF parameters crucial for next-generation telescopes like the ELT.

Contribution

It presents the first application of a blind PSF-R algorithm to real AO data, validating its performance and readiness for use in MICADO on the ELT.

Findings

Reconstructed SR and FWHM with errors below 2% and 4.5%.

Algorithm successfully tested on actual LBT SOUL data.

Demonstrates potential for accurate PSF characterization in future AO instruments.

Abstract

Current state-of-the-art adaptive optics (AO) provides ground-based, diffraction-limited observations with high Strehl ratios (SR). However, a detailed knowledge of the point spread function (PSF) is required to fully exploit the scientific potential of these data. This is even more crucial for the next generation AO instruments that will equip 30-meter class telescopes, as the characterization of the PSF will be mandatory to fulfill the planned scientific requirements. For this reason, there is a growing interest in developing tools that accurately reconstruct the observed PSF of AO systems, the so-called PSF reconstruction. In this context, a PSF-R service is a planned deliverable for the MICADO@ELT instrument and our group is in charge of its development. In the case of MICADO, a blind PSF-R approach is being pursued to have the widest applicability to science cases. This means that the PSF is reconstructed without extracting information from the science data, relying only on telemetry and calibrations. While our PSF-R algorithm is currently being developed, its implementation is mature enough to test performances with actual observations. In this presentation we will discuss the reliability of our reconstructed PSFs and the uncertainties introduced in the measurements of scientific quantities for bright, on-axis observations taken with the SOUL+LUCI instrument of the LBT. This is the first application of our algorithm to real data. It demonstrates its readiness level and paves the way to further testing. Our PSF-R algorithm is able to reconstruct the SR and full-width at half maximum of the observed PSFs with errors smaller than 2% and 4.5%, respectively. We carried out the scientific evaluation of the obtained reconstructed PSFs thanks to a dedicated set of simulated observations of an ideal science case.