On-Sky Adaptive Secondary Interaction Matrix Calibration on the MMT

AO4ELT 2023
Robin Swanson1, 2 (robin@cs.toronto.edu), Jacob Taylor1, 3, Manny Montoya 4, Suresh Sivanandam 1, 3, Amali Vaz 4, Dan Vargas 4, Grant West 4, Andrew Gardner 4, Jess Johnson 4, Olivier Durney 4, Jenny Patience 5, Masen Lamb 3, 6, Parker Levesque 1, 3
(1) Dunlap Institute for Astronomy and Astrophysics (2) Department of Computer Science, University of Toronto (3) David A. Dunlap Department of Astronomy & Astrophysics, University of Toronto (4) Steward Observatory (5) ASU School of Earth and Space Exploration (6) Gemini Observatory, Southern Operations Center

Abstract

With the commissioning of the refurbished adaptive secondary mirror (ASM) and (visible and infrared) pyramid wavefront sensors (WFS) for the 6.5-meter MMT Observatory under way, special consideration had to be made to properly calibrate the mirror response functions to generate an interaction matrix (IM). Like many upcoming extremely large aperture telescopes (ELTs), the MMT lacks a point in the optical path to place a calibration source to accurately sample the ASM's actuator response functions. We show how the DO-CRIME and Sprint algorithms were successfully implemented at the MMT to extract an IM from on sky data and match them to a mis-registration accurate synthetic IM. We also present improvements to their base algorithms, greatly improving robustness to noise as well as errant actuators. Our ultimate goal is to provide a 100 mode pseudo-synthetic calibration with under 10 minutes of on-sky time.These methods have been validated both on an optical bench AO system as well as preliminary on-sky results from the MAPS (MMTO Adaptive optics exoPlanet characterization System) project on the MMT.

Comparison of the recovered modes from MMT on-sky data first with DO-CRIME (coloured in red) and simulation output after SPRINT (black & white)

Recovered poke interaction matrix from on-sky MAPS data using DO-CRIME. On the left shows the X and Y slopes recovered using a high pass filter, on the right shows the same slopes from the same data using the optimal Crime-Wave filter.

Poster

BibTeX

TBD