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Main Authors: Taheri, Mojtaba, Cranney, Jesse, Marasco, Antonino, Monty, Stephanie, Massari, Davide, Agapito, Guido, Cresci, Giovanni, McDermid, Richard M., Rigaut, Francois, Neichel, Benoit, Brodrick, David, Plantet, Cédric
Format: Preprint
Published: 2024
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Online Access:https://arxiv.org/abs/2410.12106
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author Taheri, Mojtaba
Cranney, Jesse
Marasco, Antonino
Monty, Stephanie
Massari, Davide
Agapito, Guido
Cresci, Giovanni
McDermid, Richard M.
Rigaut, Francois
Neichel, Benoit
Brodrick, David
Plantet, Cédric
author_facet Taheri, Mojtaba
Cranney, Jesse
Marasco, Antonino
Monty, Stephanie
Massari, Davide
Agapito, Guido
Cresci, Giovanni
McDermid, Richard M.
Rigaut, Francois
Neichel, Benoit
Brodrick, David
Plantet, Cédric
contents MAVIS (the MCAO-Assisted Visible Imager and Spectrograph), planned for the VLT Adaptive Optics Facility, represents an innovative step in Multi-Conjugate Adaptive Optics (MCAO) systems, particularly in its operation at visible wavelengths and anticipated contributions to the field of astronomical astrometry. Recognizing the crucial role of high-precision astrometry in realizing science goals such as studying the dynamics of dense starfields, this study focuses on the challenges of advancing astrometry with MAVIS to its limits, as well as paving the way for further enhancement by incorporating telemetry data as part of the astrometric analysis. We employ MAVISIM, Superstar, and DAOPHOT to simulate both MAVIS imaging performance and provide a pathway to incorporate telemetry data for precise astrometry with MAVIS. Photometry analyses are conducted using the Superstar and DAOPHOT platforms, integrated into a specifically designed pipeline for astrometric analysis in MCAO settings. Combining these platforms, our research aims to elucidate the impact of utilizing telemetry data on improving astrometric precision, potentially establishing new methods for ground-based AO-assisted astrometric analysis. This endeavor not only sheds light on the capabilities of MAVIS but also paves the way for advancing astrometry in the era of next-generation MCAO-enabled giant telescopes.
format Preprint
id arxiv_https___arxiv_org_abs_2410_12106
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle MAVIS: Enabling High-Precision Ground-Based Astrometry in the Visible Spectrum
Taheri, Mojtaba
Cranney, Jesse
Marasco, Antonino
Monty, Stephanie
Massari, Davide
Agapito, Guido
Cresci, Giovanni
McDermid, Richard M.
Rigaut, Francois
Neichel, Benoit
Brodrick, David
Plantet, Cédric
Instrumentation and Methods for Astrophysics
MAVIS (the MCAO-Assisted Visible Imager and Spectrograph), planned for the VLT Adaptive Optics Facility, represents an innovative step in Multi-Conjugate Adaptive Optics (MCAO) systems, particularly in its operation at visible wavelengths and anticipated contributions to the field of astronomical astrometry. Recognizing the crucial role of high-precision astrometry in realizing science goals such as studying the dynamics of dense starfields, this study focuses on the challenges of advancing astrometry with MAVIS to its limits, as well as paving the way for further enhancement by incorporating telemetry data as part of the astrometric analysis. We employ MAVISIM, Superstar, and DAOPHOT to simulate both MAVIS imaging performance and provide a pathway to incorporate telemetry data for precise astrometry with MAVIS. Photometry analyses are conducted using the Superstar and DAOPHOT platforms, integrated into a specifically designed pipeline for astrometric analysis in MCAO settings. Combining these platforms, our research aims to elucidate the impact of utilizing telemetry data on improving astrometric precision, potentially establishing new methods for ground-based AO-assisted astrometric analysis. This endeavor not only sheds light on the capabilities of MAVIS but also paves the way for advancing astrometry in the era of next-generation MCAO-enabled giant telescopes.
title MAVIS: Enabling High-Precision Ground-Based Astrometry in the Visible Spectrum
topic Instrumentation and Methods for Astrophysics
url https://arxiv.org/abs/2410.12106