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Your search keyword '"Dionne Haynes"' showing total 7 results
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7 results on '"Dionne Haynes"'

2. ULTIMATE-Subaru: GLAO preliminary design overview

Author

Yosuke Minowa, Yoshito Ono, Yoko Tanaka, Hiroshige Yoshida, Koki Terao, Yusei Koyama, Sadman Ali, Ichi Tanaka, Takashi Hattori, Hirofumi Okita, Yutaka Hayano, Shin Oya, Kentaro Motohara, Kenshi Yanagisawa, Michitoshi Yoshida, Masayuki Akiyama, Tadayuki Kodama, Hajime Ogane, Masahiro Konishi, Noelia Martínez Rey, Nicholas Herrald, Céline d'Orgeville, François Rigaut, Israel Vaughn, David Chandler, Dionne Haynes, Warrick Schofield, Shiang-Yu Wang, Chi-Yi Chou, and Masahiko Kimura

Published
2022

6. MAVIS: imager and spectrograph

Author

Simon . Ellis, Anthony Horton, Christian Schwab, Timothy Chin, Robert Content, Nuwanthika Fernando, Nuria Lorente, Richard McDermid, Mahesh Mohanan, Helen McGregor, Jacob Pember, David Robertson, Scott Smedley, Will Saunders, Lew Waller, Ross Zhelem, Jessica Zheng, Johan Kosmalski, Giovanni Cresci, Trevor Mendel, Andrea Bianco, David Brodrick, Jennifer Burgess, Dionne Haynes, Davide Greggio, François Rigaut, and Alessio Zanutta

Published
2022

7. The MAVIS Image Simulator: predicting the astrometric performance of MAVIS

Author

Giuseppe Bono, Dionne Haynes, Mojtaba Taheri, Daniele Vassallo, Giuliana Fiorentino, Jesse Cranney, Stephanie Monty, Guido Agapito, Cedric Plantet, Richard M. McDermid, Christian Schwab, J. Trevor Mendel, Davide Greggio, and Francois Rigaut

Subjects
Photometry (optics), Point spread function, Computer science, Globular cluster, High spatial resolution, Astrometry, High order, Residual, Simulation
Abstract

"We present initial results from the Multi-conjugate Adaptive-optics Visible Imager-Spectrograph Image Simulator (MAVISIM) to explore the astrometric capabilities of the next generation instrument MAVIS. A core scientific and operational requirement of MAVIS will be to achieve highly accurate differential astrometry, with accuracies on the order that of the extremely large telescopes. To better understand the impact of known and anticipated astrometric error terms, we have created an initial astrometric budget which we present here to motivate the creation of MAVISIM. In this first version of MAVISIM we include three major astrometric error sources; point spread function (PSF) field variability due to high order aberrations, PSF degradation and field variability due to tip-tilt residual error, and field distortions due to non-common path aberrations in the AO module. An overview of MAVISIM is provided along with initial results from a study using MAVISIM to simulate an image of a Milky Way-like globular cluster. Astrometric accuracies are extracted using PSF-fitting photometry with encouraging results that suggest MAVIS will deliver accuracies of 150µas down to faint magnitudes."

Published
2020

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