Simon Carrier | Searcharxiv

arXiv2026

96 kHz on-sky imaging on an adaptive optics system with a single-photon avalanche diode

Astronomical observations requiring extremely high angular resolution necessitate advanced adaptive optics (AO) systems to overcome blurring caused by the atmosphere. In addition to obtaining much sharper point-spread functions (PSFs) with these systems, it is beneficial to be able to characterize the behaviour of the resulting PSF across time and wavelength. We have installed a commercially-available Single-photon avalanche diode (SPAD) array on the focal plane of the REVOLT AO testbench at the Dominion Astrophysical Observatory, allowing us to observe the visible PSF of the system at rates up to 96 kHz. This provides a time-resolved view of the PSF at $\sim$100 times the frequency of the AO system itself. We use this detector to analyze the high-frequency behaviour of the PSF of the AO system, including residual tip/tilt and deformable mirror response. We also explore the performance of AO-assisted lucky imaging, in which we average together only the frames which result in the best image quality. We find that high-framerate imaging can significantly improve the PSF beyond the native capabilities of the AO system.

astro-ph.IM↗

arXiv2018

CREATE: Multimodal Dataset for Unsupervised Learning, Generative Modeling and Prediction of Sensory Data from a Mobile Robot in Indoor Environments

The CREATE database is composed of 14 hours of multimodal recordings from a mobile robotic platform based on the iRobot Create. The various sensors cover vision, audition, motors and proprioception. The dataset has been designed in the context of a mobile robot that can learn multimodal representations of its environment, thanks to its ability to navigate the environment. This ability can also be used to learn the dependencies and relationships between the different modalities of the robot (e.g. vision, audition), as they reflect both the external environment and the internal state of the robot. The provided multimodal dataset is expected to have multiple usages, such as multimodal unsupervised object learning, multimodal prediction and egomotion/causality detection.

cs.RO↗