Paper 14145-191
OCEAN: a space telescope for Orbit Characterization of near-by Exoplanets by Astrometric determinatioN on a cubesat enviroment
7 July 2026 • 17:30 - 19:00 CEST | Room B4-M3
Abstract
The paper presents the current status and technological advancements of OCEAN (Orbit Characterization of near-by exoplanetary systems by Astrometric determinatioN) mission. The mission is conceived as a 36 U Cubesat, aiming at rapid implementation with a compact telescope designed for high stability and low sensitivity to perturbations, ensuring 1-μas systematic floor. The payload relies on a single-aperture, highly-stable space telescope enforcing circular symmetry on the whole optical system, down to the detector populating an annular region of uniform diffraction limited imaging. The payload is compact, light, and cheap. OCEAN represents a transformative step toward democratizing space-based based astrometry and exoplanet discovery. The mission combines scientific ambition with engineering pragmatism. It builds on existing CubeSat heritage while pushing the limits of precision and stability, potentially unlocking a new class of affordable, targeted astronomical observatories.
Presenter
INAF - Osservatorio Astrofisico di Torino (Italy)
Deborah is senior researcher at INAF-Obsservatorio astrofisico di Torino. Her scientific-technological research has been focused since her PhD, on modeling astrometric payloads, developing monitoring and diagnostic procedures for remote instrumentation, developing and implementing astrometric signal calibration procedures; adapting calibration algorithms to Infrastructure Requirements in Operational Pipelines.
Almost 20 years of involvement in the Gaia mission as second level Work Package Manager, Gaia Payload Expert and DPCT Science Manager. Participation to the Euclid Consortium since September 2020 for validation activities related to the NIR Processing Function and support for the modeling of the NIR PSF.
Coordinator of the research project related to activities concerning the design of new and efficient paradigms for the management, manipulation, processing, and analysis of Big Data. The work also encompasses managerial and group leadership responsibilities.