arXiv subjects

Louis Barbier

Publications and source records attributed to Louis Barbier.

3 recordsLinked to original sources

arXiv2026

MORFEO: Advancing Towards Final Design

The Multiconjugate adaptive Optics Relay For ELT Observations (MORFEO) is a first-generation adaptive optics module for the Extremely Large Telescope (ELT), designed to deliver a diffraction-limited, highly uniform 53x53 arcsec field of view to the MICADO near-infrared camera. As the project advances toward its Final Design Review (FDR), significant consolidations have been achieved across all subsystems. This paper presents an updated overview of the MORFEO system, highlighting its dual operational modes (MCAO and SCAO) and recent developments in its opto-mechanical architecture. We dedicate specific focus to the core adaptive hardware, detailing the fifth-generation post-focal deformable mirrors, the highly complex Laser Guide Star (LGS) objective zoom system required to track sodium layer variations, and the Natural Guide Star (NGS) low-order and reference sensing strategies. Furthermore, we detail the advanced pseudo-open-loop control strategy managed by a split Hard and Soft Real-Time Computer architecture. Finally, we report the latest end-to-end performance estimations obtained via the SPECULA simulation framework, demonstrating compliance with the stringent Strehl Ratio and sky coverage requirements under median atmospheric conditions.

astro-ph.IM↗

arXiv2024

Determining Research Priorities Using Machine Learning

We summarize our exploratory investigation into whether Machine Learning (ML) techniques applied to publicly available professional text can substantially augment strategic planning for astronomy. We find that an approach based on Latent Dirichlet Allocation (LDA) using content drawn from astronomy journal papers can be used to infer high-priority research areas. While the LDA models are challenging to interpret, we find that they may be strongly associated with meaningful keywords and scientific papers which allow for human interpretation of the topic models. Significant correlation is found between the results of applying these models to the previous decade of astronomical research ("1998-2010" corpus) and the contents of the science frontier panel report which contains high-priority research areas identified by the 2010 National Academies' Astronomy and Astrophysics Decadal Survey ("DS2010" corpus). Significant correlations also exist between model results of the 1998-2010 corpus and the submitted whitepapers to the Decadal Survey ("whitepapers" corpus). Importantly, we derive predictive metrics based on these results which can provide leading indicators of which content modeled by the topic models will become highly cited in the future. Using these identified metrics and the associations between papers and topic models it is possible to identify important papers for planners to consider. A preliminary version of our work was presented by Thronson etal. 2021 and Thomas etal. 2022.

cs.DL↗

arXiv2022

Determining Research Priorities for Astronomy Using Machine Learning

We summarize the first exploratory investigation into whether Machine Learning techniques can augment science strategic planning. We find that an approach based on Latent Dirichlet Allocation using abstracts drawn from high impact astronomy journals may provide a leading indicator of future interest in a research topic. We show two topic metrics that correlate well with the high-priority research areas identified by the 2010 National Academies' Astronomy and Astrophysics Decadal Survey science frontier panels. One metric is based on a sum of the fractional contribution to each topic by all scientific papers ("counts") while the other is the Compound Annual Growth Rate of these counts. These same metrics also show the same degree of correlation with the whitepapers submitted to the same Decadal Survey. Our results suggest that the Decadal Survey may under-emphasize fast growing research. A preliminary version of our work was presented by Thronson et al. 2021.

astro-ph.IM↗