High-Angular Resolution Astronomy
Tomographic Adaptive Optics for ground-based giant telescopes: exploring the super resolution opportunity
European astronomy is about to take on one of the greatest instrumental challenges ever imagined: the construction of the ELT (Extremely Large Telescope), a telescope 39 m in diameter, expected to go into operation by 2030.
LAM and ONERA are developing together the adaptive optics that will equip the first light spectro-imager of the ELT, called HARMONI. HARMONI will have access to the whole sky with a very high spatial and spectral resolution and thus allow unique astrophysical advances concerning the understanding of the birth and the evolution of galaxies in the primordial universe. This double instrumental constraint (high resolution and access to the whole sky) led us to propose the concept of Laser Tomographic Adaptive Optics (LTAO).
The HARMONI LTAO system will make use if Artificial Stars generated by high power lasers (> 20 W output power) with a tomographic reconstruction of the whole turbulence volume located above the telescope. While the concept of tomographic AO itself has been proposed for several decades now (Beckers-1988, Ellerbreok-1994, Fusco-2001), the practical and operational implementation on 8m diameter astronomical telescopes is quite recent (Rigaut-Neichel 2018, Oberti-2018). Its application to a giant 39m diameter telescope poses new challenges related to the complexity of the telescope itself combined with the ever-increasing performance requirements imposed by the increasingly ambitious scientific objectives associated with these giants of modern astronomy.
The objective of the thesis is to propose alternative and innovative solutions to the key problems identified in the application of tomographic AO on giant telescopes. In particular, the student will study the contribution of the concept of super-resolved wavefront sensors recently proposed in AO (Fusco-2021, Oberti-2022) and its practical application in the framework of the LTAO. This understanding of the super-resolved wavefront measurement will pass through a theoretical analysis, numerical simulations and an experimental validation on a bench and on sky.
The proposed developments will naturally be used for HARMONI but can also be extended to future tomographic AO systems which will be operational in the next decade (MAVIS at VLT, KAPPA at Keck, GNAO at Gemini, MAORY and MOSAIC on the ELT).
Machine learning data processing for Adaptive Optics assisted astronomical observations
High-resolution images from large ground-based telescopes have revolutionized visible and near-infrared astronomy over a wide range of astrophysical fields, including finding and characterizing exoplanets, black holes, brown dwarfs, and the earliest galaxies in the Universe.
These discoveries relied on adaptive optics (AO) systems, which compensate in real-time for the blurring effects of the Earth’s turbulent atmosphere (called “seeing”). AO systems give superior spatial resolution over space-based alternatives at a fraction of the cost and have been deployed on nearly all of the world’s largest telescopes, as the European Very Large Telescope (VLT) and its 10m-class telescopes counterparts. The power of AO is now widely recognized and it will be built into the 1st-light instruments of ALL the next-generation giant telescopes — the European ELT, the Giant Magellan Telescope and the Thirty Meter Telescope with diameters up to 40m. The exceptional advancement in AO technology and observational capability has, however, not been followed by a comparable advancement in the development of data analysis methods. Additionally, the increase of the telescope size introduces new effects that can be barely characterized by models of current level of complexity. This results in a difficult understanding of the AO performance, and in particular in the characterization of the AO Point Spread Function (PSF), which eventually limits the scientific analyses. Candidates will therefore develop innovative research into data-analysis algorithms aimed to extract the most precise measurements of photometric brightness, astrometric position, and morphology for planets, stars, and galaxies from adaptive-optics assisted observations on current and future ground-based telescopes. A particular focus will be on the use and development of machine learning methods applied to astronomy. Indeed, AO systems are producing a tremendous amount of data – called telemetry data – with all the wave-front sensors engaged, which could be used to improve our understanding and prediction of the AO performance. All this data is not currently used, and this Ph.D. will explore how
Machine learning could be exploited for that. The innovative algorithms developed during this PhD will be tested and validated on a large set of data, already available from VLT, and on simulation of future ELT observations.
Space Adaptive Optics
The direct imaging of Exo-Earth planets is crucial for understanding planet formation, and finding biomarkers on exoplanets atmosphere. The huge flux ratio between such a planet and its host star, around several billions, as well as the proximity between them, makes this task however extremely challenging. It is out of reach today, and requires the development of a very large and stable space observatory (the US-lead space telescope LUVEX) coupled to future high-contrast science instruments. This last part is based on coronagraphy and allows to physically suppress the star light, to the benefit of the planet light. In the case of LUVEX, where a contrast of 1010 is required, the aberration should be as small as a few picometers. It constrains the whole observatory in term of extreme stability, wave-front sensing and control, down to this level.
In order to reach the extreme performance level, it is mandatory to close fast active loops, correcting for fast and small perturbation existing onboard a space platform. We call this change of paradigm the space adaptive optics. Adaptive optics have equipped ground-based astronomical telescopes for decades now. Based on the knowledge of AO on the ground, we want to deploy a similar approach to a space-based observatory, called space adaptive optics.
The candidate will work on the modelisation of expected perturbations, modelisation of AO measurement & correction, and develop a multi-sensor and multi-correction approach. Based on the JWST experience, and access to telescope phasing data through a close collaboration with STScI, the candidate will develop a simplified AO model for space. Validation with bench experiments (HiCAT bench at STScI) and potentially in-orbit demonstration with the AZIMOV project is also foreseen.”
Astronomy & Astrophysics, Instrumentation, Optics, Ground & space based Telescopes, Adaptive Optics, wave-front control