Missions

Project title : Multi-epoch recombination in high-contrast imaging techniques for exoplanet detection using HARMONI-ELT and future instruments.

High-contrast imaging surveys performed in recent years (SHINE, GPIES) have revealed that formation mechanisms are not very efficient in producing giant planets with large separation (larger than 10 AU). Upcoming high contrast imaging instruments, including SPHERE+ on the VLT and HARMONI on the ELT, aim to probe the population of giant planets at separations smaller than 10 AU. Despite the anticipated improvements in contrast levels, detecting these planets (contrast higher than 10^6 at 50-100 mas) remains a challenge.
To enhance the detection capabilities of these next-generation instruments, we have developed K-Stacker (Le Coroller, et al., 2015; Nowak et al., 2018; Le Coroller, et al., 2022), an algorithm (https://github.com/kstacker/) that allows to search for hidden planets (e.g. S/N smaller than 2) in image series using a brute-force exploration of the possible orbital parameters. The aim of this project is to assess the performance of K-Stacker in detecting young Jupiter like planets in images acquired by the HARMONI-ELT instrument.
Once K-Stacker optimized, you will be able to re-run the algorithm on archival data (e.g., Survey SPHERE SHINE) to search for new planets in the multi-epoch observations (e.g., HD 95086 c, etc.). The K-Stacker code will allow the search for planets like those in our solar system, using future ELT instruments such as PCS, as well as space-based observatories like the Roman Space Telescope and Habitable Worlds Observatory.

Activités

The project will include the automation of tests with extensive fake planet injections in simulated HARMONI images, aiming not only to determine detection statistics (true/false positive and negative rates) but also to evaluate the accuracy of the recovered orbital parameters. For planets detectable in each epoch, we will compare the orbital parameters found by K-Stacker against those derived from traditional MCMC methods on the positions of the planets. You will explore several potential enhancements to K-Stacker, such as integrating MCMC calculations (e.g., using emcee python module) for robust orbital parameter determination. The candidate will also use K-Stacker to evaluate the best observing strategy, i.e., to find the optimal splitting of observations (e.g., number of observing epochs, spacing between each pose) to maximize the probability of detection of new planets in a minimum total exposure time with HARMONI. This work paves the way for the development of new algorithms required to detect planets like those in our solar system through reflected light: mature Jupiter with the Roman Space Telescope, and Earth-like exoplanets with NASA's Habitable Worlds Observatory.

Compétences

The applicant should have a PhD in astrophysics or an equivalent qualification (e.g., in signal processing and machine learning). Skills in signal processing, statistics (e.g., MCMC calculations), and machine learning will be valued. Knowledge of Fourier Optics will be useful. Proficiency in a programming language (e.g., Python, C++) is required.

Contexte de travail

The successful candidate will join the “Groupe Systèmes Planétaires” (GSP team) at LAM, which is involved in major international high-contrast imaging projects. He will be supervised by H. Le Coroller, who is responsible for multi-epoch recombination algorithms (CO-I) for the NASA HWO project. Additionally, you will benefit from the support of the Machine Learning “Centre de données Astrophysiques de Marseille” (CeSAM). The Laboratoire d'Astrophysique de Marseille (LAM) will provide access to a computing cluster managed by CeSAM, along with technical support for intensive computational tasks.