Description
The characterization of exoplanet atmospheres is crucial for understanding their compositions, weather patterns, and potential habitability. This project aims to develop machine learning models to analyze spectral data from exoplanets, identifying chemical signatures and atmospheric properties. The project will leverage data from telescopes and space missions, along with simulations of exoplanetary atmospheres under various conditions, to train and validate the models.
Duration
Total project length: 175/350 hours.
Task Ideas
- Develop simulations of exoplanetary atmospheres with diverse chemical compositions and physical conditions.
- Train machine learning models on simulated spectral data to recognize atmospheric characteristics.
- Apply the models to real observational data from missions like Hubble, JWST, and future telescopes to characterize exoplanet atmospheres.
- Explore the use of deep learning techniques for enhancing the models’ ability to identify subtle spectral signatures.
Expected Results
- A set of machine learning models capable of accurately characterizing exoplanet atmospheres.
- Analysis of the models’ performance on observational data, demonstrating their applicability to current and future exoplanet studies.
Requirements
- Python, PyTorch, C/Fortran
- Background in astronomy is a bonus but not a requirement
Test
Use this link for instructions on completing the test.
Mentors
Please DO NOT contact mentors directly by email. Instead, please email ml4-sci@cern.ch with Project Title and include your CV. The mentors will then get in touch with you.
Corresponding Project
Participating Organizations