Note
This project is a collaboration with EXXA and QMLHEP.
Description
The characterization of exoplanet atmospheres is crucial for understanding their compositions, weather patterns, and potential habitability. This project aims to develop quantum machine learning models to analyze spectral data from exoplanets, identifying chemical abundances, cloud/haze structure and different atmospheric processes. 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
- Identify suitable latent representations of the exoplanet transmission data.
- Develop a quantum machine learning architecture for detecting anomalous exoplanets based on synthetic transmission spectra.
- Develop a quantum generative model for simulating exoplanet transmission spectra.
- Apply the trained models to real observational data from missions like Hubble, JWST, and future telescopes to characterize exoplanet atmospheres.
- Benchmark the performance of the developed quantum machine learning models against their classical counterparts.
Expected Results
- A set of quantum machine learning models capable of accurately modeling exoplanet atmospheres or flagging anomalous spectra.
- Analysis of the models’ performance on observational data, demonstrating their applicability to current and future exoplanet studies.
Requirements
- Python
- PyTorch or TensorFlow (or similar)
- Some experience with Qiskit or Pennylane is preferred
- 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