Machine Learning for Exoplanets
- Characterization of exoplanet atmospheres from transmission spectroscopy
- Searching for novel chemicals and biosignatures in the atmospheres of exoplanets using machine learning techniques for outlier and novelty detection
- Population studies of exoplanets using machine learning
- Symbolic regression as a tool for deriving analytical expressions for radiative transfer models.
- Surrogate machine learning models as fast alternatives for Bayesian retrievals of exoplanet parameters
- Unsupervised learning for feature engineering and dimensionality reduction using large synthetic spectral databases
Machine Learning applications in physics
- Machine learning symmetries from first principles
- Discovering symmetries in the latent space
- Identifying the group theoretic structure of machine-learned symmetries
- Symbolic regression for discovering new physics laws from large databases
- Using machine learning for building particle physics models
Quantum Machine Learning
- Invariant and equivariant quantum graph neural networks
- Quantum transformers
- QUBO optimization problems