Add comprehensive README with project overview and structure

README.md

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-# EXXA
+# EXXA — Exoplanet Exploration with Machine Learning
+
+EXXA is a collection of machine learning projects for exoplanet science, developed under [ML4SCI](https://ml4sci.org/) as part of [Google Summer of Code](https://summerofcode.withgoogle.com/).
+
+The repository hosts contributions that apply deep learning, quantum machine learning, and computer vision techniques to problems in exoplanet atmosphere characterization, protoplanetary disk denoising, planetary system architecture prediction, and more.
+
+## Project Structure
+
+| Directory | Description |
+|-----------|-------------|
+| [`ANOMALY_DETECTION/`](ANOMALY_DETECTION/) | Anomaly detection in exoplanet atmospheric spectra |
+| [`ATMOSPHERE_CHARACTERIZATION/`](ATMOSPHERE_CHARACTERIZATION/) | ML models for identifying chemical species and atmospheric properties from observed spectra |
+| [`DENOISING_DIFFUSION/`](DENOISING_DIFFUSION/) | Denoising astronomical observations of protoplanetary disks |
+| [`DUST_CONTINUUM_APPROCH/`](DUST_CONTINUUM_APPROCH/) | Dust continuum analysis approaches for disk characterization |
+| [`EQUIVARIANT_NETWORKS_PLANETARY_SYSTEMS_ARCHITECTURES/`](EQUIVARIANT_NETWORKS_PLANETARY_SYSTEMS_ARCHITECTURES/) | Equivariant vision networks for predicting planetary system architectures |
+| [`FOUNDATION_MODELS_FOR_EXOPLANET_CHARACTERIZATION/`](FOUNDATION_MODELS_FOR_EXOPLANET_CHARACTERIZATION/) | Foundation models for exoplanet detection and analysis |
+| [`KINEMATIC_APPROACH/`](KINEMATIC_APPROACH/) | Kinematic approaches to exoplanet characterization |
+| [`NEURAL_NETWORK_CLASSIFIER/`](NEURAL_NETWORK_CLASSIFIER/) | Neural network classifiers for exoplanet data |
+| [`QUANTUM_MACHINE_LEARNING_FOR_EXOPLANET_CHARACTERIZATION/`](QUANTUM_MACHINE_LEARNING_FOR_EXOPLANET_CHARACTERIZATION/) | Quantum ML techniques for exoplanet atmosphere classification |
+| [`TIME_SERIES_APPROACH/`](TIME_SERIES_APPROACH/) | Time series methods for exoplanet signal analysis |
+
+Each subdirectory corresponds to a GSoC project and contains its own README with detailed instructions.
+
+## Getting Started
+
+1. Clone the repository:
+   ```bash
+   git clone https://github.com/ML4SCI/EXXA.git
+   cd EXXA
+   ```
+
+2. Navigate to the project of interest and follow its README for setup and usage instructions. Most projects use Python with PyTorch or TensorFlow and provide Jupyter notebooks for experimentation.
+
+## Related GSoC 2026 Projects
+
+- Equivariant Vision Networks for Predicting Planetary Systems' Architectures
+- Denoising Astronomical Observations of Protoplanetary Disks
+- Exoplanet Atmosphere Characterization
+- Foundation Models for Exoplanet Characterization
+- Quantum Machine Learning for Exoplanet Characterization
+
+For more details, see the [ML4SCI GSoC 2026 project list](https://ml4sci.org/gsoc/2026/summary.html).
+
+## Contributing
+
+Contributions are welcome! If you would like to contribute, please:
+
+1. Fork this repository
+2. Create a feature branch (`git checkout -b my-feature`)
+3. Commit your changes and push to your fork
+4. Open a pull request against the `main` branch
+
+For questions or discussions, reach out via the [ML4SCI Gitter channel](https://matrix.to/#/#ML4SCI_general:gitter.im) or email [ml4-sci@cern.ch](mailto:ml4-sci@cern.ch).
+
+## Acknowledgments
+
+This project is part of [Machine Learning for Science (ML4SCI)](https://ml4sci.org/), supported by Google Summer of Code. We thank all GSoC contributors and mentors who have made this work possible.