From ced36a38b95f9612ce5250de6b441bd57907640b Mon Sep 17 00:00:00 2001
From: Viren Pandey <128834400+viren-pandey@users.noreply.github.com>
Date: Tue, 17 Mar 2026 01:32:14 +0530
Subject: [PATCH] Enhance README with project details and guidelines

Added detailed overview, repository structure, requirements, usage instructions, goals, contributing guidelines, and license information to README.md.
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 1 file changed, 68 insertions(+)

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 # EXXA
+
+## Overview
+
+EXXA is a collection of machine learning approaches for problems related to exoplanet discovery, characterization, and astrophysical signal analysis. The repository contains multiple independent research directions exploring how modern ML techniques can be applied to astronomical datasets.
+
+Each directory focuses on a different scientific or modeling approach, ranging from classical neural networks to diffusion models and quantum machine learning.
+
+## Repository Structure
+
+| Directory                                               | Description                                                                                        |
+| ------------------------------------------------------- | -------------------------------------------------------------------------------------------------- |
+| ANOMALY_DETECTION                                       | Detects unusual patterns in astronomical observations that may indicate rare astrophysical events. |
+| ATMOSPHERE_CHARACTERIZATION                             | Uses machine learning to infer atmospheric properties of exoplanets from observational data.       |
+| DENOISING_DIFFUSION                                     | Diffusion-based models for reconstructing or generating astrophysical signals.                     |
+| DUST_CONTINUUM_APPROCH                                  | Models dust continuum emissions in astrophysical systems.                                          |
+| EQUIVARIANT_NETWORKS_PLANETARY_SYSTEMS_ARCHITECTURES    | Uses equivariant neural networks to model planetary system structures.                             |
+| FOUNDATION_MODELS_FOR_EXOPLANET_CHARACTERIZATION        | Experiments with foundation models for large-scale exoplanet analysis.                             |
+| KINEMATIC_APPROACH                                      | Uses kinematic methods to analyze motion and dynamics in astrophysical datasets.                   |
+| NEURAL_NETWORK_CLASSIFIER                               | Standard neural network classifiers for astronomical signal classification tasks.                  |
+| QUANTUM_MACHINE_LEARNING_FOR_EXOPLANET_CHARACTERIZATION | Explores quantum machine learning techniques for exoplanet analysis.                               |
+| TIME_SERIES_APPROACH                                    | Time-series modeling of astronomical observations such as light curves.                            |
+
+## Requirements
+
+Typical dependencies include
+
+Python 3.8+
+NumPy
+SciPy
+PyTorch or TensorFlow
+Matplotlib
+AstroPy
+Jupyter Notebook
+
+Install dependencies using
+
+pip install -r requirements.txt
+
+If a directory has its own environment or dependency file, follow the instructions within that module.
+
+## Usage
+
+Most modules are implemented as research notebooks and scripts. Navigate to the relevant directory and run the corresponding notebook.
+
+Example
+
+cd TIME_SERIES_APPROACH
+jupyter notebook
+
+Each module contains experiments, models, and evaluation workflows specific to the problem it addresses.
+
+## Goals
+
+The project explores how machine learning can assist in
+
+exoplanet detection
+atmospheric parameter estimation
+astronomical signal denoising
+rare event detection
+physical system modeling
+
+## Contributing
+
+Contributions are welcome. If you want to extend an approach or add a new ML method for astrophysical analysis, open a pull request with a clear description of the contribution.
+
+## License
+
+Specify the license for this repository here.