| --- |
| license: apache-2.0 |
| pipeline_tag: tabular-classification |
| tags: |
| - Physics |
| - Stellar Classification |
| datasets: |
| - Allanatrix/Astro |
| --- |
| # NexaAstro: Stellar Classification with SDSS Data |
|
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| **NexaAstro - Stellar Classification** is a hybrid machine learning model for classifying stars using data from the Sloan Digital Sky Survey (SDSS). It leverages a two-stage architecture: |
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| - **CatBoost**: Gradient boosting for robust feature modeling. |
| - **Feedforward Neural Network (NN)**: Refines predictions for enhanced accuracy. |
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| This model is part of the [Nexa Scientific Model Suite](https://huggingface.co/spaces/Allanatrix/NexaHub), dedicated to advancing scientific discovery through machine learning. |
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| --- |
|
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| ## Model Overview |
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| - **Task**: Multi-class stellar classification (e.g., Main Sequence, White Dwarf, Giant). |
| - **Input**: SDSS stellar attributes (u, g, r, i, z magnitudes, spectral lines, etc.). |
| - **Output**: Predicted stellar class label. |
| - **Architecture**: CatBoost for feature extraction, followed by a Feedforward Neural Network for classification. |
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| --- |
|
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| ## Applications |
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| - **Stellar Population Studies**: Analyzing distributions and characteristics of stellar types. |
| - **Galaxy Classification Support**: Providing stellar data for broader galactic studies. |
| - **Astrophysics Education**: Enabling interactive learning and research tools. |
| - **Feature Engineering**: Supporting advanced astronomical machine learning workflows. |
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| --- |
|
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| ## Getting Started |
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| ### Example Usage |
| ```python |
| import joblib |
| import torch |
| import numpy as np |
| from my_nn_model import StellarNN # Replace with actual neural network module |
| |
| # Load CatBoost model |
| catboost_model = joblib.load("Allanatrix/catboost_model.pkl") |
| |
| # Load PyTorch neural network model |
| nn_model = StellarNN() |
| nn_model.load_state_dict(torch.load("Allanatrix/stellar_nn.pt")) |
| nn_model.eval() |
| |
| # Example prediction with SDSS features |
| features = np.array([...]) # SDSS input features (e.g., magnitudes, spectral data) |
| catboost_out = catboost_model.predict(features) |
| refined_pred = nn_model(torch.tensor(catboost_out).float()) |
| ``` |
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|
| Refer to the model documentation for detailed preprocessing and input requirements. |
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| --- |
|
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| ## Dataset |
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| - **Source**: [Sloan Digital Sky Survey (SDSS)](https://www.sdss.org/). |
| - **Preprocessing**: Data cleaned, normalized, and filtered by magnitude thresholds. |
| - **Labels**: Discrete stellar class labels derived from expert annotations. |
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| --- |
|
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| ## Citation and License |
|
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| If you use NexaAstro in your research, please cite this repository and acknowledge the SDSS dataset. |
| The model and associated code are licensed under the **Boost Software License 1.1 (BSL-1.1)**. |
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| --- |
|
|
| ## Part of the Nexa Scientific Ecosystem |
|
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| Explore related tools and models in the Nexa ecosystem: |
| - [Nexa Data Studio](https://huggingface.co/spaces/Allanatrix/NexaDataStudio): Tools for data processing and visualization. |
| - [Nexa R&D](https://huggingface.co/spaces/Allanatrix/NexaR&D): Research-focused model development environment. |
| - [Nexa Infrastructure](https://huggingface.co/spaces/Allanatrix/NexaInfrastructure): Scalable ML deployment solutions. |
| - [Nexa Hub](https://huggingface.co/spaces/Allanatrix/NexaHub): Central portal for Nexa resources. |
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|
| *Coming Soon:* |
| - Galaxy Morphology Classifier |
| - Exoplanet Transit Detection Model |
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| --- |
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| *Developed and maintained by [Allan](https://huggingface.co/Allanatrix), an independent machine learning researcher specializing in astrophysical and scientific AI systems.* |
