🌌 SpecCLIP: Aligning and Translating Spectroscopic Measurements for Stars

SpecCLIP is a contrastive + domain-preserving foundation model designed to align LAMOST LRS spectra with Gaia XP spectrophotometric data. It learns a general-purpose spectral embedding (768-dim) that supports:

• Stellar parameter estimation
• Cross-survey spectral translation (LAMOST LRS ⟷ Gaia XP)
• Similarity retrieval across LAMOST LRS and GAIA XP spectra

For full documentation, installation instructions, examples, and end-to-end usage, please visit the GitHub repository: 👉 https://github.com/Xiaosheng-Zhao/SpecCLIP

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🔧 Available Models

The following pretrained weights are included in this model repository:

File	Description	Embedding Dim	Param
encoders/lrs_encoder.ckpt	LAMOST LRS masked transformer encoder	768	43M
encoders/xp_encoder.ckpt	Gaia XP masked transformer encoder	768	43M
encoders/xp_encoder_mlp.ckpt	Gaia XP autoencoder (MLP head)	768	43M
specclip/specclip_model_base.ckpt	Gaia XP ⟷ LAMOST contrastive	768	100M
specclip/specclip_model_predrecon_mlp.ckpt	CLIP alignment + pred+recon	768	168M
specclip/specclip_model_split_mlp.ckpt	CLIP alignment + split pred/recon	768	126M

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🧠 What the Model Does

SpecCLIP consists of:

• Two masked transformer encoders – LAMOST LRS – Gaia XP
• Contrastive alignment loss (CLIP-style)
• Domain-preserving prediction & reconstruction heads
• Cross-modal decoder for spectrum translation

It produces shared embeddings enabling multi-survey astrophysical analysis.

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📄 Full Documentation

To keep the Hugging Face card concise, all detailed instructions, including:

• Installation
• Parameter prediction
• Spectral translation
• Retrieval
• Full examples (Python + figures)
• Acknowledgments

are available at the GitHub repo:

👉 https://github.com/Xiaosheng-Zhao/SpecCLIP

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📊 Citation

@ARTICLE{2025arXiv250701939Z,
       author = {{Zhao}, Xiaosheng and {Huang}, Yang and {Xue}, Guirong and {Kong}, Xiao and
                 {Liu}, Jifeng and {Tang}, Xiaoyu and {Beers}, Timothy C. and
                 {Ting}, Yuan-Sen and {Luo}, A-Li},
        title = "{SpecCLIP: Aligning and Translating Spectroscopic Measurements for Stars}",
      journal = {arXiv e-prints},
     keywords = {Instrumentation and Methods for Astrophysics, Solar and Stellar Astrophysics,
                 Artificial Intelligence, Machine Learning},
         year = 2025,
        month = jul,
          eid = {arXiv:2507.01939},
        pages = {arXiv:2507.01939},
          doi = {10.48550/arXiv.2507.01939},
archivePrefix = {arXiv},
       eprint = {2507.01939},
 primaryClass = {astro-ph.IM},
}

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📬 Contact

• GitHub Issues: https://github.com/Xiaosheng-Zhao/SpecCLIP/issues
• Email: xzhao113@jh.edu