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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.01939 |
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| _version_ | 1866917157720293376 |
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| author | Zhao, Xiaosheng Huang, Yang Xue, Guirong Kong, Xiao Liu, Jifeng Tang, Xiaoyu Beers, Timothy C. Ting, Yuan-Sen Luo, A-Li |
| author_facet | Zhao, Xiaosheng Huang, Yang Xue, Guirong Kong, Xiao Liu, Jifeng Tang, Xiaoyu Beers, Timothy C. Ting, Yuan-Sen Luo, A-Li |
| contents | In recent years, large language models (LLMs) have transformed natural language understanding through vast datasets and large-scale parameterization. Inspired by this success, we present SpecCLIP, a foundation model framework that extends LLM-inspired methodologies to stellar spectral analysis. Stellar spectra, akin to structured language, encode rich physical and chemical information about stars. By training foundation models on large-scale spectral datasets, our goal is to learn robust and informative embeddings that support diverse downstream applications. As a proof of concept, SpecCLIP involves pre-training on two spectral types--LAMOST low-resolution and Gaia XP--followed by contrastive alignment using the CLIP (Contrastive Language-Image Pre-training) framework, adapted to associate spectra from different instruments. This alignment is complemented by auxiliary decoders that preserve spectrum-specific information and enable translation (prediction) between spectral types, with the former achieved by maximizing mutual information between embeddings and input spectra. The result is a cross-spectrum framework enabling intrinsic calibration and flexible applications across instruments. We demonstrate that fine-tuning these models on moderate-sized labeled datasets improves adaptability to tasks such as stellar-parameter estimation and chemical-abundance determination. SpecCLIP also enhances the accuracy and precision of parameter estimates benchmarked against external survey data. Additionally, its similarity search and cross-spectrum prediction capabilities offer potential for anomaly detection. Our results suggest that contrastively trained foundation models enriched with spectrum-aware decoders can advance precision stellar spectroscopy. Our code SpecCLIP is publicly available at https://github.com/Xiaosheng-Zhao/SpecCLIP |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_01939 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SpecCLIP: Aligning and Translating Spectroscopic Measurements for Stars Zhao, Xiaosheng Huang, Yang Xue, Guirong Kong, Xiao Liu, Jifeng Tang, Xiaoyu Beers, Timothy C. Ting, Yuan-Sen Luo, A-Li Instrumentation and Methods for Astrophysics Solar and Stellar Astrophysics Artificial Intelligence Machine Learning In recent years, large language models (LLMs) have transformed natural language understanding through vast datasets and large-scale parameterization. Inspired by this success, we present SpecCLIP, a foundation model framework that extends LLM-inspired methodologies to stellar spectral analysis. Stellar spectra, akin to structured language, encode rich physical and chemical information about stars. By training foundation models on large-scale spectral datasets, our goal is to learn robust and informative embeddings that support diverse downstream applications. As a proof of concept, SpecCLIP involves pre-training on two spectral types--LAMOST low-resolution and Gaia XP--followed by contrastive alignment using the CLIP (Contrastive Language-Image Pre-training) framework, adapted to associate spectra from different instruments. This alignment is complemented by auxiliary decoders that preserve spectrum-specific information and enable translation (prediction) between spectral types, with the former achieved by maximizing mutual information between embeddings and input spectra. The result is a cross-spectrum framework enabling intrinsic calibration and flexible applications across instruments. We demonstrate that fine-tuning these models on moderate-sized labeled datasets improves adaptability to tasks such as stellar-parameter estimation and chemical-abundance determination. SpecCLIP also enhances the accuracy and precision of parameter estimates benchmarked against external survey data. Additionally, its similarity search and cross-spectrum prediction capabilities offer potential for anomaly detection. Our results suggest that contrastively trained foundation models enriched with spectrum-aware decoders can advance precision stellar spectroscopy. Our code SpecCLIP is publicly available at https://github.com/Xiaosheng-Zhao/SpecCLIP |
| title | SpecCLIP: Aligning and Translating Spectroscopic Measurements for Stars |
| topic | Instrumentation and Methods for Astrophysics Solar and Stellar Astrophysics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2507.01939 |