銀河系分詞器指南：科學基礎模型的基準

摘要

分詞化是將科學資料適應於基於Transformer的基礎模型的核心環節，然而其對學習表徵的影響仍未被充分理解。我們在統一的Transformer架構下，針對天文影像比較了四種分詞策略：Affine、AIM、JetFormer與VQ-VAE。使用來自DESI遺產巡天的64萬張星系影像，並以共享的AstroPT為骨幹，我們評估每種方法在重建保真度與物理性質預測上的表現。結果揭示了各種方法之間的取捨：基於流模型的JetFormer實現了更高的重建品質，而VQ-VAE在星系物理性質的探針（probe）表現上表現優異；Affine與AIM則更擅長保留局部型態資訊。我們發現重建品質與表徵品質之間是相互脫鉤的，且在本研究涵蓋的各項任務中，沒有任何一種方法能持續表現最佳。透過將評估建立在獨立測量的物理量上，我們期望本研究能凸顯科學資料作為基礎模型可解釋基準建構基礎的潛力。

English

Tokenization is central to adapting scientific data for transformer-based foundation models, yet its impact on learned representations remains poorly understood. We compare four tokenization strategies, Affine, AIM, JetFormer, and VQ-VAE, within a unified transformer framework for astronomical imaging. Using 640,000 galaxy images from the DESI Legacy Survey and a shared AstroPT backbone, we evaluate each method on reconstruction fidelity and prediction of physical properties. Our results reveal trade-offs across approaches. The flow-based JetFormer achieves higher reconstruction quality, while VQ-VAE yields strong probe performance for galaxy physical properties. Affine and AIM better preserve localized morphological information. We find that reconstruction and representation quality are decoupled, and no single method consistently performs best across the tasks considered here. By grounding our evaluation in independently measured physical quantities, we hope this study serves to highlight the potential of scientific data as a basis for constructing interpretable benchmarks for foundation models.