银河系标记器指南：科学基础模型基准

摘要

词元化是将科学数据适配至基于Transformer的基础模型的核心步骤，但其对学习表征的影响仍未被充分理解。我们在统一的天体成像Transformer框架下比较了四种词元化策略：Affine、AIM、JetFormer和VQ-VAE。基于DESI Legacy巡天中的64万张星系图像及共享的AstroPT骨干网络，我们评估了每种方法的重建保真度与物理属性预测能力。研究揭示了不同策略间的权衡：基于流的JetFormer实现了更高的重建质量，而VQ-VAE在星系物理属性的探针预测中表现突出；Affine和AIM则更擅长保留局部形态学信息。我们发现重建质量与表征质量存在解耦，且没有任何一种方法能在所有评估任务中持续领先。通过将评估基准锚定于独立测量的物理量，本研究旨在凸显科学数据作为构建可解释基础模型基准的潜力。

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.