Gestalt：天文学元基础模型
Gestalt: a meta-foundation model for astronomy
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中文总结 AI 辅助
本研究提出Gestalt，一种通过组合22个冻结基础模型并应用白化与随机SVD构建的天文元基础模型，无需训练即可在星系属性估计上超越各组件，并显著节省计算资源。
中文摘要 AI 辅助
柏拉图式表征假说预测，规模足够大的基础模型会收敛到对世界的共享表征。由于每个未收敛的模型在接收相同输入时，都会对共同结构给出一个带噪声的视图，我们提出疑问：能否将多个模型组合成一个优于其各组成部分的表征？我们在星系上对此进行了测试：我们通过一组包含来自八个家族的22个冻结基础模型的篮子对图像进行嵌入，对每个视图进行白化处理，并对嵌入拼接结果进行随机奇异值分解。由此得到的1024维嵌入在HSC、JWST和DESI Legacy巡天的物理属性与星系形态估计中，在19/21的测试指标上优于篮子中的每个成员。我们发现，性能随篮子规模和篮子架构多样性的增加而提升，并且该元基础模型的性能可跨天文巡天迁移。我们得出结论：一个有用的天文基础模型可以从现有的通用模型中组装而成，无需训练，只需一次无监督投影。通过利用社区已付出的工作，我们节省了大量计算：重新预训练一个相当的单域模型将耗费约10^4至10^5 A100 GPU小时（排放数吨二氧化碳当量），而组装Gestalt仅需在单台机器上花费几分钟。
英文摘要
The Platonic Representation Hypothesis predicts that sufficiently scaled foundation models converge on a shared representation of the world. As each non-converged model gives a noisy view of a common structure when passed the same input, we ask whether we can combine models into a representation that outperforms its individual components. We test this on galaxies: we embed images via a basket of 22 frozen foundation models from eight families, whiten each view, and take a randomised SVD of the embedding concatenation. The resulting 1024-dimensional embedding outperforms every basket member on 19/21 of our tested metrics for physical property and galaxy morphology estimation for HSC, JWST, and DESI Legacy Survey imagery. We find that performance rises with basket size and basket architectural diversity, and that the meta-foundation model's performance transfers across astronomical surveys. We conclude that a useful astronomical foundation model can be assembled from existing generalist models with no training required beyond a single unsupervised projection. By leveraging the community's already-spent work, we save a lot of compute: a fresh pre-train of a comparable single-domain model would cost $\mathcal{O}(10^{4}$--$10^{5})$ A100 GPU hours (emitting several tonnes of CO$_2$eq.), whereas assembling Gestalt requires minutes on a single machine.
发表机构
- AstroAI（天文人工智能）
- UniverseTBD（宇宙待定）
- Washington University in St. Louis（圣路易斯华盛顿大学）
机构由 AI 辅助整理，请以论文原文为准。
