用流匹配模型学出32倍压缩的宇宙暗物质模拟数据表示。
CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching
- 基于流匹配学习无监督的紧凑潜在表示
- 压缩比达32倍，支持重建与参数推断
- 潜空间通道对应不同宇宙尺度特征
生成式机器学习模型已被证明能学习保留下游任务所需信息的低维数据表示。本文展示基于流匹配的生成模型可无监督地学习场级冷暗物质（CDM）模拟数据的紧凑且语义丰富的潜在表示。我们的模型CosmoFlow将表示尺寸压缩至原始场数据的1/32，可用于场级重构、合成数据生成和参数推断。该模型还学习到可解释的表示，不同潜空间通道对应不同宇宙学尺度的特征。
原文摘要 · Abstract (English)
Generative machine learning models have been demonstrated to be able to learn low dimensional representations of data that preserve information required for downstream tasks. In this work, we demonstrate that flow matching based generative models can learn compact, semantically rich latent representations of field level cold dark matter (CDM) simulation data without supervision. Our model, CosmoFlow, learns representations 32x smaller than the raw field data, usable for field level reconstruction, synthetic data generation, and parameter inference. Our model also learns interpretable representations, in which different latent channels correspond to features at different cosmological scales.
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