提出通用方法让任意网络实现洛伦兹协变，提升速度与效率。
Lorentz Local Canonicalization: How to Make Any Network Lorentz-Equivariant
- 通过预测局部参考系实现任意网络的洛伦兹协变性。
- 在粒子物理任务上达到顶尖精度，速度提升4倍，计算量减少10倍。
- 适用于高能物理建模，尤其适合追求高效协变网络的研究者。
洛伦兹协变神经网络正成为高能物理领域的主流架构。现有方法依赖专用层，限制了网络结构选择。本文提出洛伦兹局部规范化的通用框架（LLoCa），可使任意主干网络精确满足洛伦兹协变性。利用协变预测的局部参考系，构建了LLoCa-Transformer和图网络。我们将近期几何消息传递方法拓展至非紧致洛伦兹群，实现时空张量特征的传播。数据增强可由参考系的特殊选择自然生成。所提模型在相关粒子物理任务上达到竞争性及最先进性能，同时速度提升4倍，浮点运算量减少10倍。
原文摘要 · Abstract (English)
Lorentz-equivariant neural networks are becoming the leading architectures for high-energy physics. Current implementations rely on specialized layers, limiting architectural choices. We introduce Lorentz Local Canonicalization (LLoCa), a general framework that renders any backbone network exactly Lorentz-equivariant. Using equivariantly predicted local reference frames, we construct LLoCa-transformers and graph networks. We adapt a recent approach for geometric message passing to the non-compact Lorentz group, allowing propagation of space-time tensorial features. Data augmentation emerges from LLoCa as a special choice of reference frame. Our models achieve competitive and state-of-the-art accuracy on relevant particle physics tasks, while being $4\times$ faster and using $10\times$ fewer FLOPs.
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