显式还是隐式？在精密前沿编码物理
Explicit or Implicit? Encoding Physics at the Precision Frontier
AI总结：
本文比较了L-GATr和OmniLearn模型在重带宽展开、似然比估计和弱监督异常检测任务中的性能，发现显式编码粒子物理结构的效率提升在方法上具有普遍性。
摘要：
高能物理中的高性能机器学习工具依赖于两个互补方向：在架构中显式编码对称性，以及通过大规模（预）训练隐式学习数据结构。我们比较了代表性的L-GATr和OmniLearn模型在三个极具挑战性的任务中的性能：基于重带宽的展开、似然比估计和弱监督异常检测。在所有基准测试中，两种方法在细调数据集的统计精度下表现相当，表明显式编码已知粒子物理结构所带来的显著效率提升在方法上具有普遍性。
英文摘要：
High-performance machine learning tools in particle physics rest on two complementary directions: encoding symmetries explicitly in the architecture, and implicitly learning the structure of the data through large-scale (pre-) training. We compare the performance of the representative L-GATr and OmniLearn models on three especially challenging tasks: reweighting-based unfolding, likelihood-ratio estimation, and weakly supervised anomaly detection. Across all benchmarks, both methods achieve comparable performance given the statistical precision of the finetuning datasets, suggesting that the significant efficiency gains from encoding known particle physics structures are largely method-independent.
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