In this work we demonstrate that significant gains in performance and data efficiency can be achieved in High Energy Physics (HEP) by moving beyond the standard paradigm of sequential optimization or reconstruction and analysis components. We conceptually connect HEP reconstruction and analysis to modern machine learning workflows such as pretraining, finetuning, domain adaptation and high-dimensional embedding spaces and quantify the gains in the example usecase of searches of heavy resonances decaying via an intermediate di-Higgs system to four $b$-jets.

翻译：本研究证明，在高能物理（HEP）领域，通过超越标准的顺序优化或重建与分析组件范式，可以在性能和数据处理效率方面取得显著提升。我们从概念上连接了高能物理重建分析与现代机器学习工作流（如预训练、微调、域自适应和高维嵌入空间），并以通过中间双希格斯系统衰变为四$b$喷注的重共振态搜索为例，量化了相关性能增益。