微调高能物理联合分析优化的基础模型

IF 6.3 2区 物理与天体物理 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Matthias Vigl, Nicole Hartman and Lukas Heinrich
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引用次数: 0

摘要

在这项工作中,我们证明了在高能物理(HEP)中,通过超越顺序优化或重建和分析组件的标准范式,可以显著提高性能和数据效率。我们从概念上将高能物理重构和分析与预训练、微调、域适应和高维嵌入空间等现代机器学习工作流程联系起来,并量化了在通过中间二希格斯系统衰变到四个 b 喷射的重共振搜索示例用例中的收益。据我们所知,这是第一个针对下游 HEP 分析目标对低级特征提取网络进行微调的例子。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Finetuning foundation models for joint analysis optimization in High Energy Physics
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. To our knowledge this is the first example of a low-level feature extraction network finetuned for a downstream HEP analysis objective.
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来源期刊
Machine Learning Science and Technology
Machine Learning Science and Technology Computer Science-Artificial Intelligence
CiteScore
9.10
自引率
4.40%
发文量
86
审稿时长
5 weeks
期刊介绍: Machine Learning Science and Technology is a multidisciplinary open access journal that bridges the application of machine learning across the sciences with advances in machine learning methods and theory as motivated by physical insights. Specifically, articles must fall into one of the following categories: advance the state of machine learning-driven applications in the sciences or make conceptual, methodological or theoretical advances in machine learning with applications to, inspiration from, or motivated by scientific problems.
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