利用有条件的双自动编码器触发暗阵雨

IF 6.3 2区 物理与天体物理 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Machine Learning Science and Technology Pub Date : 2024-09-02 DOI:10.1088/2632-2153/ad652b
Luca Anzalone, Simranjit Singh Chhibra, Benedikt Maier, Nadezda Chernyavskaya, Maurizio Pierini
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引用次数: 0

摘要

我们提出了一系列条件双自动编码器(CoDAEs),用于在对撞机上进行与模型无关的通用新物理搜索。在我们的研究中,由新型粒子和相互作用产生的新物理信号被视为导致数据偏离预期背景事件的异常现象。在这项工作中,我们只利用背景样本进行正常异常检测,在原始探测器图像上应用(变异)自动编码器搜索暗版强作用力的表现,原始图像大且高度稀疏,无需利用任何基于物理的预处理或对信号的强假设。所提出的 CoDAE 采用双编码器设计,具有通用性,能通过空间调节学习一个辅助但紧凑的潜空间,与基于物理的竞争基线和相关方法相比有明显改善,因此也缩小了与完全监督模型的差距。这是第一次证明无监督模型对多种暗雨模型具有出色的辨别能力,说明这种方法适合作为一种精确、快速、独立于模型的算法来部署,例如在 ATLAS 和 CMS 等大型强子对撞机实验的实时事件触发系统中部署。
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Triggering dark showers with conditional dual auto-encoders
We present a family of conditional dual auto-encoders (CoDAEs) for generic and model-independent new physics searches at colliders. New physics signals, which arise from new types of particles and interactions, are considered in our study as anomalies causing deviations in data with respect to expected background events. In this work, we perform a normal-only anomaly detection, which employs only background samples, to search for manifestations of a dark version of strong force applying (variational) auto-encoders on raw detector images, which are large and highly sparse, without leveraging any physics-based pre-processing or strong assumption on the signals. The proposed CoDAE has a dual-encoder design, which is general and can learn an auxiliary yet compact latent space through spatial conditioning, showing a neat improvement over competitive physics-based baselines and related approaches, therefore also reducing the gap with fully supervised models. It is the first time an unsupervised model is shown to exhibit excellent discrimination against multiple dark shower models, illustrating the suitability of this method as an accurate, fast, model-independent algorithm to deploy, e.g. in the real-time event triggering systems of large hadron collider experiments such as ATLAS and CMS.
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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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