Pattern Recognition with Artificial Intelligence in Space Experiments

F. Cuna

doi:10.3390/particles8040099

2025-12-10

DOI：10.3390/particles8040099

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摘要

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先进人工智能（AI）技术在天体粒子实验中的应用，在数据分析和实验设计两方面均代表了重大进展。随着空间任务日益复杂，集成AI工具对于优化系统性能和最大化科学回报至关重要。本研究探讨了在空间实验的跟踪系统中使用图神经网络（GNNs）的应用。轨迹重建中的一个关键挑战是噪声水平较高，主要源于反散射轨迹，这可能会干扰初级粒子轨迹的识别。我们提出了一种基于GNN的新型方法，用于节点级分类任务，专门设计用于在跟踪器中区分初级轨迹与反散射轨迹。在该框架中，AI被用作强大的模式识别工具，使系统能够在复杂的跟踪数据中识别有意义的结构，并以更高的精度区分信号与反散射。通过解决这些挑战，本研究旨在借助先进的深度学习技术，提升天体粒子物理学中数据解释的准确性和可靠性。

Keyword:

Artificial Intelligence

Graph Neural Networks

HPC

space experiments

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Particles

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2.3

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机构

istituto nazionale di fisica nucleare (infn)

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3.0W

论文数: 1.2W

被引数: 14

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