Conveners
人工智能和机器学习的应用
- Gang LI (IHEP)
人工智能和机器学习的应用
- Ye Yuan (IHEP)
人工智能和机器学习的应用
- Zuhao LI (中国科学院高能物理研究所)
人工智能和机器学习的应用
- Liang Li (Shanghai Jiao Tong University)
人工智能和机器学习的应用
- 勋 谌 (SJTU)
人工智能和机器学习的应用
- 勋 谌 (SJTU)
The Jiangmen Underground Neutrino Observatory (JUNO) is a next-generation neutrino experiment currently under construction in southern China. It is designed with a 20 kton liquid scintillator detector and 78% photomultiplier tube (PMT) coverage. The primary physics goal of JUNO is to determine the neutrino mass ordering and measure oscillation parameters with unprecedented precision. JUNO’s...
DarkSHINE is an electron-on-target experiment proposed to search for light dark matter. In this talk, we present the application of Graph Neural Networks (GNN) for the tracking and vertex reconstruction in the proposed DarkSHINE experiment with full simulation samples. Compared to the traditional Kalman Filter method, GNN method doubles the signal efficiency while significantly reducing the...
Particle Identification (PID) plays a central role in associating the energy depositions in calorimeter cells with the type of primary particle in a particle flow oriented detector system. In this talk, we hope to demonstrate novel PID methods based on the Residual Network (ResNet) architecture to classify experiment data collected at CERN in 2022 and 2023 for the CEPC AHCAL prototype Beam...
In science and engineering, fundamental problems include the forward problem of simulating the evolution of complex physical systems, and the inverse design/inverse problem of optimizing/inferring the system's high-dimensional parameters. Traditional numerical simulation and optimization methods often require extensive computation due to complex physical dynamics. In this talk, I will...
The BESIII experiment has collected the world's largest sample of charm hadron data, yielding a wealth of physical results. Large AI models, with their comprehensive data and god's-eye view, have the potential to significantly enhance the efficiency of human scientific discovery. The Computing Center and Experimental Physics Center at IHEP have collaborated to develop Dr. Sai, an AI agent for...
The search for heavy resonances beyond the Standard Model (BSM) is a key objective at the LHC. While the recent use of advanced deep neural networks for boosted-jet tagging significantly enhances the sensitivity of dedicated searches, it is limited to specific final states, leaving vast potential BSM phase space underexplored. In this talk, we introduce a novel experimental method,...
Generative Adversarial Networks (GANs), as a powerful framework in deep learning, have been widely applied in various fields. This study aims to develop a rapid data generator by training GAN networks on the data collected from the Pandax-II Run11 AmBe experiment. The data generated by the GAN network not only preserves the individual physical characteristics but also maintains the...
Graphs are a popular mathematical abstraction for systems of relations and interactions that can be applied in various domains such as physics, biology, social sciences, etc. Towards unleashing the power of machine learning models for graphs, one fundamental challenge is how to obtain high-quality representations for graph-structured data with diverse scales and properties. We will talk about...
Machine learning has revolutionized the analysis of large-scale data samples in high energy physics (HEP) and greatly increased the discovery potential for new fundamental laws of nature. Specifically, graph neural networks (GNNs), thanks to their high flexibility and expressiveness, have demonstrated superior performance over classical deep learning approaches in tackling data analysis...
In the Muon g-2 experiment, the tracking reconstruction is a key component of the data reconstruction and analysis, it provides essential beam dynamics parameters and muon weighting parameters and determines the precision of muon EDM measurements. This presentation introduces the GNN-based tracking reconstruction method. Leveraging message-passing mechanisms and the Louvain algorithm, the GNN...
To enhance the scientific discovery power of high-energy collider experiments, we propose and realize the concept of jet-origin identification that categorizes jets into five quark species (b; c; s; u; d), five corresponding antiquarks, and the gluon.
Using state-of-the-art algorithms and simulated ν¯νH, H → jj
events at 240 GeV center-of-mass energy at the electron-positron Higgs factory,...
We propose a new jet tagging method based on Transformer architecture called More Interaction Particle Transformer (miParT). This method improves upon the ParT algorithm by modifying the attention mechanism and increasing the embedding dimension of the pairwise particle interaction input, all while reducing the total number of parameters and computational complexity. We tested miParT on two...
The reconstruction of neutral hadrons, particularly (anti-)neutrons, presents a significant challenge in high-energy physics experiments, notably at BESIII, which lacks a dedicated hadronic calorimeter. This talk will cover the innovative techniques applied in the identification of neutrons, in the first observation of the $\Lambda_c^+\to ne^+ \nu$ process. Furthermore, it will discuss the...
The classification of cosmic-ray components in ground-based air shower experiments such as LHAASO is a challenging task. ParticleNet is a DGCNN-based model designed for particle physics applications. In this presentation, we use ParticleNet to identify the proton and light components from background cosmic-ray events in the simulation data of LHAASO-KM2A. The results show enhanced...
