This talk will focus on the application of Deep Learning, specifically Graph Neural Networks (GNNs), in physics analysis at BESIII. It will explore their effectiveness in studying Lambda_c semileptonic decays and hadronic decays, leveraging the power of GNNs to analyze data with complex relational structures. We will also try to discuss the systematic uncertainry treatment, which is still an...
We have developed an end-to-end data analysis framework, HEP ML Lab (HML), based on Python for signal-background analysis in high-energy physics research. It offers essential interfaces and shortcuts for event generation, dataset creation, and method application.
With the HML API, a large volume of collision events can be generated in sequence under different settings. The representations...
The Jiangmen Underground Neutrino Observatory (JUNO) is designed to determine neutrino mass ordering (NMO) using a large liquid scintillator detector located in southern China. While JUNO’s NMO sensitivity mostly comes from reactor neutrinos, atmospheric neutrino oscillation in JUNO can provide complimentary sensitivity via matter effects, and enhance its overall sensitivity in the combined...
对马约拉纳中微子的研究是当前粒子物理领域探索超出标准模型新物理的热点研究方向，无中微子双贝塔衰变（NLDBD）是实验上可以确认中微子马约拉纳属性的稀有核衰变。PandaX-III合作组致力于打造具有国际竞争力的百公斤靶质量实验，采用基于气体微结构探测器技术的高压气氙时间投影室来寻找Xe-136的NLDBD过程，其最显著优势在于能够通过带电粒子径迹特征进行信号本底鉴别，进而大幅提高实验对NLDBD的探测灵敏度。本报告将从PandaX-III实验中带电粒子径迹特征分析入手，基于机器学习方法开展信号鉴别、事例顶点重建等粒子特征分析工作，推动PandaX-III朝着零本底实验条件发展。
High-energy physics relies on large and accurate samples of simulated events, but generating these samples with GEANT4 is CPU intensive. The ATLAS experiment has employed generative adversarial networks (GANs) for fast shower simulation, which is an important approach to solving the problem. Quantum GANs, leveraging the advantages of quantum computing, have the potential to outperform standard...
At the High Luminosity Large Hadron Collider (HL-LHC), we will enter the “exa-byte” era, where the annual computing cost will increase by a factor of 10-20 from the ongoing LHC program. Without various innovations, the experiments will not be able to operate. The Graphical Processing Units (GPU) and other state-of-the-art artificial intelligence technologies will be the baseline at the HL-LHC....
Utilizing the Lehmann-Symanzik-Zimmermann (LSZ) reduction formula, we present a new general framework for computing scattering amplitudes in quantum field theory with quantum computers in a fully nonperturbative way. In this framework, one only has to construct one-particle states of zero momentum, and no wave packets of incoming particles are needed. The framework is able to incorporate...
The reconstruction of top-quarks from their decay components is a complex problem which limits the sensitivity of many analyses. A novel approach to this problem, utilizing Symmetry Preserving Attention Networks, has been previously presented for all-hadronic ttbar decays. In this talk, we present new features implemented in the algorithm as well as its extended application to semi-leptonic...
