本系列讲座旨在探索大模型在高能物理实验中的应用与挑战，涵盖从机器学习、生成模型到大规模计算在大科学装置中的实践。我们将邀请来自全球的专家，深入讨论大模型如何推动实验高能物理的创新与进展。
This seminar series aims to explore the applications and challenges of large models in experimental high-energy physics, covering topics ranging from machine learning, generative models, to large-scale computing in large scientific facilities. We will invite experts from around the world to discuss how large models are driving innovation and progress in experimental high-energy physics.
第一期：喷注物理中的大模型
Episode 1: Large Jet Models
在本次讲座中，我们将重点讨论大喷注模型（Large Jet Models），这些模型在粒子探测和碰撞数据分析中扮演着至关重要的角色。
本次讲座将包括两个关于自监督学习的报告，也会包含一个监督式大尺度表示学习，聚焦如何利用机器学习方法提升大喷注模型的精度与效率。
In this episode, we will focus on Large Jet Models, which play a critical role in particle detection and collision data analysis.
This episode will feature two reports on self-supervised learning and one on representation learning with large scale supervised learning, focusing on how machine learning techniques can enhance the precision and efficiency of large jet models.
本期会议为线上进行，线下设有有限的座位（～五十人）。
This session will be held majorly online, with limited in-person seats available (approximately fifty people).
ZOOM: https://cern.zoom.us/j/69346649179?pwd=xbXA8GpSUWcuN0bmuC1WVVe5xuuzVI.1
(passcode: 485746)
