CMS实验中大动量玻色子喷注探针方法的发展及应用  

Development and applications of boosted boson jet tagging methods in the CMS experiment

作　　者：高乐耘 付大为 赵宇哲 郭启隆 钱思天 杨天一 邓森 李强[1] Leyun Gao;Dawei Fu;Yuzhe Zhao;Qilong Guo;Sitian Qian;Tianyi Yang;Sen Deng;Qiang Li(State Key Laboratory of Nuclear Physics and Technology,Peking University,Beijing 100871,China)

机构地区：[1]北京大学核物理与核技术国家重点实验室,北京100871

出　　处：《科学通报》2024年第22期3208-3221,共14页Chinese Science Bulletin

基　　金：国家自然科学基金(12325504,12150005,12075004,12061141002);国家重点研发计划(2018YFA0403900)资助。

摘　　要：过去10余年来,大动量玻色子喷注的标记方法受到高能物理理论及实验界的极大关注,已经成为高能量物理前沿领域的推动技术和创新亮点.大动量玻色子常常作为共振产生的重粒子的衰变产物,广泛出现于超出标准模型的新粒子和新物理的寻找工作中.对大动量的规范玻色子(统称为V)和希格斯玻色子(H),CMS实验组成功开发了V→qq和H→bb等过程的标记技术及校准方案,并成功将其应用于多玻色子共振态的寻找等工作中.本文回顾了10余年来CMS实验中大动量玻色子探针方法的发展及应用,其中包括最新提出的新技术和新方法,特别是人工智能时代到来后出现的与深度学习紧密结合的喷注标记技术.Boosted boson jet tagging methods have been of great interest in high energy physics(HEP)theoretical and experimental research in the past decades and become driving forces and innovation targets in the high energy frontier.Boosted bosons are usually decay products of massive resonances and are widely studied in search of new particles and new physics beyond the standard model(BSM).For boosted gauge bosons(V)and Higgs bosons(H),the CMS Collaboration successfully developed tagging techniques and calibration methods for processes like V→qq and H→bb and has successfully applied them to searches for BSM processes like multi-boson resonances.This paper reviews the development and applications of boosted boson tagging methods in the CMS experiment in the past decade,including the latest proposed techniques and algorithms,especially those based heavily on deep learning techniques since the dawn of the artificial intelligence era.Traditional tagging methods mainly focus on characteristic jet substructures.They work well because the signal processes usually have typical jet substructures distinguishable from the background.Quantitative expressions of jet substructures are defined as signal-background determinants,such as N-subjettiness and energy correlation functions(ECFs),which indicate the prong number of a jet,and soft-dropped four-momentum,which is usually more precise than the raw one.In the near past,with the introduction of machine learning techniques,boosted boson tagging methods have made a great breakthrough.Successful experiences of the machine learning applications in industry are learnt to make powerful and generalizable machine learning jet tagging models.As an example,convolutional neural networks are famous for their outstanding information extraction power on images and sequences.Representing a jet as an image in theη-ϕ(pseudorapidity-azimuthal)space or sequences of particles,2D or 1D CNNs are then applicable for jet classification.For example,DeepAK8 is a great step forward in the boosted boson ta

关 键 词：CMS实验 大动量玻色子探针 超出标准模型的新物理 深度学习 

分 类 号：O572.2[理学—粒子物理与原子核物理]