[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122915-en":3,"doc-seo-122915-105":30,"detail-sidebar-cat-0-en-105":95},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122915,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Identifying quenched jets in heavy ion collisions with machine learning","Measurements of jet substructure in ultra-relativistic heavy ion collisions indicate that jet showering is modified by the interaction with the quark-gluon plasma. Hard substructure changes can be investigated using data-driven techniques, and this work develops a machine learning method to identify quenched jets. Jet showering is simulated with Jewel (quenching) and Pythia 8 (no quenching), while sequential angular-ordered substructure variables train an LSTM-based neural network. The method detects quenching despite the large uncorrelated soft-particle background.","Published for SISSA by  Springer  \nReceived: July 14, 2022  \nRevised: March 9, 2023  \nAccepted: March 30, 2023  \nPublished: April 28, 2023  \nIdentifying quenched jets in heavy ion collisions with machine learning  \nLihan Liu,a Julia Velkovska,a Yilun Wua and Marta Verweijb  \na Department of Physics and Astronomy, Vanderbilt University, PMB 401807, 2301 Vanderbilt Place, Nashville, TN 37235, U. S. A.  \nb Department of Physics, Utrecht University, Heidelberglaan 8 3584 CS Utrecht, The Netherlands  \nE-mail: [lihan.liu@vanderbilt.edu](lihan.liu@vanderbilt.edu), [julia.velkovska@vanderbilt.edu](julia.velkovska@vanderbilt.edu),  \n[yilun.wu@vanderbilt.edu](yilun.wu@vanderbilt.edu), [m.verweij@uu.nl](m.verweij@uu.nl)  \nAbstract: Measurements of jet substructure in ultra-relativistic heavy ion collisions suggest that the jet showering process is modi􀀜ed by the interaction with the quark-gluon plasma. Modi􀀜cations of the hard substructure of jets can be explored with modern datadriven techniques. In this study, a machine learning approach to the identi􀀜cation of quenched jets is designed. Jet showering processes are simulated with a jet quenching model Jewel and a non-quenching model Pythia 8 . Sequential substructure variables are extracted from the jet clustering history following an angular-ordered sequence and are used in the training of a neural network built on top of a long short-term memory network. We show that this approach successfully identi􀀜es the quenching e􀀛ect in the presence of the large uncorrelated background of soft particles created in heavy-ion collisions.  \nKeywords: Jets and Jet Substructure, Quark-Gluon Plasma  \nArXiv ePrint: 2206.01628  \nOpen Access, 􀀍c The Authors.  \nArticle funded by SCOAP3 . [https://doi.org/10.1007/JHEP04](https://doi.org/10.1007/JHEP04) (2023)140  \nJ HEP04(2023)140  \nContents  \n1 Introduction 1  \n2 Data sample 3  \n3 Supervised machine learning 7  \n3.1 Feature engineering 7  \n3.2 Training of LSTM-based neural network 8  \n3.3 Hyper-tuning 10  \n3.4 Robustness 11  \n4 Results 13  \n5 Conclusion 13  \nA Training with Jewel Vacuum Events 16  \n1 Introduction  \nHighly energetic partons (quarks and gluons) are expected to lose energy while traversing the extremely hot and dense quark-gluon plasma (QGP) created in ultra-relativistic heavy ion collisions [1] . This phenomenon, known as 􀀐jet quenching􀀑, was discovered at the Relativistic Heavy Ion Collider (RHIC) through the observation of suppression of high transverse momentum (pT ) hadrons [2􀀕5] and back-to-back dihadron correlations [6] . These observations were con􀀜rmed at the Large Hadron Collider (LHC) and extended into a larger kinematic range [7􀀕11] .  \nIn addition to measurements on high pT hadrons, which are the leading fragments of jets, the LHC opened new opportunities for direct observations of jet quenching and jet modi􀀜cations in the QGP. Signi􀀜cant dijet transverse momentum asymmetry [12􀀕15] and suppressed jet production [16􀀕18] were observed in lead-lead (PbPb) collisions. Jet-hadron correlations gave insights into how the energy lost by the jets is distributed in the medium produced in the collisions. Measurements of the jet shapes (transverse momentum radial pro􀀜le) [19􀀕21] and the jet fragmentation functions [22, 23] were performed to further investigate the mechanism of the jet shower modi􀀜cations in the QGP. To quantify the medium modi􀀜cations, measurements in proton-proton (pp) collisions [24􀀕32] are used as a reference for jet modi􀀜cations in the medium. The jet splitting function has been measured in both pp and PbPb collisions [33􀀕35], through the usage of a jet grooming algorithm that is able to split (􀀐decluster􀀑) a single jet into two subjets and locate the hard splitting by removing softer wide-angle radiation contributions. The hard splitting, characterized by  \nJ HEP04(2023)140  \ntwo well-separated subjets (􀀐two-prong􀀑 structure), provides access to the early stages in the parton shower evolution.  \nIn recent years many m","cbCaigOfag6mtDaY","https://ap.wps.com/l/cbCaigOfag6mtDaY","pdf",906791,1,23,"English","en",105,"# Introduction\n# Data sample\n# Supervised machine learning\n## Feature engineering\n## Training of LSTM-based neural network\n## Hyper-tuning\n## Robustness\n# Results\n# Conclusion\n# Training with Jewel Vacuum Events","[{\"question\":\"What physics problem does this study address?\",\"answer\":\"It aims to identify quenched jets and quantify modifications to jet showering induced by the quark-gluon plasma in ultra-relativistic heavy ion collisions.\"},{\"question\":\"How is jet quenching modeled for training and evaluation?\",\"answer\":\"Jets are simulated using Jewel with a quenching model and Pythia 8 without quenching, providing paired examples for supervised learning.\"},{\"question\":\"What machine learning approach is used to classify quenched jets?\",\"answer\":\"Sequential jet substructure variables extracted from an angular-ordered clustering history are used to train a neural network built on a long short-term memory (LSTM) architecture.\"},{\"question\":\"Does the method remain effective in realistic heavy-ion backgrounds?\",\"answer\":\"Yes. The approach is shown to successfully identify the quenching effect even in the presence of a large uncorrelated background of soft particles.\"}]","Identifying quenched jets in heavy ion collisions with machine learning | PDF",1785813637,58,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":90,"head_meta":92,"extra_data":94,"updated_unix":28},"identifying-quenched-jets-in-heavy-ion-collisions-with-machine-learning","",{"@graph":36,"@context":89},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/identifying-quenched-jets-in-heavy-ion-collisions-with-machine-learning/122915/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"What physics problem does this study address?","Question",{"text":75,"@type":76},"It aims to identify quenched jets and quantify modifications to jet showering induced by the quark-gluon plasma in ultra-relativistic heavy ion collisions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is jet quenching modeled for training and evaluation?",{"text":80,"@type":76},"Jets are simulated using Jewel with a quenching model and Pythia 8 without quenching, providing paired examples for supervised learning.",{"name":82,"@type":73,"acceptedAnswer":83},"What machine learning approach is used to classify quenched jets?",{"text":84,"@type":76},"Sequential jet substructure variables extracted from an angular-ordered clustering history are used to train a neural network built on a long short-term memory (LSTM) architecture.",{"name":86,"@type":73,"acceptedAnswer":87},"Does the method remain effective in realistic heavy-ion backgrounds?",{"text":88,"@type":76},"Yes. 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