[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122943-en":3,"doc-seo-122943-105":29,"detail-sidebar-cat-0-en-105":90},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},122943,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Particle identification with machine learning in ALICE Run 3 - Domain adaptation and neural networks for PID","Particle identification (PID) is essential for ALICE measurements of quark–gluon plasma, where different particle species must be discriminated with high precision. ALICE subdetectors provide information over roughly 100 MeV/c to 20 GeV/c, but machine learning can exploit richer detector correlations. Building on Run 2 studies where Random Forests improved efficiency and purity, this work develops domain-adaptation neural networks to reduce mismatches between Monte Carlo and experimental data and extends them with feature set embedding and attention. The approach is integrated into the ALICE Run 3 Analysis Framework, with preliminary PID results and potential optimizations discussed.","Particle identification with machine learning in ALICE Run 3  \nMaja Karwowska 1,2 , ∗ , Monika Jakubowska3 , Łukasz Graczykowski2 , Kamil Deja4,5 , and Miłosz Kasak4  \n1CERN – European Organization for Nuclear Research  \n2Faculty of Physics, Warsaw University of Technology  \n3Faculty of Electrical Engineering, Warsaw University of Technology  \n4Faculty of Electronics and Information Technology, Warsaw University of Technology  \n5IDEAS NCBR  \nAbstract. The main focus of the ALICE experiment, quark–gluon plasma measurements, requires accurate particle identification (PID) . The ALICE subdetectors allow identifying particles over a broad momentum interval ranging from about 100 MeV/c up to 20 GeV/c. However, a machine learning (ML) model can explore more detector information. During LHC Run 2, preliminary studies with Random Forests obtained much higher efficiencies and purities for selected particles than standard techniques.  \nFor Run 3, we investigate Domain Adaptation Neural Networks that account for the discrepancies between the Monte Carlo simulations and the experimental data. Preliminary studies show that domain adaptation improves particle classification. Moreover, the solution is extended with Feature Set Embedding and attention to give the network more flexibility to train on data with various sets of detector signals. PID ML is already integrated with the ALICE Run 3 Analysis Framework. Preliminary results for the PID of selected particle species, including real-world analyzes, are discussed as well as the possible optimizations.  \n1 Introduction  \nALICE (A Large Ion Collider Experiment) [1] is one of the four big detectors located at the Large Hadron Collider (LHC) at CERN. ALICE studies the properties of quark–gluon plasma (QGP), a hot and dense state of matter, and the strong force that holds quarks together inside hadrons [2] . Detailed analysis of QGP requires accurate particle identification (PID), i.e., the ability to discriminate between different particle species produced during the collision. High PID precision distinguishes ALICE from other LHC experiments and allows for selecting a subset of particles required for specific analysis.  \nThe ALICE experiment is composed of several sub-detectors, some of which measure particle properties that can be used for identification. Figure 1 presents a scheme of the detector in Run 1 and Run 2 LHC data-taking periods.  \nThe three detectors particularly useful for PID are: Time Projection Chamber (TPC), Time-of-Flight (TOF), Transition Radiation Detector (TRD) . TPC is one of the most important ALICE detectors as it records 3D information of the trajectory of the particles, and  \n∗[e-mail: mkabus@cern.ch](e-mail: mkabus@cern.ch)  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \nFigure 1. Components of the ALICE detector in its Run 2 configuration [3] .  \nparticle ionization energy loss essential for particle identification. The Time-of-Flight detector measures particle travel time from the collision vertex to the detector, from which particle velocity and mass are calculated. TRD records transition radiation, that is, the emission of a few photons by traversing electrons, which helps in distinguishing electrons from other charged particles.  \nWith the signals recorded by the detectors described above, particles are chosen using selection criteria compared with theoretical calculations. The traditional method compares the number of standard deviations from the expected value for all detector signals. Particles falling outside the selection region are rejected. However, when the characteristics of different particle species overlap, combining information from multiple detectors becomes difficult. Choosing selection regions by trial and error is less effective in this case, low","cbCaivzusbmjEjfq","https://ap.wps.com/l/cbCaivzusbmjEjfq","pdf",3639215,1,"English","en",105,"# Introduction\n# Machine learning for PID\n## Feed-forward neural network baseline\n## Data and detector-information selection\n## Limitations and performance considerations","[{\"question\":\"Why is particle identification crucial for ALICE quark–gluon plasma analyses?\",\"answer\":\"Accurate PID enables ALICE to distinguish particle species produced in collisions. High PID precision allows selecting the particle subset needed for specific QGP studies and improves analysis precision and significance.\"},{\"question\":\"What improvement over standard techniques is investigated for ALICE Run 3 PID?\",\"answer\":\"The study investigates domain-adaptation neural networks to address discrepancies between Monte Carlo simulations and experimental data. Preliminary work indicates improved particle classification compared with approaches that do not handle the domain shift.\"},{\"question\":\"How do the authors extend the neural-network approach beyond a basic classifier?\",\"answer\":\"The solution incorporates feature set embedding and attention mechanisms to make the network more flexible when training on data with different sets of detector signals. PID ML is already integrated into the ALICE Run 3 Analysis Framework.\"}]","Particle identification with machine learning in ALICE Run 3 - Domain adaptation and neural networks for PID | PDF",1785813799,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"particle-identification-with-machine-learning-in-alice-run-3-domain-adaptation-and-neural-networks-for-pid","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/particle-identification-with-machine-learning-in-alice-run-3-domain-adaptation-and-neural-networks-for-pid/122943/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why is particle identification crucial for ALICE quark–gluon plasma analyses?","Question",{"text":74,"@type":75},"Accurate PID enables ALICE to distinguish particle species produced in collisions. High PID precision allows selecting the particle subset needed for specific QGP studies and improves analysis precision and significance.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What improvement over standard techniques is investigated for ALICE Run 3 PID?",{"text":79,"@type":75},"The study investigates domain-adaptation neural networks to address discrepancies between Monte Carlo simulations and experimental data. Preliminary work indicates improved particle classification compared with approaches that do not handle the domain shift.",{"name":81,"@type":72,"acceptedAnswer":82},"How do the authors extend the neural-network approach beyond a basic classifier?",{"text":83,"@type":75},"The solution incorporates feature set embedding and attention mechanisms to make the network more flexible when training on data with different sets of detector signals. 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