[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122625-en":3,"doc-seo-122625-105":30,"detail-sidebar-cat-0-en-105":91},{"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},122625,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","NA61/SHINE online noise filtering using machine learning methods - Paper Abstract","NA61/SHINE is a high-energy physics experiment at the CERN SPS whose detector upgrade increases the event flow rate from 80 Hz to 1 kHz by replacing the TPC read-out electronics. The resulting data volume requires an efficient online noise filtering tool. Traditional filtering reconstructs tracks and discards clusters not belonging to any trajectory but is time- and resource-intensive. The proceedings present a machine-learning-based method that classifies TPC clusters to remove noisy ones quickly.","NA61/SHINE online noise filtering using machine learning methods  \nDownloaded from: [https://research.chalmers.se](https://research.chalmers.se), 2023-04-21 14:37 UTC  \nCitation for the original published paper (version of record):  \nKawecka, A., Bryliński, W., Omana Kuttan, M. et al (2023). NA61/SHINE online noise filtering using machine learning methods. Journal of Physics: Conference Series, 2438(1) .  \n[http://dx.doi.org/10.1088/1742-6596/2438/1/012104](http://dx.doi.org/10.1088/1742-6596/2438/1/012104)  \nN. B. When citing this work, cite the original published paper.  \nresearch.chalmers.se offers the possibility of retrieving research publications produced at Chalmers University of Technology. It covers all kind of research output: articles, dissertations, conference papers, reports etc. since 2004.  \nresearch.chalmers.se is administrated and maintained by Chalmers Library  \n(article starts on next page)  \nPAPER • OPEN ACCESS  \nNA61/SHINE online noise filtering using machine learning methods  \nTo cite this article: Anna Kawęcka et al 2023 J. Phys. : Conf. Ser. 2438 012104  \nView the article online for updates and enhancements.  \nYou may also like  \n-Event-by-event study of space-time dynamics in flux-tube fragmentation  \nCheuk-Yin Wong  \n-The NA61/SHINE long target pilot analysis for T2K  \nNicolas Abgrall and (on behalf ofthe NA61/SHINE collaboration)  \n-Ion program of NA61/SHINE at the CERN SPS  \nMarek Gazdzicki and (for the NA61/SHINE Collaboration)  \nThis content was downloaded from IP address [92.35.34.226](92.35.34.226) on 27/03/2023 at 08:42  \nJournal of Physics: Conference Series 2438 (2023) 012104 doi:10.1088/1742-6596/2438/1/012104  \nNA61/SHINE online noise filtering using machine learning methods  \nAnna Kawęcka 1 ,2 , Wojciech Bryliński 1 , Manjunath Omana Kuttan3 ,4 , Olena Linnyk3 ,5 ,6 , Janik Pawlowski3 ,7 , Katarzyna Schmidt8 ,  \nMarcin Słodkowski 1 , Oskar Wyszyński9 , Jakub Zieliński 1  \n1 Warsaw University of Technology, Warsaw, Poland  \n2 Chalmers University of Technology, Gothenburg, Sweden  \n3 Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany  \n4 Johann Wolfgang Goethe University, Frankfurt am Main, Germany  \n5 Justus-Liebig University Giessen, Giessen Germany  \n6 milch&zucker AG, Giessen, Germany  \n7 Philipps-University Marburg, Marburg, Germany  \n8 University of Silesia, Katowice, Poland  \n9 Jan Kochanowski University in Kielce, Poland E-mail: [anna.kawecka@cern.ch](anna.kawecka@cern.ch)  \nAbstract. The NA61/SHINE is a high-energy physics experiment operating at the SPS accelerator at CERN. The physics program of the experiment was recently extended, requiring a significant upgrade of the detector setup. The main goal of the upgrade is to increase the event flow rate from 80Hz to 1kHz by exchanging the read-out electronics of the NA61/SHINE main tracking detectors (Time-Projection-Chambers-TPCs) . As the amount of collected data will increase significantly, a tool for online noise filtering is needed. The standard method is based on the reconstruction of tracks and removal of clusters which do not belong to any particle trajectory. However, this method takes a substantial amount of time and resources. A novel approach based on machine learning methods is presented in this proceedings.  \n1. Motivation and data preparation  \nSPS Heavy Ion and Neutrino Experiment (SHINE) [1] is a fixed-target experiment operating at CERN Super Proton Synchrotron (SPS) . The NA61/SHINE detector is a multi-purpose spectrometer optimized to study hadron production in various types of collisions. The main subdetectors of the whole setup are the Time Projection Chambers (TPCs) . Two Vertex-TPCs (VTPCs), located in the magnetic field, together with two large volume Main-TPCs (MTPCs), are the main tracking devices and are able to register a large number of particle tracks (up to 1500 in central Pb+Pb collisions) . More information about the detector can be found in [1] . The primary physics motivation of th","cbCaigxCXPCMqgG7","https://ap.wps.com/l/cbCaigxCXPCMqgG7","pdf",1029339,1,7,"English","en",105,"# Motivation and data preparation\n## TPC upgrade and need for online filtering\n## Clusterization, track reconstruction, and noise impact\n## Machine learning approach for cluster classification","[{\"question\":\"Why is online noise filtering needed for NA61/SHINE?\",\"answer\":\"The detector upgrade increases the event flow rate from 80 Hz to 1 kHz, producing much higher data rates. Since not all data can be stored, fast noise filtering is required.\"},{\"question\":\"How does the standard noise filtering method work?\",\"answer\":\"The standard approach reconstructs tracks from TPC data and removes clusters that do not belong to any particle trajectory. This relies on reconstruction steps that are time- and resource-consuming.\"},{\"question\":\"What is the main idea of the machine learning approach?\",\"answer\":\"The proposed algorithms learn to classify TPC clusters and remove the noisy clusters from the data, reducing computation compared with full track-based filtering.\"}]","NA61/SHINE online noise filtering using machine learning methods - Paper Abstract | PDF",1785811784,18,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"na61shine-online-noise-filtering-using-machine-learning-methods-paper-abstract","",{"@graph":36,"@context":85},[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/na61shine-online-noise-filtering-using-machine-learning-methods-paper-abstract/122625/",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],{"name":72,"@type":73,"acceptedAnswer":74},"Why is online noise filtering needed for NA61/SHINE?","Question",{"text":75,"@type":76},"The detector upgrade increases the event flow rate from 80 Hz to 1 kHz, producing much higher data rates. Since not all data can be stored, fast noise filtering is required.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the standard noise filtering method work?",{"text":80,"@type":76},"The standard approach reconstructs tracks from TPC data and removes clusters that do not belong to any particle trajectory. 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