M. Pehlivanova , M. Tytgat , K. Mota Amarilo , A. Samalan , K. Skovpen , G.A. Alves , E. Alves Coelho , F. Marujo da Silva , M. Barroso Ferreira Filho , E.M. Da Costa , D. De Jesus Damiao , S. Fonseca De Souza , R. Gomes De Souza , L. Mundim , H. Nogima , J.P. Pinheiro , A. Santoro , M. Thiel , A. Aleksandrov , R. Hadjiiska , F. Fienga
{"title":"Machine learning approach to CMS RPC HV scan data analysis","authors":"M. Pehlivanova , M. Tytgat , K. Mota Amarilo , A. Samalan , K. Skovpen , G.A. Alves , E. Alves Coelho , F. Marujo da Silva , M. Barroso Ferreira Filho , E.M. Da Costa , D. De Jesus Damiao , S. Fonseca De Souza , R. Gomes De Souza , L. Mundim , H. Nogima , J.P. Pinheiro , A. Santoro , M. Thiel , A. Aleksandrov , R. Hadjiiska , F. Fienga","doi":"10.1016/j.nima.2025.170367","DOIUrl":null,"url":null,"abstract":"<div><div>Resistive Plate Chambers (RPC) are gaseous detectors in the muon system of the Compact Muon Solenoid (CMS) experiment at the European Laboratory for Particle Physics, CERN. The RPC high-voltage scan is a crucial sequence of calibration runs typically conducted at the onset of each data-taking year with the initial collisions of the CERN Large Hadron Collider (LHC) at nominal luminosity in proton–proton collisions <span><math><mrow><mn>2</mn><mo>×</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>34</mn></mrow></msup><mspace></mspace><msup><mrow><mi>cm</mi></mrow><mrow><mo>−</mo><mn>2</mn></mrow></msup><msup><mrow><mi>s</mi></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></mrow></math></span>, ensuring RPC proper functioning by establishing correct working points. This study applies machine learning algorithms to automate and accelerate previously manual, time-consuming analysis, enhancing efficiency and decision-making. We developed an autoencoder artificial neural network (ANN) in Fourier space (FSAC) to approximate efficiency curves, which are then used to determine working points. This new approach reduces the time for data analysis from over three months to less than a week.</div></div>","PeriodicalId":19359,"journal":{"name":"Nuclear Instruments & Methods in Physics Research Section A-accelerators Spectrometers Detectors and Associated Equipment","volume":"1075 ","pages":"Article 170367"},"PeriodicalIF":1.5000,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nuclear Instruments & Methods in Physics Research Section A-accelerators Spectrometers Detectors and Associated Equipment","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0168900225001688","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"INSTRUMENTS & INSTRUMENTATION","Score":null,"Total":0}
引用次数: 0
Abstract
Resistive Plate Chambers (RPC) are gaseous detectors in the muon system of the Compact Muon Solenoid (CMS) experiment at the European Laboratory for Particle Physics, CERN. The RPC high-voltage scan is a crucial sequence of calibration runs typically conducted at the onset of each data-taking year with the initial collisions of the CERN Large Hadron Collider (LHC) at nominal luminosity in proton–proton collisions , ensuring RPC proper functioning by establishing correct working points. This study applies machine learning algorithms to automate and accelerate previously manual, time-consuming analysis, enhancing efficiency and decision-making. We developed an autoencoder artificial neural network (ANN) in Fourier space (FSAC) to approximate efficiency curves, which are then used to determine working points. This new approach reduces the time for data analysis from over three months to less than a week.
期刊介绍:
Section A of Nuclear Instruments and Methods in Physics Research publishes papers on design, manufacturing and performance of scientific instruments with an emphasis on large scale facilities. This includes the development of particle accelerators, ion sources, beam transport systems and target arrangements as well as the use of secondary phenomena such as synchrotron radiation and free electron lasers. It also includes all types of instrumentation for the detection and spectrometry of radiations from high energy processes and nuclear decays, as well as instrumentation for experiments at nuclear reactors. Specialized electronics for nuclear and other types of spectrometry as well as computerization of measurements and control systems in this area also find their place in the A section.
Theoretical as well as experimental papers are accepted.