CMS RPC HV扫描数据分析的机器学习方法

IF 1.4 3区物理与天体物理 Q3 INSTRUMENTS & INSTRUMENTATION

Nuclear Instruments & Methods in Physics Research Section A-accelerators Spectrometers Detectors and Associated Equipment Pub Date : 2025-03-08 DOI:10.1016/j.nima.2025.170367

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":"CMS RPC HV扫描数据分析的机器学习方法","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":1.4000,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"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\":1.4000,\"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}","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

摘要

电阻板室（RPC）是欧洲粒子物理研究所（CERN）紧凑型介子螺线管（CMS）实验中介子系统中的气体探测器。RPC高压扫描是一个关键的校准序列，通常在每个数据采集年的开始进行，与CERN大型强子对撞机（LHC）在质子-质子碰撞2×1034cm−2s−1的标称亮度下的初始碰撞一起进行，通过建立正确的工作点来确保RPC正常工作。本研究应用机器学习算法来自动化和加速以前手工的、耗时的分析，提高效率和决策。我们在傅里叶空间（FSAC）中开发了一种自编码器人工神经网络（ANN）来近似效率曲线，然后将其用于确定工作点。这种新方法将数据分析的时间从三个多月缩短到不到一周。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Machine learning approach to CMS RPC HV scan data analysis

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

2 \times 1 0^{34} {cm}^{- 2} s^{- 1}

, 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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Nuclear Instruments & Methods in Physics Research Section A-accelerators Spectrometers Detectors and Associated Equipment 物理-光谱学

CiteScore

3.20

自引率

21.40%

发文量

787

审稿时长

1 months

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.