Machine learning approach to CMS RPC HV scan data analysis
  • Pehlivanova, M.
  • Tytgat, M.
  • Amarilo, K. Mota
  • Samalan, A.
  • Skovpen, K.
  • 외 100명
Citations

WEB OF SCIENCE

0
Citations

SCOPUS

0

초록

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 2×1034cm−2s−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. © 2025

키워드

ANNEfficiencyFSACHV scanRPCWorking points
제목
Machine learning approach to CMS RPC HV scan data analysis
저자
Pehlivanova, M.Tytgat, M.Amarilo, K. MotaSamalan, A.Skovpen, K.Alves, G.A.Coelho, E. Alvesda Silva, F. MarujoFilho, M. Barroso FerreiraDa Costa, E.M.De Jesus Damiao, D.De Souza, S. FonsecaDe Souza, R. GomesMundim, L.Nogima, H.Pinheiro, J.P.Santoro, A.Thiel, M.Aleksandrov, A.Hadjiiska, R.Iaydjiev, P.Shopova, M.Sultanov, G.Dimitrov, A.Litov, L.Pavlov, B.Petkov, P.Petrov, A.Shumka, E.Cao, P.Diao, W.Hou, Q.Kou, H.Liu, Z.-A.Song, J.Zhao, J.Qian, S.J.Avila, C.Barbosa Trujillo, D.A.Cabrera, A.Florez, C.A.Vega, J.A. ReyesAly, R.Radi, A.Assran, Y.Crotty, I.Mahmoud, M.A.Gouzevitch, M.Grenier, G.Laktineh, I.B.Mirabito, L.Bagaturia, I.Lomidze, I.Tsamalaidze, Z.Amoozegar, V.Boghrati, B.Ebrahimi, M.Esfandi, F.Hosseini, Y.Najafabadi, M. MohammadiZareian, E.Abbrescia, M.De Filippis, N.Iaselli, G.Loddo, F.Pugliese, G.Ramos, D.Benussi, L.Bianco, S.Meola, S.Piccolo, D.Buontempo, S.Carnevali, F.Lista, L.Paolucci, P.Braghieri, A.Montagna, P.Riccardi, C.Salvini, P.Vitulo, P.Asilar, E.Kim, T.J.Ryou, Y.Choi, S.Hong, B.Lee, K.S.Goh, J.Shin, J.Lee, Y.Pedraza, I.Estrada, C. UribeCastilla-Valdez, H.Lopez-Fernandez, R.Hernández, A. SánchezGarcía, M. RamírezRamirez Guadarrama, D.L.Shah, M.A.Vazquez, E.Zaganidis, N.Ahmad, A.Asghar, M.I.Hoorani, H.R.Muhammad, S.Eysermans, J.Fienga, F.
DOI
10.1016/j.nima.2025.170367
발행일
2025-06
유형
Article
저널명
Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
1075