Machine Learning based tool for CMS RPC currents quality monitoring

The muon system of the CERN Compact Muon Solenoid (CMS) experiment includes more than a thousand Resistive Plate Chambers (RPC). They are gaseous detectors operated in the hostile environment of the CMS underground cavern on the Large Hadron Collider where pp luminosities of up to <math display=&...

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Detalles Bibliográficos
Autores principales: Shumka, E., Samalan, A., Tytgat, M., Sawy, M. El, Alves, G.A., Marujo, F., Coelho, E.A., Da Costa, E.M., Nogima, H., Santoro, A., De Souza, S. Fonseca, De Jesus Damiao, D., Thiel, M., Mota Amarilo, K., Filho, M. Barroso Ferreira, Aleksandrov, A., Hadjiiska, R., Iaydjiev, P., Rodozov, M., Shopova, M., Soultanov, G., Dimitrov, A., Litov, L., Pavlov, B., Petkov, P., Petrov, A., Qian, S.J., Kou, H., Liu, Z.-A., Zhao, J., Song, J., Hou, Q., Diao, W., Cao, P., Avila, C., Barbosa, D., Cabrera, A., Florez, A., Fraga, J., Reyes, J., Assran, Y., Mahmoud, M.A., Mohammed, Y., Crotty, I., Laktineh, I., Grenier, G., Gouzevitch, M., Mirabito, L., Shchablo, K., Bagaturia, I., Lomidze, I., Tsamalaidze, Z., Amoozegar, V., Boghrati, B., Ebraimi, M., Mohammadi Najafabadi, M., Zareian, E., Abbrescia, M., Iaselli, G., Pugliese, G., Loddo, F., De Filippis, N., Aly, R., Ramos, D., Elmetenawee, W., Leszki, S., Margjeka, I., Paesani, D., Benussi, L., Bianco, S., Piccolo, D., Meola, S., Buontempo, S., Carnevali, F., Lista, L., Paolucci, P., Fienga, F., Braghieri, A., Salvini, P., Montagna, P., Riccardi, C., Vitulo, P., Asilar, E., Choi, J., Kim, T.J., Choi, S.Y., Hong, B., Lee, K.S., Oh, H.Y., Goh, J., Yu, I., Estrada, C. Uribe, Pedraza, I., Castilla-Valdez, H., Sanchez-Hernandez, A., Fernandez, R.L., Ramirez-Garcia, M., Vazquez, E., Shah, M.A., Zaganidis, N., Radi, A., Hoorani, H., Muhammad, S., Ahmad, A., Asghar, I., Khan, W.A., Eysermans, J., Da Silva De Araujo, F. Torres
Lenguaje:eng
Publicado: 2023
Materias:
hep-ex
Particle Physics - Experiment
physics.ins-det
Detectors and Experimental Techniques
Acceso en línea:https://dx.doi.org/10.1016/j.nima.2023.168449
http://cds.cern.ch/record/2856533
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author Shumka, E.
Samalan, A.
Tytgat, M.
Sawy, M. El
Alves, G.A.
Marujo, F.
Coelho, E.A.
Da Costa, E.M.
Nogima, H.
Santoro, A.
De Souza, S. Fonseca
De Jesus Damiao, D.
Thiel, M.
Mota Amarilo, K.
Filho, M. Barroso Ferreira
Aleksandrov, A.
Hadjiiska, R.
Iaydjiev, P.
Rodozov, M.
Shopova, M.
Soultanov, G.
Dimitrov, A.
Litov, L.
Pavlov, B.
Petkov, P.
Petrov, A.
Qian, S.J.
Kou, H.
Liu, Z.-A.
Zhao, J.
Song, J.
Hou, Q.
Diao, W.
Cao, P.
Avila, C.
Barbosa, D.
Cabrera, A.
Florez, A.
Fraga, J.
Reyes, J.
Assran, Y.
Mahmoud, M.A.
Mohammed, Y.
Crotty, I.
Laktineh, I.
Grenier, G.
Gouzevitch, M.
Mirabito, L.
Shchablo, K.
Bagaturia, I.
Lomidze, I.
Tsamalaidze, Z.
Amoozegar, V.
Boghrati, B.
Ebraimi, M.
Mohammadi Najafabadi, M.
Zareian, E.
Abbrescia, M.
Iaselli, G.
Pugliese, G.
Loddo, F.
De Filippis, N.
Aly, R.
Ramos, D.
Elmetenawee, W.
Leszki, S.
Margjeka, I.
Paesani, D.
Benussi, L.
Bianco, S.
Piccolo, D.
Meola, S.
Buontempo, S.
Carnevali, F.
Lista, L.
Paolucci, P.
Fienga, F.
Braghieri, A.
Salvini, P.
Montagna, P.
Riccardi, C.
Vitulo, P.
Asilar, E.
Choi, J.
Kim, T.J.
Choi, S.Y.
Hong, B.
Lee, K.S.
Oh, H.Y.
Goh, J.
Yu, I.
Estrada, C. Uribe
Pedraza, I.
Castilla-Valdez, H.
Sanchez-Hernandez, A.
Fernandez, R.L.
Ramirez-Garcia, M.
Vazquez, E.
Shah, M.A.
Zaganidis, N.
Radi, A.
Hoorani, H.
Muhammad, S.
Ahmad, A.
Asghar, I.
Khan, W.A.
Eysermans, J.
Da Silva De Araujo, F. Torres
author_facet Shumka, E.
Samalan, A.
Tytgat, M.
Sawy, M. El
Alves, G.A.
Marujo, F.
Coelho, E.A.
Da Costa, E.M.
Nogima, H.
Santoro, A.
De Souza, S. Fonseca
De Jesus Damiao, D.
Thiel, M.
Mota Amarilo, K.
Filho, M. Barroso Ferreira
Aleksandrov, A.
Hadjiiska, R.
Iaydjiev, P.
Rodozov, M.
Shopova, M.
Soultanov, G.
Dimitrov, A.
Litov, L.
Pavlov, B.
Petkov, P.
Petrov, A.
Qian, S.J.
Kou, H.
Liu, Z.-A.
Zhao, J.
Song, J.
Hou, Q.
Diao, W.
Cao, P.
Avila, C.
Barbosa, D.
Cabrera, A.
Florez, A.
Fraga, J.
Reyes, J.
Assran, Y.
Mahmoud, M.A.
Mohammed, Y.
Crotty, I.
Laktineh, I.
Grenier, G.
Gouzevitch, M.
Mirabito, L.
Shchablo, K.
Bagaturia, I.
Lomidze, I.
Tsamalaidze, Z.
Amoozegar, V.
Boghrati, B.
Ebraimi, M.
Mohammadi Najafabadi, M.
Zareian, E.
Abbrescia, M.
Iaselli, G.
Pugliese, G.
Loddo, F.
De Filippis, N.
Aly, R.
Ramos, D.
Elmetenawee, W.
Leszki, S.
Margjeka, I.
Paesani, D.
Benussi, L.
Bianco, S.
Piccolo, D.
Meola, S.
Buontempo, S.
Carnevali, F.
Lista, L.
Paolucci, P.
Fienga, F.
Braghieri, A.
Salvini, P.
Montagna, P.
Riccardi, C.
Vitulo, P.
Asilar, E.
Choi, J.
Kim, T.J.
Choi, S.Y.
Hong, B.
Lee, K.S.
Oh, H.Y.
Goh, J.
Yu, I.
Estrada, C. Uribe
Pedraza, I.
Castilla-Valdez, H.
Sanchez-Hernandez, A.
Fernandez, R.L.
Ramirez-Garcia, M.
Vazquez, E.
Shah, M.A.
Zaganidis, N.
Radi, A.
Hoorani, H.
Muhammad, S.
Ahmad, A.
Asghar, I.
Khan, W.A.
Eysermans, J.
Da Silva De Araujo, F. Torres
author_sort Shumka, E.
collection CERN
description The muon system of the CERN Compact Muon Solenoid (CMS) experiment includes more than a thousand Resistive Plate Chambers (RPC). They are gaseous detectors operated in the hostile environment of the CMS underground cavern on the Large Hadron Collider where pp luminosities of up to <math display="inline" id="d1e1856" altimg="si1.svg"><mrow><mn>2</mn><mo linebreak="goodbreak" linebreakstyle="after">×</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>34</mn></mrow></msup></mrow></math><math display="inline" id="d1e1873" altimg="si2.svg"><mrow><msup><mrow><mtext>cm</mtext></mrow><mrow><mo>−</mo><mn>2</mn></mrow></msup><msup><mrow><mtext>s</mtext></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></mrow></math> are routinely achieved. The CMS RPC system performance is constantly monitored and the detector is regularly maintained to ensure stable operation. The main monitorable characteristics are dark current, efficiency for muon detection, noise rate etc. Herein we describe an automated tool for CMS RPC current monitoring which uses Machine Learning techniques. We further elaborate on the dedicated generalized linear model proposed already and add autoencoder models for self-consistent predictions as well as hybrid models to allow for RPC current predictions in a distant future.
id cern-2856533
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2023
record_format invenio
spelling cern-28565332023-07-21T03:24:25Zdoi:10.1016/j.nima.2023.168449http://cds.cern.ch/record/2856533engShumka, E.Samalan, A.Tytgat, M.Sawy, M. ElAlves, G.A.Marujo, F.Coelho, E.A.Da Costa, E.M.Nogima, H.Santoro, A.De Souza, S. FonsecaDe Jesus Damiao, D.Thiel, M.Mota Amarilo, K.Filho, M. Barroso FerreiraAleksandrov, A.Hadjiiska, R.Iaydjiev, P.Rodozov, M.Shopova, M.Soultanov, G.Dimitrov, A.Litov, L.Pavlov, B.Petkov, P.Petrov, A.Qian, S.J.Kou, H.Liu, Z.-A.Zhao, J.Song, J.Hou, Q.Diao, W.Cao, P.Avila, C.Barbosa, D.Cabrera, A.Florez, A.Fraga, J.Reyes, J.Assran, Y.Mahmoud, M.A.Mohammed, Y.Crotty, I.Laktineh, I.Grenier, G.Gouzevitch, M.Mirabito, L.Shchablo, K.Bagaturia, I.Lomidze, I.Tsamalaidze, Z.Amoozegar, V.Boghrati, B.Ebraimi, M.Mohammadi Najafabadi, M.Zareian, E.Abbrescia, M.Iaselli, G.Pugliese, G.Loddo, F.De Filippis, N.Aly, R.Ramos, D.Elmetenawee, W.Leszki, S.Margjeka, I.Paesani, D.Benussi, L.Bianco, S.Piccolo, D.Meola, S.Buontempo, S.Carnevali, F.Lista, L.Paolucci, P.Fienga, F.Braghieri, A.Salvini, P.Montagna, P.Riccardi, C.Vitulo, P.Asilar, E.Choi, J.Kim, T.J.Choi, S.Y.Hong, B.Lee, K.S.Oh, H.Y.Goh, J.Yu, I.Estrada, C. UribePedraza, I.Castilla-Valdez, H.Sanchez-Hernandez, A.Fernandez, R.L.Ramirez-Garcia, M.Vazquez, E.Shah, M.A.Zaganidis, N.Radi, A.Hoorani, H.Muhammad, S.Ahmad, A.Asghar, I.Khan, W.A.Eysermans, J.Da Silva De Araujo, F. TorresMachine Learning based tool for CMS RPC currents quality monitoringhep-exParticle Physics - Experimentphysics.ins-detDetectors and Experimental TechniquesThe muon system of the CERN Compact Muon Solenoid (CMS) experiment includes more than a thousand Resistive Plate Chambers (RPC). They are gaseous detectors operated in the hostile environment of the CMS underground cavern on the Large Hadron Collider where pp luminosities of up to <math display="inline" id="d1e1856" altimg="si1.svg"><mrow><mn>2</mn><mo linebreak="goodbreak" linebreakstyle="after">×</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>34</mn></mrow></msup></mrow></math><math display="inline" id="d1e1873" altimg="si2.svg"><mrow><msup><mrow><mtext>cm</mtext></mrow><mrow><mo>−</mo><mn>2</mn></mrow></msup><msup><mrow><mtext>s</mtext></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></mrow></math> are routinely achieved. The CMS RPC system performance is constantly monitored and the detector is regularly maintained to ensure stable operation. The main monitorable characteristics are dark current, efficiency for muon detection, noise rate etc. Herein we describe an automated tool for CMS RPC current monitoring which uses Machine Learning techniques. We further elaborate on the dedicated generalized linear model proposed already and add autoencoder models for self-consistent predictions as well as hybrid models to allow for RPC current predictions in a distant future.The muon system of the CERN Compact Muon Solenoid (CMS) experiment includes more than a thousand Resistive Plate Chambers (RPC). They are gaseous detectors operated in the hostile environment of the CMS underground cavern on the Large Hadron Collider where pp luminosities of up to $2\times 10^{34}$$\text{cm}^{-2}\text{s}^{-1}$ are routinely achieved. The CMS RPC system performance is constantly monitored and the detector is regularly maintained to ensure stable operation. The main monitorable characteristics are dark current, efficiency for muon detection, noise rate etc. Herein we describe an automated tool for CMS RPC current monitoring which uses Machine Learning techniques. We further elaborate on the dedicated generalized linear model proposed already and add autoencoder models for self-consistent predictions as well as hybrid models to allow for RPC current predictions in a distant future.arXiv:2302.02764oai:cds.cern.ch:28565332023-02-06
spellingShingle hep-ex
Particle Physics - Experiment
physics.ins-det
Detectors and Experimental Techniques
Shumka, E.
Samalan, A.
Tytgat, M.
Sawy, M. El
Alves, G.A.
Marujo, F.
Coelho, E.A.
Da Costa, E.M.
Nogima, H.
Santoro, A.
De Souza, S. Fonseca
De Jesus Damiao, D.
Thiel, M.
Mota Amarilo, K.
Filho, M. Barroso Ferreira
Aleksandrov, A.
Hadjiiska, R.
Iaydjiev, P.
Rodozov, M.
Shopova, M.
Soultanov, G.
Dimitrov, A.
Litov, L.
Pavlov, B.
Petkov, P.
Petrov, A.
Qian, S.J.
Kou, H.
Liu, Z.-A.
Zhao, J.
Song, J.
Hou, Q.
Diao, W.
Cao, P.
Avila, C.
Barbosa, D.
Cabrera, A.
Florez, A.
Fraga, J.
Reyes, J.
Assran, Y.
Mahmoud, M.A.
Mohammed, Y.
Crotty, I.
Laktineh, I.
Grenier, G.
Gouzevitch, M.
Mirabito, L.
Shchablo, K.
Bagaturia, I.
Lomidze, I.
Tsamalaidze, Z.
Amoozegar, V.
Boghrati, B.
Ebraimi, M.
Mohammadi Najafabadi, M.
Zareian, E.
Abbrescia, M.
Iaselli, G.
Pugliese, G.
Loddo, F.
De Filippis, N.
Aly, R.
Ramos, D.
Elmetenawee, W.
Leszki, S.
Margjeka, I.
Paesani, D.
Benussi, L.
Bianco, S.
Piccolo, D.
Meola, S.
Buontempo, S.
Carnevali, F.
Lista, L.
Paolucci, P.
Fienga, F.
Braghieri, A.
Salvini, P.
Montagna, P.
Riccardi, C.
Vitulo, P.
Asilar, E.
Choi, J.
Kim, T.J.
Choi, S.Y.
Hong, B.
Lee, K.S.
Oh, H.Y.
Goh, J.
Yu, I.
Estrada, C. Uribe
Pedraza, I.
Castilla-Valdez, H.
Sanchez-Hernandez, A.
Fernandez, R.L.
Ramirez-Garcia, M.
Vazquez, E.
Shah, M.A.
Zaganidis, N.
Radi, A.
Hoorani, H.
Muhammad, S.
Ahmad, A.
Asghar, I.
Khan, W.A.
Eysermans, J.
Da Silva De Araujo, F. Torres
Machine Learning based tool for CMS RPC currents quality monitoring
title Machine Learning based tool for CMS RPC currents quality monitoring
title_full Machine Learning based tool for CMS RPC currents quality monitoring
title_fullStr Machine Learning based tool for CMS RPC currents quality monitoring
title_full_unstemmed Machine Learning based tool for CMS RPC currents quality monitoring
title_short Machine Learning based tool for CMS RPC currents quality monitoring
title_sort machine learning based tool for cms rpc currents quality monitoring
topic hep-ex
Particle Physics - Experiment
physics.ins-det
Detectors and Experimental Techniques
url https://dx.doi.org/10.1016/j.nima.2023.168449
http://cds.cern.ch/record/2856533
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