Autoencoder-based Anomaly Detection System for Online Data Quality Monitoring of the CMS Electromagnetic Calorimeter

The CMS detector is a general-purpose apparatus that detects high-energy collisions produced at the LHC. Online Data Quality Monitoring of the CMS electromagnetic calorimeter is a vital operational tool that allows detector experts to quickly identify, localize, and diagnose a broad range of detecto...

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Detalles Bibliográficos
Autores principales: Abadjiev, D., Adams, T., Adzic, P., Ahmad, M., Amendola, C., Andrews, M.B., Arcidiacono, R., Argiro, S., Askew, A., Auffray, E., Azzolini, V., Bailleux, D., Band, R., Barney, D., Barria, P., Bartosik, N., Basile, C., Bastos, D., Bell, K.W., Besancon, M., Bianco, R., Biino, C., Blinov, V., Borca, C., Bornheim, A., Brown, R.M., Campana, M., Castells, S., Cavallari, F., Cetorelli, F., Chatterjee, R.M., Chatterjee, S., Chaudhary, G., Chen, J.A., Chernyavskaya, N., Chung, H., Cipriani, M., Cokic, L., Cooke, C., Cossio, F., Couderc, F., Cristoforetti, D., Cucciati, G., Cunqueiro Mendez, L., Da Silva Di Calafiori, D., Dafinei, I., Cockerill, D.J.A., Dejardin, M., Del Re, D., Della Ricca, G., Depasse, P., Dervan, J., Di Marco, E., Diemoz, M., Dimova, T., Dissertori, G., Dittmar, M., Dolgopolov, A., Donegà, M., Dordevic, M., El Mamouni, H., Errico, F., Espinosa, F., Faure, J.L., Fay, J., Fernandez Menendez, J., Ferri, F., Finco, L., Fiori, F., Frahm, E., Funk, W., Gadek, T., Gajownik, J., Galli, M., Ganjour, S., Gascon, S., Ghezzi, A., Ghose, P., Gninenko, S., Goadhouse, S., Godinovic, N., Golubev, N., Govoni, P., Gras, P., Hakala, J., Hamel de Monchenault, G., Härringer, N., Hashmi, R., Heath, H.F., Hirosky, R., Ho, K.W., Hou, X., Ingram, Q., Jain, Sh., Javaid, T., Jessop, C., Jimènez, R., Joshi, B.M., Jourd'hui, E., Kaadze, K., Kao, Y.-W., Kardapoltsev, L., Khurana, R., King, J., Kirilovas, A., Konstantinov, D., Kovac, M., Krishna, A., Kuo, C.M., Lambrecht, L., Lavizzari, G., Lecoq, P., Ledovskoy, A., Legger, F., Lelas, D., Li, Y.Y., Liang, Z., Lin, W., Longo, E., Loukas, N., Lu, R.-S., Lustermann, W., Lutton, L., Lyon, A.-M., Maeshima, K., Malcles, J., Mandrik, P., Manzoni, R.A., Marchese, L., Marinelli, N., Marini, A.C., Martin, L., Marzocchi, B., Mascellani, A., Massironi, A., Matveev, V., Mazza, G., Meridiani, P., Mijic, M., Mijuskovic, J., Milenovic, P., Milosevic, J., Monteno, M., Monti, F., Moortgat, F., Mousa, J., Mudholkar, T., Nessi-Tedaldi, F., Nicolaou, C., Nigamova, A., Obertino, M.M., Organtini, G., Orimoto, T., Orlandi, F., Ovtin, I., Paganis, E., Papagiannis, D., Pandolfi, F., Paramatti, R., Park, K., Pastrone, N., Paulini, M., Pauss, F., Petkovic, A., Petraityte, E., Pettinacci, V., Petyt, D., Pigazzini, S., Pinolini, B.S., Prova, P.R., Quaranta, C., Ragazzi, S., Rahatlou, S., Rasteiro Da Silva, J.C., Razis, P.A., Rebello Teles, P., Reis, T., Riti, F., Rogan, C., Romanteau, T., Rosowsky, A., Rovelli, C., Rovere, M., Rusack, R., Salvi, G., Sancar, O., Sanchez, A., Sandever, C., Santanastasio, F., Saradhy, R., Sarkar, U., Schneider, M., Schroeder, N., Sculac, A., Sculac, T., Shahzad, M.A., Shepherd-Themistocleous, C.H., Simkina, P., Singla, A., Singovsky, A., Skovpen, Y., Smith, V.J., Soffi, L., Stachon, K., Steen, A., Steggemann, J., Succar, M., Tao, J., Tishelman-Charny, A., Tiwari, P.C., Tornago, M., Tramontano, R., Tsai, L.-S., Usai, E., Valsecchi, D., Vagnerini, A., Varela, J., Venditti, R., Verma, P.V., Vlassov, E., Wachirapusitanand, V., Wamorkar, T., Wang, C., Wang, J., Wadud, M.A., Yu, S.S., Zabi, A., Zghiche, A., Zhang, L., Zhu, R.Y., Zygala, L.
Lenguaje:eng
Publicado: 2023
Materias:
physics.data-an
Other Fields of Physics
hep-ex
Particle Physics - Experiment
cs.LG
Computing and Computers
physics.ins-det
Detectors and Experimental Techniques
Acceso en línea:http://cds.cern.ch/record/2871804
_version_ 1780978565680988160
author Abadjiev, D.
Adams, T.
Adzic, P.
Ahmad, M.
Amendola, C.
Andrews, M.B.
Arcidiacono, R.
Argiro, S.
Askew, A.
Auffray, E.
Azzolini, V.
Bailleux, D.
Band, R.
Barney, D.
Barria, P.
Bartosik, N.
Basile, C.
Bastos, D.
Bell, K.W.
Besancon, M.
Bianco, R.
Biino, C.
Blinov, V.
Borca, C.
Bornheim, A.
Brown, R.M.
Campana, M.
Castells, S.
Cavallari, F.
Cetorelli, F.
Chatterjee, R.M.
Chatterjee, S.
Chaudhary, G.
Chen, J.A.
Chernyavskaya, N.
Chung, H.
Cipriani, M.
Cokic, L.
Cooke, C.
Cossio, F.
Couderc, F.
Cristoforetti, D.
Cucciati, G.
Cunqueiro Mendez, L.
Da Silva Di Calafiori, D.
Dafinei, I.
Cockerill, D.J.A.
Dejardin, M.
Del Re, D.
Della Ricca, G.
Depasse, P.
Dervan, J.
Di Marco, E.
Diemoz, M.
Dimova, T.
Dissertori, G.
Dittmar, M.
Dolgopolov, A.
Donegà, M.
Dordevic, M.
El Mamouni, H.
Errico, F.
Espinosa, F.
Faure, J.L.
Fay, J.
Fernandez Menendez, J.
Ferri, F.
Finco, L.
Fiori, F.
Frahm, E.
Funk, W.
Gadek, T.
Gajownik, J.
Galli, M.
Ganjour, S.
Gascon, S.
Ghezzi, A.
Ghose, P.
Gninenko, S.
Goadhouse, S.
Godinovic, N.
Golubev, N.
Govoni, P.
Gras, P.
Hakala, J.
Hamel de Monchenault, G.
Härringer, N.
Hashmi, R.
Heath, H.F.
Hirosky, R.
Ho, K.W.
Hou, X.
Ingram, Q.
Jain, Sh.
Javaid, T.
Jessop, C.
Jimènez, R.
Joshi, B.M.
Jourd'hui, E.
Kaadze, K.
Kao, Y.-W.
Kardapoltsev, L.
Khurana, R.
King, J.
Kirilovas, A.
Konstantinov, D.
Kovac, M.
Krishna, A.
Kuo, C.M.
Lambrecht, L.
Lavizzari, G.
Lecoq, P.
Ledovskoy, A.
Legger, F.
Lelas, D.
Li, Y.Y.
Liang, Z.
Lin, W.
Longo, E.
Loukas, N.
Lu, R.-S.
Lustermann, W.
Lutton, L.
Lyon, A.-M.
Maeshima, K.
Malcles, J.
Mandrik, P.
Manzoni, R.A.
Marchese, L.
Marinelli, N.
Marini, A.C.
Martin, L.
Marzocchi, B.
Mascellani, A.
Massironi, A.
Matveev, V.
Mazza, G.
Meridiani, P.
Mijic, M.
Mijuskovic, J.
Milenovic, P.
Milosevic, J.
Monteno, M.
Monti, F.
Moortgat, F.
Mousa, J.
Mudholkar, T.
Nessi-Tedaldi, F.
Nicolaou, C.
Nigamova, A.
Obertino, M.M.
Organtini, G.
Orimoto, T.
Orlandi, F.
Ovtin, I.
Paganis, E.
Papagiannis, D.
Pandolfi, F.
Paramatti, R.
Park, K.
Pastrone, N.
Paulini, M.
Pauss, F.
Petkovic, A.
Petraityte, E.
Pettinacci, V.
Petyt, D.
Pigazzini, S.
Pinolini, B.S.
Prova, P.R.
Quaranta, C.
Ragazzi, S.
Rahatlou, S.
Rasteiro Da Silva, J.C.
Razis, P.A.
Rebello Teles, P.
Reis, T.
Riti, F.
Rogan, C.
Romanteau, T.
Rosowsky, A.
Rovelli, C.
Rovere, M.
Rusack, R.
Salvi, G.
Sancar, O.
Sanchez, A.
Sandever, C.
Santanastasio, F.
Saradhy, R.
Sarkar, U.
Schneider, M.
Schroeder, N.
Sculac, A.
Sculac, T.
Shahzad, M.A.
Shepherd-Themistocleous, C.H.
Simkina, P.
Singla, A.
Singovsky, A.
Skovpen, Y.
Smith, V.J.
Soffi, L.
Stachon, K.
Steen, A.
Steggemann, J.
Succar, M.
Tao, J.
Tishelman-Charny, A.
Tiwari, P.C.
Tornago, M.
Tramontano, R.
Tsai, L.-S.
Usai, E.
Valsecchi, D.
Vagnerini, A.
Varela, J.
Venditti, R.
Verma, P.V.
Vlassov, E.
Wachirapusitanand, V.
Wamorkar, T.
Wang, C.
Wang, J.
Wadud, M.A.
Yu, S.S.
Zabi, A.
Zghiche, A.
Zhang, L.
Zhu, R.Y.
Zygala, L.
author_facet Abadjiev, D.
Adams, T.
Adzic, P.
Ahmad, M.
Amendola, C.
Andrews, M.B.
Arcidiacono, R.
Argiro, S.
Askew, A.
Auffray, E.
Azzolini, V.
Bailleux, D.
Band, R.
Barney, D.
Barria, P.
Bartosik, N.
Basile, C.
Bastos, D.
Bell, K.W.
Besancon, M.
Bianco, R.
Biino, C.
Blinov, V.
Borca, C.
Bornheim, A.
Brown, R.M.
Campana, M.
Castells, S.
Cavallari, F.
Cetorelli, F.
Chatterjee, R.M.
Chatterjee, S.
Chaudhary, G.
Chen, J.A.
Chernyavskaya, N.
Chung, H.
Cipriani, M.
Cokic, L.
Cooke, C.
Cossio, F.
Couderc, F.
Cristoforetti, D.
Cucciati, G.
Cunqueiro Mendez, L.
Da Silva Di Calafiori, D.
Dafinei, I.
Cockerill, D.J.A.
Dejardin, M.
Del Re, D.
Della Ricca, G.
Depasse, P.
Dervan, J.
Di Marco, E.
Diemoz, M.
Dimova, T.
Dissertori, G.
Dittmar, M.
Dolgopolov, A.
Donegà, M.
Dordevic, M.
El Mamouni, H.
Errico, F.
Espinosa, F.
Faure, J.L.
Fay, J.
Fernandez Menendez, J.
Ferri, F.
Finco, L.
Fiori, F.
Frahm, E.
Funk, W.
Gadek, T.
Gajownik, J.
Galli, M.
Ganjour, S.
Gascon, S.
Ghezzi, A.
Ghose, P.
Gninenko, S.
Goadhouse, S.
Godinovic, N.
Golubev, N.
Govoni, P.
Gras, P.
Hakala, J.
Hamel de Monchenault, G.
Härringer, N.
Hashmi, R.
Heath, H.F.
Hirosky, R.
Ho, K.W.
Hou, X.
Ingram, Q.
Jain, Sh.
Javaid, T.
Jessop, C.
Jimènez, R.
Joshi, B.M.
Jourd'hui, E.
Kaadze, K.
Kao, Y.-W.
Kardapoltsev, L.
Khurana, R.
King, J.
Kirilovas, A.
Konstantinov, D.
Kovac, M.
Krishna, A.
Kuo, C.M.
Lambrecht, L.
Lavizzari, G.
Lecoq, P.
Ledovskoy, A.
Legger, F.
Lelas, D.
Li, Y.Y.
Liang, Z.
Lin, W.
Longo, E.
Loukas, N.
Lu, R.-S.
Lustermann, W.
Lutton, L.
Lyon, A.-M.
Maeshima, K.
Malcles, J.
Mandrik, P.
Manzoni, R.A.
Marchese, L.
Marinelli, N.
Marini, A.C.
Martin, L.
Marzocchi, B.
Mascellani, A.
Massironi, A.
Matveev, V.
Mazza, G.
Meridiani, P.
Mijic, M.
Mijuskovic, J.
Milenovic, P.
Milosevic, J.
Monteno, M.
Monti, F.
Moortgat, F.
Mousa, J.
Mudholkar, T.
Nessi-Tedaldi, F.
Nicolaou, C.
Nigamova, A.
Obertino, M.M.
Organtini, G.
Orimoto, T.
Orlandi, F.
Ovtin, I.
Paganis, E.
Papagiannis, D.
Pandolfi, F.
Paramatti, R.
Park, K.
Pastrone, N.
Paulini, M.
Pauss, F.
Petkovic, A.
Petraityte, E.
Pettinacci, V.
Petyt, D.
Pigazzini, S.
Pinolini, B.S.
Prova, P.R.
Quaranta, C.
Ragazzi, S.
Rahatlou, S.
Rasteiro Da Silva, J.C.
Razis, P.A.
Rebello Teles, P.
Reis, T.
Riti, F.
Rogan, C.
Romanteau, T.
Rosowsky, A.
Rovelli, C.
Rovere, M.
Rusack, R.
Salvi, G.
Sancar, O.
Sanchez, A.
Sandever, C.
Santanastasio, F.
Saradhy, R.
Sarkar, U.
Schneider, M.
Schroeder, N.
Sculac, A.
Sculac, T.
Shahzad, M.A.
Shepherd-Themistocleous, C.H.
Simkina, P.
Singla, A.
Singovsky, A.
Skovpen, Y.
Smith, V.J.
Soffi, L.
Stachon, K.
Steen, A.
Steggemann, J.
Succar, M.
Tao, J.
Tishelman-Charny, A.
Tiwari, P.C.
Tornago, M.
Tramontano, R.
Tsai, L.-S.
Usai, E.
Valsecchi, D.
Vagnerini, A.
Varela, J.
Venditti, R.
Verma, P.V.
Vlassov, E.
Wachirapusitanand, V.
Wamorkar, T.
Wang, C.
Wang, J.
Wadud, M.A.
Yu, S.S.
Zabi, A.
Zghiche, A.
Zhang, L.
Zhu, R.Y.
Zygala, L.
author_sort Abadjiev, D.
collection CERN
description The CMS detector is a general-purpose apparatus that detects high-energy collisions produced at the LHC. Online Data Quality Monitoring of the CMS electromagnetic calorimeter is a vital operational tool that allows detector experts to quickly identify, localize, and diagnose a broad range of detector issues that could affect the quality of physics data. A real-time autoencoder-based anomaly detection system using semi-supervised machine learning is presented enabling the detection of anomalies in the CMS electromagnetic calorimeter data. A novel method is introduced which maximizes the anomaly detection performance by exploiting the time-dependent evolution of anomalies as well as spatial variations in the detector response. The autoencoder-based system is able to efficiently detect anomalies, while maintaining a very low false discovery rate. The performance of the system is validated with anomalies found in 2018 and 2022 LHC collision data. Additionally, the first results from deploying the autoencoder-based system in the CMS online Data Quality Monitoring workflow during the beginning of Run 3 of the LHC are presented, showing its ability to detect issues missed by the existing system.
id cern-2871804
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2023
record_format invenio
spelling cern-28718042023-10-14T02:21:34Zhttp://cds.cern.ch/record/2871804engAbadjiev, D.Adams, T.Adzic, P.Ahmad, M.Amendola, C.Andrews, M.B.Arcidiacono, R.Argiro, S.Askew, A.Auffray, E.Azzolini, V.Bailleux, D.Band, R.Barney, D.Barria, P.Bartosik, N.Basile, C.Bastos, D.Bell, K.W.Besancon, M.Bianco, R.Biino, C.Blinov, V.Borca, C.Bornheim, A.Brown, R.M.Campana, M.Castells, S.Cavallari, F.Cetorelli, F.Chatterjee, R.M.Chatterjee, S.Chaudhary, G.Chen, J.A.Chernyavskaya, N.Chung, H.Cipriani, M.Cokic, L.Cooke, C.Cossio, F.Couderc, F.Cristoforetti, D.Cucciati, G.Cunqueiro Mendez, L.Da Silva Di Calafiori, D.Dafinei, I.Cockerill, D.J.A.Dejardin, M.Del Re, D.Della Ricca, G.Depasse, P.Dervan, J.Di Marco, E.Diemoz, M.Dimova, T.Dissertori, G.Dittmar, M.Dolgopolov, A.Donegà, M.Dordevic, M.El Mamouni, H.Errico, F.Espinosa, F.Faure, J.L.Fay, J.Fernandez Menendez, J.Ferri, F.Finco, L.Fiori, F.Frahm, E.Funk, W.Gadek, T.Gajownik, J.Galli, M.Ganjour, S.Gascon, S.Ghezzi, A.Ghose, P.Gninenko, S.Goadhouse, S.Godinovic, N.Golubev, N.Govoni, P.Gras, P.Hakala, J.Hamel de Monchenault, G.Härringer, N.Hashmi, R.Heath, H.F.Hirosky, R.Ho, K.W.Hou, X.Ingram, Q.Jain, Sh.Javaid, T.Jessop, C.Jimènez, R.Joshi, B.M.Jourd'hui, E.Kaadze, K.Kao, Y.-W.Kardapoltsev, L.Khurana, R.King, J.Kirilovas, A.Konstantinov, D.Kovac, M.Krishna, A.Kuo, C.M.Lambrecht, L.Lavizzari, G.Lecoq, P.Ledovskoy, A.Legger, F.Lelas, D.Li, Y.Y.Liang, Z.Lin, W.Longo, E.Loukas, N.Lu, R.-S.Lustermann, W.Lutton, L.Lyon, A.-M.Maeshima, K.Malcles, J.Mandrik, P.Manzoni, R.A.Marchese, L.Marinelli, N.Marini, A.C.Martin, L.Marzocchi, B.Mascellani, A.Massironi, A.Matveev, V.Mazza, G.Meridiani, P.Mijic, M.Mijuskovic, J.Milenovic, P.Milosevic, J.Monteno, M.Monti, F.Moortgat, F.Mousa, J.Mudholkar, T.Nessi-Tedaldi, F.Nicolaou, C.Nigamova, A.Obertino, M.M.Organtini, G.Orimoto, T.Orlandi, F.Ovtin, I.Paganis, E.Papagiannis, D.Pandolfi, F.Paramatti, R.Park, K.Pastrone, N.Paulini, M.Pauss, F.Petkovic, A.Petraityte, E.Pettinacci, V.Petyt, D.Pigazzini, S.Pinolini, B.S.Prova, P.R.Quaranta, C.Ragazzi, S.Rahatlou, S.Rasteiro Da Silva, J.C.Razis, P.A.Rebello Teles, P.Reis, T.Riti, F.Rogan, C.Romanteau, T.Rosowsky, A.Rovelli, C.Rovere, M.Rusack, R.Salvi, G.Sancar, O.Sanchez, A.Sandever, C.Santanastasio, F.Saradhy, R.Sarkar, U.Schneider, M.Schroeder, N.Sculac, A.Sculac, T.Shahzad, M.A.Shepherd-Themistocleous, C.H.Simkina, P.Singla, A.Singovsky, A.Skovpen, Y.Smith, V.J.Soffi, L.Stachon, K.Steen, A.Steggemann, J.Succar, M.Tao, J.Tishelman-Charny, A.Tiwari, P.C.Tornago, M.Tramontano, R.Tsai, L.-S.Usai, E.Valsecchi, D.Vagnerini, A.Varela, J.Venditti, R.Verma, P.V.Vlassov, E.Wachirapusitanand, V.Wamorkar, T.Wang, C.Wang, J.Wadud, M.A.Yu, S.S.Zabi, A.Zghiche, A.Zhang, L.Zhu, R.Y.Zygala, L.Autoencoder-based Anomaly Detection System for Online Data Quality Monitoring of the CMS Electromagnetic Calorimeterphysics.data-anOther Fields of Physicshep-exParticle Physics - Experimentcs.LGComputing and Computersphysics.ins-detDetectors and Experimental TechniquesThe CMS detector is a general-purpose apparatus that detects high-energy collisions produced at the LHC. Online Data Quality Monitoring of the CMS electromagnetic calorimeter is a vital operational tool that allows detector experts to quickly identify, localize, and diagnose a broad range of detector issues that could affect the quality of physics data. A real-time autoencoder-based anomaly detection system using semi-supervised machine learning is presented enabling the detection of anomalies in the CMS electromagnetic calorimeter data. A novel method is introduced which maximizes the anomaly detection performance by exploiting the time-dependent evolution of anomalies as well as spatial variations in the detector response. The autoencoder-based system is able to efficiently detect anomalies, while maintaining a very low false discovery rate. The performance of the system is validated with anomalies found in 2018 and 2022 LHC collision data. Additionally, the first results from deploying the autoencoder-based system in the CMS online Data Quality Monitoring workflow during the beginning of Run 3 of the LHC are presented, showing its ability to detect issues missed by the existing system.arXiv:2309.10157oai:cds.cern.ch:28718042023-09-18
spellingShingle physics.data-an
Other Fields of Physics
hep-ex
Particle Physics - Experiment
cs.LG
Computing and Computers
physics.ins-det
Detectors and Experimental Techniques
Abadjiev, D.
Adams, T.
Adzic, P.
Ahmad, M.
Amendola, C.
Andrews, M.B.
Arcidiacono, R.
Argiro, S.
Askew, A.
Auffray, E.
Azzolini, V.
Bailleux, D.
Band, R.
Barney, D.
Barria, P.
Bartosik, N.
Basile, C.
Bastos, D.
Bell, K.W.
Besancon, M.
Bianco, R.
Biino, C.
Blinov, V.
Borca, C.
Bornheim, A.
Brown, R.M.
Campana, M.
Castells, S.
Cavallari, F.
Cetorelli, F.
Chatterjee, R.M.
Chatterjee, S.
Chaudhary, G.
Chen, J.A.
Chernyavskaya, N.
Chung, H.
Cipriani, M.
Cokic, L.
Cooke, C.
Cossio, F.
Couderc, F.
Cristoforetti, D.
Cucciati, G.
Cunqueiro Mendez, L.
Da Silva Di Calafiori, D.
Dafinei, I.
Cockerill, D.J.A.
Dejardin, M.
Del Re, D.
Della Ricca, G.
Depasse, P.
Dervan, J.
Di Marco, E.
Diemoz, M.
Dimova, T.
Dissertori, G.
Dittmar, M.
Dolgopolov, A.
Donegà, M.
Dordevic, M.
El Mamouni, H.
Errico, F.
Espinosa, F.
Faure, J.L.
Fay, J.
Fernandez Menendez, J.
Ferri, F.
Finco, L.
Fiori, F.
Frahm, E.
Funk, W.
Gadek, T.
Gajownik, J.
Galli, M.
Ganjour, S.
Gascon, S.
Ghezzi, A.
Ghose, P.
Gninenko, S.
Goadhouse, S.
Godinovic, N.
Golubev, N.
Govoni, P.
Gras, P.
Hakala, J.
Hamel de Monchenault, G.
Härringer, N.
Hashmi, R.
Heath, H.F.
Hirosky, R.
Ho, K.W.
Hou, X.
Ingram, Q.
Jain, Sh.
Javaid, T.
Jessop, C.
Jimènez, R.
Joshi, B.M.
Jourd'hui, E.
Kaadze, K.
Kao, Y.-W.
Kardapoltsev, L.
Khurana, R.
King, J.
Kirilovas, A.
Konstantinov, D.
Kovac, M.
Krishna, A.
Kuo, C.M.
Lambrecht, L.
Lavizzari, G.
Lecoq, P.
Ledovskoy, A.
Legger, F.
Lelas, D.
Li, Y.Y.
Liang, Z.
Lin, W.
Longo, E.
Loukas, N.
Lu, R.-S.
Lustermann, W.
Lutton, L.
Lyon, A.-M.
Maeshima, K.
Malcles, J.
Mandrik, P.
Manzoni, R.A.
Marchese, L.
Marinelli, N.
Marini, A.C.
Martin, L.
Marzocchi, B.
Mascellani, A.
Massironi, A.
Matveev, V.
Mazza, G.
Meridiani, P.
Mijic, M.
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Milenovic, P.
Milosevic, J.
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Moortgat, F.
Mousa, J.
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Nessi-Tedaldi, F.
Nicolaou, C.
Nigamova, A.
Obertino, M.M.
Organtini, G.
Orimoto, T.
Orlandi, F.
Ovtin, I.
Paganis, E.
Papagiannis, D.
Pandolfi, F.
Paramatti, R.
Park, K.
Pastrone, N.
Paulini, M.
Pauss, F.
Petkovic, A.
Petraityte, E.
Pettinacci, V.
Petyt, D.
Pigazzini, S.
Pinolini, B.S.
Prova, P.R.
Quaranta, C.
Ragazzi, S.
Rahatlou, S.
Rasteiro Da Silva, J.C.
Razis, P.A.
Rebello Teles, P.
Reis, T.
Riti, F.
Rogan, C.
Romanteau, T.
Rosowsky, A.
Rovelli, C.
Rovere, M.
Rusack, R.
Salvi, G.
Sancar, O.
Sanchez, A.
Sandever, C.
Santanastasio, F.
Saradhy, R.
Sarkar, U.
Schneider, M.
Schroeder, N.
Sculac, A.
Sculac, T.
Shahzad, M.A.
Shepherd-Themistocleous, C.H.
Simkina, P.
Singla, A.
Singovsky, A.
Skovpen, Y.
Smith, V.J.
Soffi, L.
Stachon, K.
Steen, A.
Steggemann, J.
Succar, M.
Tao, J.
Tishelman-Charny, A.
Tiwari, P.C.
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Tramontano, R.
Tsai, L.-S.
Usai, E.
Valsecchi, D.
Vagnerini, A.
Varela, J.
Venditti, R.
Verma, P.V.
Vlassov, E.
Wachirapusitanand, V.
Wamorkar, T.
Wang, C.
Wang, J.
Wadud, M.A.
Yu, S.S.
Zabi, A.
Zghiche, A.
Zhang, L.
Zhu, R.Y.
Zygala, L.
Autoencoder-based Anomaly Detection System for Online Data Quality Monitoring of the CMS Electromagnetic Calorimeter
title Autoencoder-based Anomaly Detection System for Online Data Quality Monitoring of the CMS Electromagnetic Calorimeter
title_full Autoencoder-based Anomaly Detection System for Online Data Quality Monitoring of the CMS Electromagnetic Calorimeter
title_fullStr Autoencoder-based Anomaly Detection System for Online Data Quality Monitoring of the CMS Electromagnetic Calorimeter
title_full_unstemmed Autoencoder-based Anomaly Detection System for Online Data Quality Monitoring of the CMS Electromagnetic Calorimeter
title_short Autoencoder-based Anomaly Detection System for Online Data Quality Monitoring of the CMS Electromagnetic Calorimeter
title_sort autoencoder-based anomaly detection system for online data quality monitoring of the cms electromagnetic calorimeter
topic physics.data-an
Other Fields of Physics
hep-ex
Particle Physics - Experiment
cs.LG
Computing and Computers
physics.ins-det
Detectors and Experimental Techniques
url http://cds.cern.ch/record/2871804
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AT yuss autoencoderbasedanomalydetectionsystemforonlinedataqualitymonitoringofthecmselectromagneticcalorimeter
AT zabia autoencoderbasedanomalydetectionsystemforonlinedataqualitymonitoringofthecmselectromagneticcalorimeter
AT zghichea autoencoderbasedanomalydetectionsystemforonlinedataqualitymonitoringofthecmselectromagneticcalorimeter
AT zhangl autoencoderbasedanomalydetectionsystemforonlinedataqualitymonitoringofthecmselectromagneticcalorimeter
AT zhury autoencoderbasedanomalydetectionsystemforonlinedataqualitymonitoringofthecmselectromagneticcalorimeter
AT zygalal autoencoderbasedanomalydetectionsystemforonlinedataqualitymonitoringofthecmselectromagneticcalorimeter

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