التفاصيل البيبلوغرافية
| العنوان: |
Autoencoder-Based Anomaly Detection System for Online Data Quality Monitoring of the CMS Electromagnetic Calorimeter |
| المؤلفون: |
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., Re, D. Del, Ricca, G. Della, Depasse, P., Dervan, J., Marco, E. Di, Diemoz, M., Dimova, T., Dissertori, G., Dittmar, M., Dolgopolov, A., Donegà, M., Dordevic, M., Mamouni, H. El, Errico, F., Espinosa, F., Faure, J. L., Fay, J., Menendez, J. Fernandez, 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., de Monchenault, G. Hamel, Harilal, A., 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., Teles, P. Rebello, 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., 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., Zygal, L. |
| المصدر: |
Springer International Publishing |
| بيانات النشر: |
Springer Science and Business Media LLC |
| سنة النشر: |
2024 |
| المجموعة: |
DSpace@MIT (Massachusetts Institute of Technology) |
| الوصف: |
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. In addition, 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. |
| نوع الوثيقة: |
article in journal/newspaper |
| وصف الملف: |
application/pdf |
| اللغة: |
English |
| Relation: |
Computing and Software for Big Science; https://hdl.handle.net/1721.1/155671; PUBLISHER_CC |
| الاتاحة: |
https://hdl.handle.net/1721.1/155671 |
| Rights: |
Creative Commons Attribution ; https://creativecommons.org/licenses/by/4.0/ ; The Author(s) |
| رقم الانضمام: |
edsbas.B11F382E |
| قاعدة البيانات: |
BASE |