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CERN Document Server 2,023 records found  1 - 10nextend  jump to record: Search took 0.55 seconds. 
1.
(100)
Autoencoder-based time series anomaly detection for ATLAS Liquid Argon calorimeter data quality monitoring
This note introduces a prototype autoencoder-based algorithm designed to identify detector anomalies in ATLAS liquid argon calorimeter data. [...]
ATL-DAPR-PUB-2024-002. - 2024 - mult..
Original Communication (restricted to ATLAS) - Full text
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2.
(21)
An AutoEncoder-based Anomaly Detection tool with a per-LS granularity /CMS Collaboration
An AutoEncoder-based Anomaly Detection Tool capable of detecting anomalies in DQM Monitor Elements with a per-Lumisection granularity is presented..
CMS-DP-2023-010; CERN-CMS-DP-2023-010.- Geneva : CERN, 2023 - 2 p. Fulltext: PDF;
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3.
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Autoencoder-based Online Data Quality Monitoring for the CMS Electromagnetic Calorimeter / Harilal, Abhirami (Carnegie Mellon U.) ; Park, Kyungmin (Carnegie Mellon U.) ; Andrews, Michael (Carnegie Mellon U.) ; Paulini, Manfred (Carnegie Mellon U.)
The online Data Quality Monitoring system (DQM) of the CMS electromagnetic calorimeter (ECAL) is a crucial operational tool that allows ECAL experts to quickly identify, localize, and diagnose a broad range of detector issues that would otherwise hinder physics-quality data taking. [...]
arXiv:2308.16659 ; CMS-CR-2023-020. - 2023. - 7 p.
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4.
(17)
Autoencoder-based Anomaly Detection System for Online Data Quality Monitoring of the CMS Electromagnetic Calorimeter / The CMS ECAL Collaboration
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. [...]
CMS-NOTE-2023-009; CERN-CMS-NOTE-2023-009.- Geneva : CERN, 2023 - 30 p. Fulltext: PDF;
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5.
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Anomaly Detection Based on Machine Learning for the CMS Electromagnetic Calorimeter Online Data Quality Monitoring / Harilal, Abhirami (Carnegie Mellon U.) ; Park, Kyungmin (Carnegie Mellon U.) ; Paulini, Manfred (Carnegie Mellon U.)
A real-time autoencoder-based anomaly detection system using semi-supervised machine learning has been developed for the online Data Quality Monitoring system of the electromagnetic calorimeter of the CMS detector at the CERN LHC. [...]
arXiv:2407.20278 ; CMS CR-2024/135. -
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6.
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Anomaly detection using an Autoencoder for the High-Granularity Calorimeter / Tran, Lam Giang
The High-Granularity Calorimeter (HGCAL) is the new upcoming endcap for the Compact Muon Solenoid (CMS) detector, replacing the existing endcap calorimeter for the High-Luminosity Large Hadron Collider (HL-LHC) era. [...]
CERN-STUDENTS-Note-2024-218. - 2024
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7.
(12)
Automating ATLAS control room anomaly detection with deep learning / Hanna, Avery Bryn
To ensure high-quality data acquisition at ATLAS, the detector status is monitored by a team of shifters in the control room where they watch plots of the incoming data and compare them with the expected standards. [...]
CERN--Note-2024-003. - 2024.
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8.
(12)
Automating ATLAS control room anomaly detection with deep learning / Hanna, Avery Bryn
To ensure high-quality data acquisition at ATLAS, the detector status is monitored by a team of shifters in the control room where they watch plots of the incoming data and compare them with the expected standards. [...]
CERN-STUDENTS-Note-2024-074. - 2024
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9.
(10)
Development of machine-learning based app for anomaly detection in CMSWEB / Hussain, Nasir
This project that we have developed revolves around the idea of an advanced anomaly detection system for the CMSWEB services, which is a vital part of CERN’s infrastructure that houses more than two dozen web services like DBS, DAS, CRAB, and WMCore. [...]
CERN-STUDENTS-Note-2024-221. - 2024
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10.
(8)
Time Series Anomaly Detection for CERN Large-Scale Computing Infrastructure / Paltenghi, Matteo
Anomaly Detection in the CERN Data Center is a challenging task due to the large scale of the computing infrastructure and the large volume of data to monitor [...]
CERN-THESIS-2020-282 - Milano : Politecnico di Milano, 2020-10-02. - 119 p.

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