An official website of the United States government
Abstract The operation and performance of the Compact Muon Solenoid (CMS) electromagnetic calorimeter (ECAL) are presented, based on data collected in pp collisions at √s=13 TeV at the CERN LHC, in the years from 2015 to 2018 (LHC Run 2), corresponding to an integrated luminosity of 151 fb-1. The CMS ECAL is a scintillating lead-tungstate crystal calorimeter, with a silicon strip preshower detector in the forward region that provides precise measurements of the energy and the time-of-arrival of electrons and photons. The successful operation of the ECAL is crucial for a broad range of physics goals, ranging from observing the Higgs boson and measuring its properties, to other standard model measurements and searches for new phenomena. Precise calibration, alignment, and monitoring of the ECAL response are important ingredients to achieve these goals. To face the challenges posed by the higher luminosity, which characterized the operation of the LHC in Run 2, the procedures established during the 2011–2012 run of the LHC have been revisited and new methods have been developed for the energy measurement and for the ECAL calibration. The energy resolution of the calorimeter, for electrons from Z boson decays reaching the ECAL without significant loss of energy by bremsstrahlung, was better than 1.8%, 3.0%, and 4.5% in the |η| intervals [0.0,0.8], [0.8,1.5], [1.5, 2.5], respectively. This resulting performance is similar to that achieved during Run 1 in 2011–2012, in spite of the more severe running conditions.
more »
« less
Autoencoder-Based Anomaly Detection System for Online Data Quality Monitoring of the CMS Electromagnetic Calorimeter
Abstract 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.
more »
« less
- Award ID(s):
- 2209764
- PAR ID:
- 10518285
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Publisher / Repository:
- Springer Nature
- Date Published:
- Journal Name:
- Computing and Software for Big Science
- Volume:
- 8
- Issue:
- 1
- ISSN:
- 2510-2036
- Subject(s) / Keyword(s):
- Anomaly detection Machine learning Autoencoders Data quality monitoring Calorimeter
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
FASER, or the Forward Search Experiment, is a new experiment at CERN designed to complement the LHC’s ongoing physics program, extending its discovery potential to light and weakly interacting particles that may be produced copiously at the LHC in the far-forward region. New particles targeted by FASER, such as long-lived dark photons or axion-like particles, are characterised by a signature with two oppositely charged tracks or two photons in the multi-TeV range that emanate from a common vertex inside the detector. The full detector was successfully installed in March 2021 in an LHC side tunnel 480 m downstream from the interaction point in the ATLAS detector. FASER is planned to be operational for LHC Run 3. The experiment is composed of a silicon-strip tracking-based spectrometer using three dipole magnets with a 20 cm aperture, supplemented by four scintillator stations and an electromagnetic calorimeter. The FASER electromagnetic calorimeter is constructed from four spare LHCb calorimeter modules. The modules are of the Shashlik type with interleaved scintillator and lead plates that result in 25 radiation lengths and 1% energy resolution for TeV electromagnetic showers. In 2021, a test beam campaign was carried out using one of the CERN SPS beam lines to set up the calibration of the FASER calorimeter system in preparation for physics data taking. The relative calorimeter response to electrons with energies between 10 and 300 GeV, as well as high energy muons and pions, has been measured under various high voltage settings and beam positions. The measured calorimeter resolution, energy calibration, and particle identification capabilities are presented.more » « less
-
While the blockchain technology provides strong cryptographic protection on the ledger and the system operations, the underlying blockchain networking remains vulnerable due to potential threats such as denial of service (DoS), Eclipse, spoofing, and Sybil attacks. Effectively detecting such malicious events should thus be an essential task for securing blockchain networks and services. Due to its importance, several studies investigated anomaly detection in Bitcoin and blockchain networks, but their analyses mainly focused on the blockchain ledger in the application context (e.g., transactions) and targets specific types of attacks (e.g., double-spending, deanonymization, etc). In this study, we present a security mechanism based on the analysis of blockchain network traffic statistics (rather than ledger data) to detect malicious events, through the functions of data collection and anomaly detection. The data collection engine senses the underlying blockchain traffic and generates multi-dimensional data streams in a periodic manner. The anomaly detection engine then detects anomalies from the created data instances based on semi-supervised learning, which is capable of detecting previously unseen patterns, and we introduce our profiling-based detection engine implemented on top of AutoEncoder (AE). Our experimental results support the effectiveness of the presented security mechanism for accurate, online detection of malicious events from blockchain networking traffic data. We also show further reduction in time complexity (up to 66.8% for training and 85.7% for testing), without any performance degradation using feature prioritization compared to the utilization of the entire features.more » « less
-
Abstract Since the initial data taking of the CERN LHC, the CMS experiment has undergone substantial upgrades and improvements. This paper discusses the CMS detector as it is configured for the third data-taking period of the CERN LHC, Run 3, which started in 2022. The entire silicon pixel tracking detector was replaced. A new powering system for the superconducting solenoid was installed. The electronics of the hadron calorimeter was upgraded. All the muon electronic systems were upgraded, and new muon detector stations were added, including a gas electron multiplier detector. The precision proton spectrometer was upgraded. The dedicated luminosity detectors and the beam loss monitor were refurbished. Substantial improvements to the trigger, data acquisition, software, and computing systems were also implemented, including a new hybrid CPU/GPU farm for the high-level trigger.more » « less
-
Abstract The ATLAS tile calorimeter (TileCal) is the hadronic sampling calorimeter covering the central region of the ATLAS detector at the Large Hadron Collider (LHC). This paper gives an overview of the calorimeter’s operation and performance during the years 2015–2018 (Run 2). In this period, ATLAS collected proton–proton collision data at a centre-of-mass energy of 13 TeV and the TileCal was 99.65% efficient for data-taking. The signal reconstruction, the calibration procedures, and the detector operational status are presented. The performance of two ATLAS trigger systems making use of TileCal information, the minimum-bias trigger scintillators and the tile muon trigger, is discussed. Studies of radiation effects allow the degradation of the output signals at the end of the LHC and HL-LHC operations to be estimated. Finally, the TileCal response to isolated muons, hadrons and jets from proton–proton collisions is presented. The energy and time calibration methods performed excellently, resulting in good stability and uniformity of the calorimeter response during Run 2. The setting of the energy scale was performed with an uncertainty of 2%. The results demonstrate that the performance is in accordance with specifications defined in the Technical Design Report.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s41781-024-00118-z
