CERN Accelerating science

CMS Detector Performance Summaries

Ostatnio dodane:
2022-11-21
14:05
DT Phase 2 Slice Test performance in early Run 3 collisions /CMS Collaboration
During Long Shutdown 2 one sector of the CMS DT detector has been instrumented with front-end and back-end prototypes of the DT Phase-2 upgrade electronics intended to operate during HL-LHC. The chamber FE signals are split and fed into both the legacy system and this new electronics. One of the backend boards runs the so-called Analytical Method firmware, which generates the trigger primitives. This way, both Phase-1 and Phase-2 electronics can be run inside the CMS infrastructure, and the trigger firmware can be validated under realistic conditions. The information extracted from Phase-2 primitives is compared with Phase-1 segments. Some performance results are presented in this note..
CMS-DP-2022-059; CERN-CMS-DP-2022-059.- Geneva : CERN, 2022 - 18 p. Fulltext: PDF;

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2022-11-21
14:04
ECAL DeepSC: Optimization of the DeepSC model inference strategy for reconstruction speedup /CMS Collaboration
Optimisation of the ECAL Deep Super-clustering model inference strategy for reconstruction speedup
CMS-DP-2022-058; CERN-CMS-DP-2022-058.- Geneva : CERN, 2022 - 14 p. Fulltext: PDF;

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2022-11-21
14:04
Reconstruction of unconverted photons in the presence of high pileup at the CMS Phase-2 High Granularity Endcap Calorimeter /CMS Collaboration
The CMS Collaboration foresees the installation of a novel endcap calorimeter, named High-Granularity CALorimeter (HGCAL), to sustain the harsher conditions of the High-Luminosity LHC (HL-LHC). The new calorimeter will predominantly use silicon sensors to achieve sufficient radiation tolerance and will exploit highly granular information to achieve high performance object reconstruction and mitigate the effects of the pile up. [...]
CMS-DP-2022-057; CERN-CMS-DP-2022-057.- Geneva : CERN, 2022 - 14 p. Fulltext: PDF;

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2022-11-21
14:04
Trigger-less readout and unbiased data quality monitoring of the CMS drift tubes muon detector /CMS Collaboration
The CMS experiment 40MHz data scouting project is aimed at intercepting the data produced at the level of the detectors' front-end without the filters induced by hardware-based triggers. A first implementation is realized by the trigger-less reading and processing of a fraction of the Drift Tube (DT) muon detector, equipped with a preliminary version of the Phase-2 Upgrade on-detector electronics boards. The data are transferred via high-speed optical links to back-end boards independently from the central experiment data acquisition (DAQ). This permits real-time monitoring of detector status, based on all signals produced at the front-end level, and provides an unbiased estimate of the CMS DT hit-rate under various data-taking conditions..
CMS-DP-2022-055; CERN-CMS-DP-2022-055.- Geneva : CERN, 2022 - 6 p. Fulltext: PDF;

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2022-11-21
14:04
Jet Energy Scale and Resolution Measurements Using Prompt Run3 Data Collected by CMS in the First Months of 2022 at 13.6 TeV /CMS Collaboration
We present jet energy scale and resolution measurements with promptly reconstructed Run3 data collected by CMS in the first months of 2022 at 13.6 TeV..
CMS-DP-2022-054; CERN-CMS-DP-2022-054.- Geneva : CERN, 2022 - 12 p. Fulltext: PDF;

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2022-11-17
15:30
Towards common ttbar Monte-Carlo settings for ATLAS and CMS
This note presents version 2 of the common settings for the Powheg+Pythia8 Monte-Carlo generator used by ATLAS and CMS, as well as version 1 of common Sherpa settings. Samples generated with the different settings are compared using a publicly available Rivet routine..
CMS-DP-2022-056; CERN-CMS-DP-2022-056.- Geneva : CERN, 2022 - 15 p. Fulltext: PDF;

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2022-11-08
18:48
Primary Vertex Reconstruction for Heterogeneous Architecture at CMS /CMS Collaboration
This note reports on vertex reconstruction at CMS in HL-LHC conditions. The CMS vertex reconstruction is a two-step procedure consisting of vertex finding and fitting. [...]
CMS-DP-2022-052; CERN-CMS-DP-2022-052.- Geneva : CERN, 2022 - 18 p. Fulltext: PDF;

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2022-11-08
18:45
Efficiency parametrization of b-tagging classifier using Graph Neural Networks /CMS Collaboration
In high-energy physics experiments, estimating the efficiency of a process using selection cuts is a widely used technique. However, this method is limited by the number of events that could be simulated in the required analysis phase space [...]
CMS-DP-2022-051; CERN-CMS-DP-2022-051.- Geneva : CERN, 2022 - 19 p. Fulltext: PDF;

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2022-11-08
18:45
Transformer models for heavy flavor jet identification /CMS Collaboration
During Run2 of the Large Hadron Collider (LHC), deep-learning-based algorithms were established and led to a significantly improved heavy flavor (b and c) jet tagging performance. In the scope of large-radius boosted jets like top-quark jets, Graph Neural Network (GNN) based models, e.g., ParticleNet, have reached state-of-the-art performance. [...]
CMS-DP-2022-050; CERN-CMS-DP-2022-050.- Geneva : CERN, 2022 - 11 p. Fulltext: PDF;

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2022-11-08
18:45
Adversarial training for b-tagging algorithms in CMS /CMS Collaboration
Modern neural networks bring considerable performance improvements in various areas of high-energy physics, such as object identification. Flavour-tagging is one example that profits from complex architectures, leveraging information from large numbers of low-level inputs. [...]
CMS-DP-2022-049; CERN-CMS-DP-2022-049.- Geneva : CERN, 2022 - 20 p. Fulltext: PDF;

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