CERN Accelerating science

CMS Detector Performance Summaries

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Performance of Muon identification and isolation in 2022 data and simulation at 13.6 TeV /CMS Collaboration
We present the performance of muon reconstruction plus identification, and isolation with 33.2 1/fb of data collected during the 2022 LHC proton-proton run at 13.6 TeV. Dataset is splitted in two periods, corresponding to two different data taking conditions of the CMS detector. [...]
CMS-DP-2024-019; CERN-CMS-DP-2024-019.- Geneva : CERN, 2024 - 29 p. Fulltext: PDF;

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ECAL Trigger Performance in Run 3 /CMS Collaboration
ECAL Trigger Performance in Run 3
CMS-DP-2024-021; CERN-CMS-DP-2024-021.- Geneva : CERN, 2024 - 21 p. Fulltext: PDF;

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ECAL calibration performance in Run 3 with reprocessed data /CMS Collaboration
The operation and performance of the Compact Muon Solenoid (CMS) electromagnetic calorimeter (ECAL) are presented based on data collected in pp collisions at 13.6TeV center-of-mass energy at the CERN LHC, in the years from 2022 to 2023 in LHC Run3. Precise calibration, alignment, and monitoring of the ECAL response are important ingredients to achieve and maintain the excellent performance obtained in Run3 in terms of energy scale and resolution. This note presents the refined calibration and excellent performance of the CMS ECAL that were achieved for the 2022 and 2023 data..
CMS-DP-2024-022; CERN-CMS-DP-2024-022.- Geneva : CERN, 2024 - 24 p. Fulltext: PDF;

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b-hive: a modular training framework for state-of-the-art object-tagging within the Python ecosystem at the CMS experiment /CMS Collaboration
In high-energy physics (HEP), neural-network (NN) based algorithms have found many applications, such as quark-flavor identification of jets in experiments like the Compact Muon Solenoid (CMS) at the Large Hadron Collider (LHC) at CERN. Unfortunately, complete training pipelines often encounter application-specific obstacles like the processing of many, large files of HEP data format such as ROOT, the data provisioning to the model, and a correct evaluation of performance. We have developed a framework called "b-hive" that combines state-of-the-art tools for HEP data processing and training in a Python-based ecosystem. [...]
CMS-DP-2024-020; CERN-CMS-DP-2024-020.- Geneva : CERN, 2024 - 18 p. Fulltext: PDF;

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NNPuppiTaus: PUPPI tau reconstruction in the Level-1 trigger with real-time machine learning /CMS Collaboration
The future LHC High-Luminosity upgrade amplifies the proton collision rate by a factor of about 5-7, posing challenges for physics object reconstruction and identification including the tau leptons. Detecting taus at the CMS Level-1 (L1) trigger enables many important physics analyses in the experiment. [...]
CMS-DP-2024-018; CERN-CMS-DP-2024-018.- Geneva : CERN, 2024 - 14 p. Fulltext: PDF;

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Efficiency of multijet triggers using b-tagging and electron+HT/jet triggers in 2022 and 2023 /CMS Collaboration
This note presents Level-1 Trigger + High-Level Trigger efficiencies for some of the dedicated trigger algorithms used for top-physics analyses in CMS. The triggers considered here include (a) hadronic triggers selecting events based on the scalar sum of jet transverse momenta, jet multiplicity and jet b-tagging discriminants (HT+multijet+Btag triggers), and (b) triggers selecting events with an electron produced in association with hadronic jets (electron+HT/jet triggers). [...]
CMS-DP-2024-016; CERN-CMS-DP-2024-016.- Geneva : CERN, 2024 - 14 p. Fulltext: PDF;

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Data compression via partial online processing in CMS: experience in heavy ions and prospects /CMS Collaboration
As the LHC starts to deliver higher luminosities of proton-proton and heavy-ion collisions, the size of raw event data output begins to be a limiting factor in the number of events that can be recorded. Due to the large number of channels, the original silicon strip tracker (SST) data format, which stores the per-silicon-strip ADC counts, is the largest component of the CMS raw-data size. [...]
CMS-DP-2024-007; CERN-CMS-DP-2024-007.- Geneva : CERN, 2023 - 11 p. Fulltext: PDF;

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Luminosity calibration of 2017 XeXe collisions at 5.44 TeV /CMS Collaboration
In this note, we present the luminosity calibration for the CMS experiment during the Xenon-Xenon collisions that occurred in the year 2017 at a nucleon-nucleon center-of-mass energy of 5.44 TeV and also the cross-luminometer consistency. This calibration is based on the analysis of data recorded by three sub-detectors: the Pixel Luminosity Telescope (PLT), the Fast Beam Conditions Monitor (BCM1F), and the forward hadron calorimeter (HFOC). The calibration constant (visible cross-section) is determined through the utilization of emittance scan (a vdM-like scan) data, where the sub-detectors' rates are measured as a function of beam separation [...]
CMS-DP-2024-015; CERN-CMS-DP-2024-015.- Geneva : CERN, 2024 - 7 p. Fulltext: PDF;

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Performance of the Line Segment Tracking Algorithm in the CMS Phase-2 High Level Trigger Tracking /CMS Collaboration
This note presents the status of the Line Segment Tracking algorithm, incl. its physics performance and computational speed, in the context of the track reconstruction being developed for the CMS Phase-2 High-Level Trigger..
CMS-DP-2024-014; CERN-CMS-DP-2024-014.- Geneva : CERN, 2024 - 25 p. Fulltext: PDF;

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Performance of Track Reconstruction at the CMS High-Level Trigger in 2023 data /CMS Collaboration
This note describes the physics performance of track reconstruction in the CMS High-Level Trigger in data collected in 2023, including comparisons to the performance measured in 2022..
CMS-DP-2024-013; CERN-CMS-DP-2024-013.- Geneva : CERN, 2024 - 20 p. Fulltext: PDF;

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