2024-05-27
11:35 |
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2024-05-23
18:53 |
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2024-05-23
18:53 |
Run 3 commissioning results of heavy-flavor jet tagging at $\sqrt{s}=$13.6 TeV with CMS data using a modern framework for data processing
/CMS Collaboration
Identifying jets originating from the hadronization of bottom and charm hadrons (heavy-flavor jets) in the CMS experiment holds significant importance for various physics analyses, including investigations of the properties of the Higgs boson, top quarks, and the search for new physics beyond the standard model. This identification primarily relies on detector inputs from reconstructed charged particle tracks and information about secondary vertices contained within hadrons reconstructed as jets. [...]
CMS-DP-2024-024; CERN-CMS-DP-2024-024.-
Geneva : CERN, 2024 - 51 p.
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2024-05-23
18:53 |
Muon ID and Isolation Efficiencies with 2023 data
/CMS Collaboration
We present the performance of muon reconstruction plus identification, and isolation with 27.2 1/fb of data collected during the 2023 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-2024-023; CERN-CMS-DP-2024-023.-
Geneva : CERN, 2024 - 27 p.
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2024-05-21
16:59 |
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2024-05-21
15:08 |
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2024-05-16
16:40 |
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.
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2024-04-23
15:29 |
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.
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2024-04-16
18:04 |
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.
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2024-03-21
13:45 |
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.
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