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CMS Detector Performance Summaries

新增:
2024-07-17
12:31
Jet energy scale and resolution of jets with ParticleNet $p_{\mathrm{T}}$ regression using Run3 data collected by the CMS experiment in 2022 and 2023 at 13.6 TeV /CMS Collaboration
Jet energy scale (JES) calibration is presented, based on the first reprocessing of data collected in pp collisions at $\sqrt{s}=13.6\mathrm{TeV}$ for data taking periods Era C (5.0$\,\text{fb}^{-1}$) and Era D (3.0$\,\text{fb}^{-1}$) of 2022, and prompt-reconstructed data from Era C (17.8$\,\text{fb}^{-1}$) of 2023. The results are shown for jets clustered from particle flow (PF) candidates using the anti-$k_{\mathrm{T}}$ algorithm with $R = 0.4$, and applying the Pileup Per Particle Identification (PUPPI) algorithm for pileup (PU) mitigation. Additionally, a new machine learning algorithm based on ParticleNet (PNet) is used for energy regression..
CMS-DP-2024-064; CERN-CMS-DP-2024-064.- Geneva : CERN, 2024 - 12 p. Fulltext: PDF;

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2024-07-17
12:31
Muon ID and Isolation efficiencies with muons in proton-proton collisions at sqrt(s) = 13.6 TeV /CMS Collaboration
We present the performance of muon reconstruction, identification, and isolation with the proton-proton collision data collected during the 2022, 2023, and 2024 LHC proton-proton run at 13.6 TeV. Dataset is splitted in five periods, corresponding to the different data taking conditions of the CMS detector. [...]
CMS-DP-2024-067; CERN-CMS-DP-2024-067.- Geneva : CERN, 2024 - 15 p. Fulltext: PDF;

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2024-07-17
12:31
A unified approach for jet tagging in Run 3 at $\sqrt{s}$=13.6 TeV in CMS /CMS Collaboration
The steady progress in machine learning leads to substantial performance improvements in various areas of high-energy physics, especially for object identification. Jet flavor identification (tagging) is a prominent benchmark that profits from elaborate architectures, leveraging information from low-level input variables and their correlations. Throughout the data-taking eras of the Large Hadron Collider (LHC) (Run 1 - Run 3), various deep-learning-based algorithms were established and led to a significantly improved tagging performance of heavy flavor jets, originating from the hadronization of b and c quarks. [...]
CMS-DP-2024-066; CERN-CMS-DP-2024-066.- Geneva : CERN, 2024 - 49 p. Fulltext: PDF;

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2024-07-17
12:31
Muon momentum calibration with proton-proton collisions at sqrt(s) = 13.6 TeV /CMS Collaboration
We present the results about the standard muon momentum scale and resolution measurements done on the proton-proton collision data collected during the 2022, and 2023 LHC proton-proton run at 13.6 TeV. Two different techniques have been applied to respectively study medium-p$_{T}$ and high-p$_{T}$ muons, as briefly described. [...]
CMS-DP-2024-065; CERN-CMS-DP-2024-065.- Geneva : CERN, 2024 - 26 p. Fulltext: PDF;

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2024-07-17
12:31
Jet energy scale and resolution of jets with ParticleNet $p_{\mathrm{T}}$ regression using Run3 data collected by the CMS experiment in 2022 and 2023 at 13.6 TeV /CMS Collaboration
Jet energy scale (JES) calibration is presented, based on the first reprocessing of data collected in pp collisions at $\sqrt{s}=13.6\mathrm{TeV}$ for data taking periods Era C (5.0$\,\text{fb}^{-1}$) and Era D (3.0$\,\text{fb}^{-1}$) of 2022, and prompt-reconstructed data from Era C (17.8$\,\text{fb}^{-1}$) of 2023. The results are shown for jets clustered from particle flow (PF) candidates using the anti-$k_{\mathrm{T}}$ algorithm with $R = 0.4$, and applying the Pileup Per Particle Identification (PUPPI) algorithm for pileup (PU) mitigation. Additionally, a new machine learning algorithm based on ParticleNet (PNet) is used for energy regression..
CMS-DP-2024-064; CERN-CMS-DP-2024-064.- Geneva : CERN, 2024 - 12 p. Fulltext: PDF;

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2024-07-17
12:31
Performance of the CNN-based tau identification algorithm with Domain Adaptation using Adversarial Machine Learning for Run 2 /CMS Collaboration
In this note, we present the advancements in tau identification at the CMS experiment using a convolutional neural network (CNN)-based algorithm enhanced by domain adaptation techniques through adversarial machine learning. The novel model exhibits significant performance improvements over its predecessor, showcasing enhanced efficiency and purity in tau identification. [...]
CMS-DP-2024-063; CERN-CMS-DP-2024-063.- Geneva : CERN, 2024 - 19 p. Fulltext: PDF;

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2024-07-17
12:31
DT performance in 2024 and some comparisons with the past /CMS Collaboration
The performance of the CMS Drift Tubes in 2024 is presented through its main observables: the fraction of active channels and the efficiency of hit detection and local reconstruction are found to match 2022 and 2023. The efficiency for local trigger primitive generation is compared to the TwinMux efficiency, which also exploits the RPC information. [...]
CMS-DP-2024-062; CERN-CMS-DP-2024-062.- Geneva : CERN, 2024 - 21 p. Fulltext: PDF;

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2024-07-17
12:31
Studies of ECAL Timing Reconstruction /CMS Collaboration
Studies of ECAL Timing Reconstruction..
CMS-DP-2024-061; CERN-CMS-DP-2024-061.- Geneva : CERN, 2024 - 4 p. Fulltext: PDF;

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2024-07-17
12:31
Fast Beam Conditions Monitor Linearity /CMS Collaboration
A study of the linearity of the Fast Beam Conditions Monitor (FBCM) was performed using simulations..
CMS-DP-2024-060; CERN-CMS-DP-2024-060.- Geneva : CERN, 2024 - 5 p. Fulltext: PDF;

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2024-07-17
12:31
2024 Data Collected with AXOL1TL Anomaly Detection at the CMS Level-1 Trigger /CMS Collaboration
AXOL1TL (Anomaly eXtraction Online Level-1 Trigger aLgorithm) is an anomaly detection algorithm operating in the global trigger subsystem of the CMS Level-1 Trigger. The CMS experiment has been collecting data with this algorithm since May 2024. [...]
CMS-DP-2024-059; CERN-CMS-DP-2024-059.- Geneva : CERN, 2024 - 13 p. Fulltext: PDF;

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