2024-07-30
12:00 |
CMS tracker alignment performance results for Run 3 reprocessing
/CMS Collaboration
The tracking system of the CMS experiment is the world's largest silicon tracker with its 1856 and 15148 silicon pixel and strip modules, respectively. To accurately reconstruct trajectories of charged particles the position, rotation, and curvature of each module must be corrected such that the alignment resolution is smaller than, or comparable to, the hit resolution. [...]
CMS-DP-2024-071; CERN-CMS-DP-2024-071.-
Geneva : CERN, 2024 - 47 p.
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2024-07-30
12:00 |
CMS tracker data quality certification with new machine learning tools
/CMS Collaboration
The CMS tracking system in Run 3 is made up of thousands of silicon modules. Because of the aging of the detector, and all other possible incidents that may happen during operations, there is the need for constant monitoring of the detector components, in order to guarantee the best data quality. [...]
CMS-DP-2024-070; CERN-CMS-DP-2024-070.-
Geneva : CERN, 2024 - 32 p.
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2024-07-30
11:52 |
Run 3 detector paper L1T Plots
/CMS Collaboration
LHC Run 3 offers an opportunity to achieve significant progress in our understanding of the fundamental
nature of matter through the detection of new physics beyond the standard model, in particular by
measuring unconventional physics signatures that require the implementation of non-standard triggering
techniques. For Run 3, although no major hardware upgrade has been performed, these capabilities are
available already through the design of novel algorithm approaches, some based on machine learning (ML)
techniques. [...]
CMS-DP-2022-065; CERN-CMS-DP-2022-065.-
Geneva : CERN, 2022 - 15 p.
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2024-07-18
10:52 |
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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.
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2024-07-17
12:31 |
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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.
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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.
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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 |
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